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import os
# Precomputes a list of the 100 first triangular numbers
snake_case = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.dirname(os.path.realpath(lowercase ) )
SCREAMING_SNAKE_CASE : str = os.path.join(lowercase , "words.txt" )
SCREAMING_SNAKE_CASE : str = ""
with open(lowercase ) as f:
SCREAMING_SNAKE_CASE : Dict = f.readline()
SCREAMING_SNAKE_CASE : Optional[int] = [word.strip("\"" ) for word in words.strip("\r\n" ).split("," )]
SCREAMING_SNAKE_CASE : Dict = [
word
for word in [sum(ord(lowercase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(lowercase )
if __name__ == "__main__":
print(solution())
| 319
|
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Any = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : Optional[int] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Any = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(lowercase , cache_dir=lowercase , split=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = parquet_path
elif issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [parquet_path]
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Tuple = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=("train",) ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
for split in splits:
SCREAMING_SNAKE_CASE : Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Dict = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(
{"train": parquet_path} , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[int] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Dict = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : str = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : Optional[Any] = ParquetDatasetReader({"train": parquet_path} , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if split:
SCREAMING_SNAKE_CASE : Any = {split: parquet_path}
else:
SCREAMING_SNAKE_CASE : Tuple = "train"
SCREAMING_SNAKE_CASE : int = {"train": parquet_path, "test": parquet_path}
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : str = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : int = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : Tuple = pq.ParquetFile(tmp_path / "foo.parquet" )
SCREAMING_SNAKE_CASE : List[Any] = pf.read()
assert dataset.data.table == output_table
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = str(shared_datadir / "test_image_rgb.jpg" )
SCREAMING_SNAKE_CASE : Union[str, Any] = {"image": [image_path]}
SCREAMING_SNAKE_CASE : Union[str, Any] = Features({"image": Image()} )
SCREAMING_SNAKE_CASE : int = Dataset.from_dict(lowercase , features=lowercase )
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : str = Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
assert dataset.features == reloaded_dataset.features
SCREAMING_SNAKE_CASE : Any = ParquetDatasetReader(str(tmp_path / "foo.parquet" ) , streaming=lowercase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32" )} ), None),
(Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert get_writer_batch_size(lowercase ) == expected
| 319
| 1
|
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__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ArgumentParser("Transformers CLI tool" , usage="transformers-cli <command> [<args>]" )
SCREAMING_SNAKE_CASE : Optional[Any] = parser.add_subparsers(help="transformers-cli command helpers" )
# Register commands
ConvertCommand.register_subcommand(lowercase )
DownloadCommand.register_subcommand(lowercase )
EnvironmentCommand.register_subcommand(lowercase )
RunCommand.register_subcommand(lowercase )
ServeCommand.register_subcommand(lowercase )
UserCommands.register_subcommand(lowercase )
AddNewModelCommand.register_subcommand(lowercase )
AddNewModelLikeCommand.register_subcommand(lowercase )
LfsCommands.register_subcommand(lowercase )
PTtoTFCommand.register_subcommand(lowercase )
# Let's go
SCREAMING_SNAKE_CASE : Any = parser.parse_args()
if not hasattr(lowercase , "func" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE : int = args.func(lowercase )
service.run()
if __name__ == "__main__":
main()
| 319
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
import math
from datetime import datetime, timedelta
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = year % 19
SCREAMING_SNAKE_CASE : List[str] = year % 4
SCREAMING_SNAKE_CASE : str = year % 7
SCREAMING_SNAKE_CASE : List[Any] = math.floor(year / 100 )
SCREAMING_SNAKE_CASE : int = math.floor((13 + 8 * leap_day_inhibits) / 25 )
SCREAMING_SNAKE_CASE : List[str] = leap_day_inhibits / 4
SCREAMING_SNAKE_CASE : Dict = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
SCREAMING_SNAKE_CASE : Dict = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
SCREAMING_SNAKE_CASE : Union[str, Any] = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
SCREAMING_SNAKE_CASE : List[str] = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(lowercase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(lowercase , 4 , 18 )
else:
return datetime(lowercase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1_994, 2_000, 2_010, 2_021, 2_023):
snake_case = """will be""" if year > datetime.now().year else """was"""
print(F"""Easter in {year} {tense} {gauss_easter(year)}""")
| 319
|
def lowerCamelCase__ ( lowercase , lowercase = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = length or len(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = list_data[i + 1], list_data[i]
SCREAMING_SNAKE_CASE : str = True
return list_data if not swapped else bubble_sort(lowercase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
| 1
|
import os
import time
import numpy as np
import onnxruntime as ort
snake_case = """1"""
snake_case = """0"""
snake_case = """1"""
snake_case = ort.SessionOptions()
snake_case = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
snake_case = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
snake_case = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
snake_case = ort.RunOptions()
snake_case = 128
snake_case = 1
snake_case = np.ones((batch, sequence), dtype=np.intaa)
snake_case = np.ones((batch, sequence), dtype=np.intaa)
snake_case = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
snake_case = time.time()
snake_case = 2_000
snake_case = {}
for iter in range(max_iters):
snake_case = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1_000 / max_iters))
| 319
|
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case = get_logger(__name__)
snake_case = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : str , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[int] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ):
for processor in self:
SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(processor.__call__ ).parameters
if len(UpperCAmelCase_ ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
f'''Make sure that all the required parameters: {list(function_args.keys() )} for '''
f'''{processor.__class__} are passed to the logits processor.''' )
SCREAMING_SNAKE_CASE : int = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , **UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : Dict = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : float ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not (temperature > 0):
raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}''' )
SCREAMING_SNAKE_CASE : Optional[int] = temperature
def __call__( self : List[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = scores / self.temperature
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : float , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (top_p < 0 or top_p > 1.0):
raise ValueError(f'''`top_p` has to be a float > 0 and < 1, but is {top_p}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (min_tokens_to_keep < 1):
raise ValueError(f'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}''' )
SCREAMING_SNAKE_CASE : Optional[int] = top_p
SCREAMING_SNAKE_CASE : str = filter_value
SCREAMING_SNAKE_CASE : List[str] = min_tokens_to_keep
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = lax.top_k(UpperCAmelCase_ , scores.shape[-1] )
SCREAMING_SNAKE_CASE : str = jnp.full_like(UpperCAmelCase_ , self.filter_value )
SCREAMING_SNAKE_CASE : Optional[int] = jax.nn.softmax(UpperCAmelCase_ , axis=-1 ).cumsum(axis=-1 )
SCREAMING_SNAKE_CASE : Tuple = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
SCREAMING_SNAKE_CASE : Optional[int] = jnp.roll(UpperCAmelCase_ , 1 )
score_mask |= score_mask.at[:, 0].set(UpperCAmelCase_ )
# min tokens to keep
SCREAMING_SNAKE_CASE : Union[str, Any] = score_mask.at[:, : self.min_tokens_to_keep].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = jnp.where(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jax.lax.sort_key_val(UpperCAmelCase_ , UpperCAmelCase_ )[-1]
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or top_k <= 0:
raise ValueError(f'''`top_k` has to be a strictly positive integer, but is {top_k}''' )
SCREAMING_SNAKE_CASE : List[str] = max(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = filter_value
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = scores.shape
SCREAMING_SNAKE_CASE : List[str] = jnp.full(batch_size * vocab_size , self.filter_value )
SCREAMING_SNAKE_CASE : List[str] = min(self.top_k , scores.shape[-1] ) # Safety check
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = lax.top_k(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = jnp.broadcast_to((jnp.arange(UpperCAmelCase_ ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
SCREAMING_SNAKE_CASE : List[str] = topk_scores.flatten()
SCREAMING_SNAKE_CASE : List[Any] = topk_indices.flatten() + shift
SCREAMING_SNAKE_CASE : Dict = next_scores_flat.at[topk_indices_flat].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = next_scores_flat.reshape(UpperCAmelCase_ , UpperCAmelCase_ )
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = bos_token_id
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.bool_(cur_len - 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.bos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = max_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : List[str] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : str = 1 - jnp.bool_(cur_len - self.max_length + 1 )
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.eos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or min_length < 0:
raise ValueError(f'''`min_length` has to be a positive integer, but is {min_length}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or eos_token_id < 0:
raise ValueError(f'''`eos_token_id` has to be a positive integer, but is {eos_token_id}''' )
SCREAMING_SNAKE_CASE : List[str] = min_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
# create boolean flag to decide if min length penalty should be applied
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
SCREAMING_SNAKE_CASE : Optional[int] = jnp.where(UpperCAmelCase_ , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = list(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = begin_index
def __call__( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Union[str, Any] = 1 - jnp.bool_(cur_len - self.begin_index )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(UpperCAmelCase_ , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : List[Any] = list(UpperCAmelCase_ )
def __call__( self : Any , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Tuple = scores.at[..., self.suppress_tokens].set(-float("inf" ) )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : List[Any] = dict(UpperCAmelCase_ )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
SCREAMING_SNAKE_CASE : Any = force_token_array.at[index].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = jnp.intaa(UpperCAmelCase_ )
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
def _force_token(UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : List[str] = scores.shape[0]
SCREAMING_SNAKE_CASE : Optional[int] = self.force_token_array[generation_idx]
SCREAMING_SNAKE_CASE : Tuple = jnp.ones_like(UpperCAmelCase_ , dtype=scores.dtype ) * -float("inf" )
SCREAMING_SNAKE_CASE : Dict = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
SCREAMING_SNAKE_CASE : Optional[Any] = lax.dynamic_update_slice(UpperCAmelCase_ , UpperCAmelCase_ , (0, current_token) )
return new_scores
SCREAMING_SNAKE_CASE : Any = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(UpperCAmelCase_ ) , lambda: scores , ) , )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Union[str, Any] = generate_config.eos_token_id
SCREAMING_SNAKE_CASE : Tuple = generate_config.no_timestamps_token_id
SCREAMING_SNAKE_CASE : List[Any] = generate_config.no_timestamps_token_id + 1
SCREAMING_SNAKE_CASE : Dict = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(UpperCAmelCase_ , "max_initial_timestamp_index" ):
SCREAMING_SNAKE_CASE : List[Any] = generate_config.max_initial_timestamp_index
else:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
if self.max_initial_timestamp_index is None:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
def __call__( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
# suppress <|notimestamps|> which is handled by without_timestamps
SCREAMING_SNAKE_CASE : int = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) )
def handle_pairs(UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) >= 1 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) < 2 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , UpperCAmelCase_ , UpperCAmelCase_ , )
return jnp.where(
UpperCAmelCase_ , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Optional[Any] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(cur_len == self.begin_index , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = self.timestamp_begin + self.max_initial_timestamp_index
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(
UpperCAmelCase_ , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , UpperCAmelCase_ , )
# if sum of probability over timestamps is above any other token, sample timestamp
SCREAMING_SNAKE_CASE : List[Any] = jax.nn.log_softmax(UpperCAmelCase_ , axis=-1 )
def handle_cumulative_probs(UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
return scores
| 319
| 1
|
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
SCREAMING_SNAKE_CASE : Optional[int] = cst_fwd.get(lowercase , np.inf )
SCREAMING_SNAKE_CASE : Optional[int] = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
SCREAMING_SNAKE_CASE : str = new_cost_f
SCREAMING_SNAKE_CASE : int = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
SCREAMING_SNAKE_CASE : List[Any] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = -1
SCREAMING_SNAKE_CASE : Any = set()
SCREAMING_SNAKE_CASE : Tuple = set()
SCREAMING_SNAKE_CASE : List[Any] = {source: 0}
SCREAMING_SNAKE_CASE : List[Any] = {destination: 0}
SCREAMING_SNAKE_CASE : Dict = {source: None}
SCREAMING_SNAKE_CASE : Any = {destination: None}
SCREAMING_SNAKE_CASE : PriorityQueue[Any] = PriorityQueue()
SCREAMING_SNAKE_CASE : PriorityQueue[Any] = PriorityQueue()
SCREAMING_SNAKE_CASE : Dict = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = queue_forward.get()
visited_forward.add(lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = queue_backward.get()
visited_backward.add(lowercase )
SCREAMING_SNAKE_CASE : List[str] = pass_and_relaxation(
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , )
SCREAMING_SNAKE_CASE : Tuple = pass_and_relaxation(
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
SCREAMING_SNAKE_CASE : Union[str, Any] = shortest_distance
return shortest_path_distance
snake_case = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
snake_case = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
snake_case = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
print("""Torch version:""", None)
try:
import transformers
print("""transformers version:""", transformers.__version__)
except ImportError:
print("""transformers version:""", None)
| 319
| 1
|
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ['''keras_nlp''']
def __init__( self : Optional[Any] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Any ):
requires_backends(self , ["keras_nlp"] )
| 319
|
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 319
| 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
snake_case = logging.get_logger(__name__)
snake_case = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
snake_case = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
snake_case = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1_350, 1_853, 1_982, 2_460, 2_627,
3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = '''whisper'''
UpperCamelCase_ : Any = ['''past_key_values''']
UpperCamelCase_ : Dict = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : str , UpperCAmelCase_ : List[str]=5_1865 , UpperCAmelCase_ : Union[str, Any]=80 , UpperCAmelCase_ : Tuple=6 , UpperCAmelCase_ : Union[str, Any]=4 , UpperCAmelCase_ : Union[str, Any]=6 , UpperCAmelCase_ : Optional[int]=4 , UpperCAmelCase_ : Optional[int]=1536 , UpperCAmelCase_ : List[str]=1536 , UpperCAmelCase_ : List[str]=0.0 , UpperCAmelCase_ : int=0.0 , UpperCAmelCase_ : List[Any]=5_0257 , UpperCAmelCase_ : Union[str, Any]=True , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : str="gelu" , UpperCAmelCase_ : Optional[Any]=256 , UpperCAmelCase_ : List[str]=0.0 , UpperCAmelCase_ : int=0.0 , UpperCAmelCase_ : str=0.0 , UpperCAmelCase_ : List[str]=0.02 , UpperCAmelCase_ : Dict=False , UpperCAmelCase_ : Optional[Any]=1500 , UpperCAmelCase_ : Optional[int]=448 , UpperCAmelCase_ : Any=5_0256 , UpperCAmelCase_ : Any=5_0256 , UpperCAmelCase_ : Dict=5_0256 , UpperCAmelCase_ : str=None , UpperCAmelCase_ : List[str]=[220, 5_0256] , UpperCAmelCase_ : Optional[int]=False , UpperCAmelCase_ : List[str]=256 , UpperCAmelCase_ : Tuple=False , UpperCAmelCase_ : Tuple=0.05 , UpperCAmelCase_ : str=10 , UpperCAmelCase_ : Any=2 , UpperCAmelCase_ : List[Any]=0.0 , UpperCAmelCase_ : List[Any]=10 , UpperCAmelCase_ : Optional[Any]=0 , UpperCAmelCase_ : int=7 , **UpperCAmelCase_ : Any , ):
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = num_mel_bins
SCREAMING_SNAKE_CASE : List[Any] = d_model
SCREAMING_SNAKE_CASE : Optional[Any] = encoder_layers
SCREAMING_SNAKE_CASE : Tuple = encoder_attention_heads
SCREAMING_SNAKE_CASE : List[str] = decoder_layers
SCREAMING_SNAKE_CASE : int = decoder_attention_heads
SCREAMING_SNAKE_CASE : Dict = decoder_ffn_dim
SCREAMING_SNAKE_CASE : Tuple = encoder_ffn_dim
SCREAMING_SNAKE_CASE : Any = dropout
SCREAMING_SNAKE_CASE : List[str] = attention_dropout
SCREAMING_SNAKE_CASE : List[str] = activation_dropout
SCREAMING_SNAKE_CASE : List[Any] = activation_function
SCREAMING_SNAKE_CASE : Dict = init_std
SCREAMING_SNAKE_CASE : Any = encoder_layerdrop
SCREAMING_SNAKE_CASE : List[Any] = decoder_layerdrop
SCREAMING_SNAKE_CASE : Optional[int] = use_cache
SCREAMING_SNAKE_CASE : Any = encoder_layers
SCREAMING_SNAKE_CASE : List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE : Optional[Any] = max_source_positions
SCREAMING_SNAKE_CASE : int = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE : Tuple = classifier_proj_size
SCREAMING_SNAKE_CASE : int = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE : Any = apply_spec_augment
SCREAMING_SNAKE_CASE : Any = mask_time_prob
SCREAMING_SNAKE_CASE : Optional[Any] = mask_time_length
SCREAMING_SNAKE_CASE : Any = mask_time_min_masks
SCREAMING_SNAKE_CASE : Optional[int] = mask_feature_prob
SCREAMING_SNAKE_CASE : Optional[int] = mask_feature_length
SCREAMING_SNAKE_CASE : Dict = mask_feature_min_masks
SCREAMING_SNAKE_CASE : Tuple = median_filter_width
super().__init__(
pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , is_encoder_decoder=UpperCAmelCase_ , decoder_start_token_id=UpperCAmelCase_ , suppress_tokens=UpperCAmelCase_ , begin_suppress_tokens=UpperCAmelCase_ , **UpperCAmelCase_ , )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@property
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : List[Any] = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE : int = {0: "batch"}
else:
SCREAMING_SNAKE_CASE : int = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase_ , direction="inputs" )
return common_inputs
def _A ( self : List[Any] , UpperCAmelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional["TensorType"] = None , UpperCAmelCase_ : int = 2_2050 , UpperCAmelCase_ : float = 5.0 , UpperCAmelCase_ : int = 220 , ):
SCREAMING_SNAKE_CASE : List[Any] = OrderedDict()
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=UpperCAmelCase_ , framework=UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , time_duration=UpperCAmelCase_ , frequency=UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Dict = encoder_inputs["input_features"].shape[2]
SCREAMING_SNAKE_CASE : List[str] = encoder_sequence_length // 2 if self.use_past else seq_length
SCREAMING_SNAKE_CASE : Union[str, Any] = super().generate_dummy_inputs(
preprocessor.tokenizer , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = encoder_inputs.pop("input_features" )
SCREAMING_SNAKE_CASE : Tuple = decoder_inputs.pop("decoder_input_ids" )
if "past_key_values" in decoder_inputs:
SCREAMING_SNAKE_CASE : int = decoder_inputs.pop("past_key_values" )
return dummy_inputs
@property
def _A ( self : Any ):
return 1E-3
| 319
|
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
snake_case = {
"""b0""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1_408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1_536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1_792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2_304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2_560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = EfficientNetConfig()
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["hidden_dim"]
SCREAMING_SNAKE_CASE : Tuple = CONFIG_MAP[model_name]["width_coef"]
SCREAMING_SNAKE_CASE : Optional[int] = CONFIG_MAP[model_name]["depth_coef"]
SCREAMING_SNAKE_CASE : Union[str, Any] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = CONFIG_MAP[model_name]["dropout_rate"]
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["dw_padding"]
SCREAMING_SNAKE_CASE : str = "huggingface/label-files"
SCREAMING_SNAKE_CASE : str = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE : str = 1000
SCREAMING_SNAKE_CASE : List[Any] = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE : Tuple = {int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Union[str, Any] = idalabel
SCREAMING_SNAKE_CASE : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE : List[Any] = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : int = EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=lowercase , )
return preprocessor
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
SCREAMING_SNAKE_CASE : List[str] = sorted(set(lowercase ) )
SCREAMING_SNAKE_CASE : List[str] = len(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = {b: str(lowercase ) for b, i in zip(lowercase , range(lowercase ) )}
SCREAMING_SNAKE_CASE : Dict = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
SCREAMING_SNAKE_CASE : Tuple = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
SCREAMING_SNAKE_CASE : int = {}
for item in rename_keys:
if item[0] in original_param_names:
SCREAMING_SNAKE_CASE : Any = "efficientnet." + item[1]
SCREAMING_SNAKE_CASE : Optional[Any] = "classifier.weight"
SCREAMING_SNAKE_CASE : List[str] = "classifier.bias"
return key_mapping
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
SCREAMING_SNAKE_CASE : str = key_mapping[key]
if "_conv" in key and "kernel" in key:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
SCREAMING_SNAKE_CASE : int = torch.from_numpy(lowercase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
SCREAMING_SNAKE_CASE : List[str] = torch.from_numpy(np.transpose(lowercase ) )
else:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(lowercase )
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = model_classes[model_name](
include_top=lowercase , weights="imagenet" , input_tensor=lowercase , input_shape=lowercase , pooling=lowercase , classes=1000 , classifier_activation="softmax" , )
SCREAMING_SNAKE_CASE : List[Any] = original_model.trainable_variables
SCREAMING_SNAKE_CASE : Dict = original_model.non_trainable_variables
SCREAMING_SNAKE_CASE : Dict = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
SCREAMING_SNAKE_CASE : Tuple = param.numpy()
SCREAMING_SNAKE_CASE : Tuple = list(tf_params.keys() )
# Load HuggingFace model
SCREAMING_SNAKE_CASE : Tuple = get_efficientnet_config(lowercase )
SCREAMING_SNAKE_CASE : str = EfficientNetForImageClassification(lowercase ).eval()
SCREAMING_SNAKE_CASE : Dict = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
SCREAMING_SNAKE_CASE : Dict = rename_keys(lowercase )
replace_params(lowercase , lowercase , lowercase )
# Initialize preprocessor and preprocess input image
SCREAMING_SNAKE_CASE : Optional[int] = convert_image_processor(lowercase )
SCREAMING_SNAKE_CASE : int = preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = hf_model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.logits.detach().numpy()
# Original model inference
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
SCREAMING_SNAKE_CASE : Tuple = image.img_to_array(lowercase )
SCREAMING_SNAKE_CASE : Tuple = np.expand_dims(lowercase , axis=0 )
SCREAMING_SNAKE_CASE : Any = original_model.predict(lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(lowercase , lowercase , atol=1E-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(lowercase ):
os.mkdir(lowercase )
# Save converted model and image processor
hf_model.save_pretrained(lowercase )
preprocessor.save_pretrained(lowercase )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(lowercase )
hf_model.push_to_hub(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
snake_case = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
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|
import os
import sys
import unittest
snake_case = 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,
)
snake_case = os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
snake_case = os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : List[str] = get_test_to_tester_mapping(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = get_test_to_tester_mapping(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = {"BertModelTest": "BertModelTester"}
SCREAMING_SNAKE_CASE : List[str] = {
"BlipModelTest": "BlipModelTester",
"BlipTextImageModelTest": "BlipTextImageModelsModelTester",
"BlipTextModelTest": "BlipTextModelTester",
"BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
"BlipVQAModelTest": "BlipVQAModelTester",
"BlipVisionModelTest": "BlipVisionModelTester",
}
self.assertEqual(get_test_info.to_json(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(get_test_info.to_json(UpperCAmelCase_ ) , UpperCAmelCase_ )
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : List[str] = get_model_to_test_mapping(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = get_model_to_test_mapping(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = {
"BertForMaskedLM": ["BertModelTest"],
"BertForMultipleChoice": ["BertModelTest"],
"BertForNextSentencePrediction": ["BertModelTest"],
"BertForPreTraining": ["BertModelTest"],
"BertForQuestionAnswering": ["BertModelTest"],
"BertForSequenceClassification": ["BertModelTest"],
"BertForTokenClassification": ["BertModelTest"],
"BertLMHeadModel": ["BertModelTest"],
"BertModel": ["BertModelTest"],
}
SCREAMING_SNAKE_CASE : Dict = {
"BlipForConditionalGeneration": ["BlipTextImageModelTest"],
"BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
"BlipForQuestionAnswering": ["BlipVQAModelTest"],
"BlipModel": ["BlipModelTest"],
"BlipTextModel": ["BlipTextModelTest"],
"BlipVisionModel": ["BlipVisionModelTest"],
}
self.assertEqual(get_test_info.to_json(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(get_test_info.to_json(UpperCAmelCase_ ) , UpperCAmelCase_ )
def _A ( self : str ):
SCREAMING_SNAKE_CASE : str = get_model_to_tester_mapping(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = get_model_to_tester_mapping(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = {
"BertForMaskedLM": ["BertModelTester"],
"BertForMultipleChoice": ["BertModelTester"],
"BertForNextSentencePrediction": ["BertModelTester"],
"BertForPreTraining": ["BertModelTester"],
"BertForQuestionAnswering": ["BertModelTester"],
"BertForSequenceClassification": ["BertModelTester"],
"BertForTokenClassification": ["BertModelTester"],
"BertLMHeadModel": ["BertModelTester"],
"BertModel": ["BertModelTester"],
}
SCREAMING_SNAKE_CASE : Any = {
"BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
"BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
"BlipForQuestionAnswering": ["BlipVQAModelTester"],
"BlipModel": ["BlipModelTester"],
"BlipTextModel": ["BlipTextModelTester"],
"BlipVisionModel": ["BlipVisionModelTester"],
}
self.assertEqual(get_test_info.to_json(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(get_test_info.to_json(UpperCAmelCase_ ) , UpperCAmelCase_ )
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|
def lowerCamelCase__ ( ):
"""simple docstring"""
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
snake_case = generate_large_matrix()
snake_case = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
assert all(row == sorted(lowercase , reverse=lowercase ) for row in grid )
assert all(list(lowercase ) == sorted(lowercase , reverse=lowercase ) for col in zip(*lowercase ) )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowercase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
SCREAMING_SNAKE_CASE : List[Any] = (left + right) // 2
SCREAMING_SNAKE_CASE : Optional[int] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
SCREAMING_SNAKE_CASE : List[Any] = mid + 1
else:
SCREAMING_SNAKE_CASE : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : List[str] = len(grid[0] )
for i in range(len(lowercase ) ):
SCREAMING_SNAKE_CASE : Any = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowercase ) * len(grid[0] )) - total
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return len([number for row in grid for number in row if number < 0] )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = 0
for row in grid:
for i, number in enumerate(lowercase ):
if number < 0:
total += len(lowercase ) - i
break
return total
def lowerCamelCase__ ( ):
"""simple docstring"""
from timeit import timeit
print("Running benchmarks" )
SCREAMING_SNAKE_CASE : List[str] = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
SCREAMING_SNAKE_CASE : Union[str, Any] = timeit(F'''{func}(grid=grid)''' , setup=lowercase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
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| 1
|
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Tuple=13 , UpperCAmelCase_ : str=30 , UpperCAmelCase_ : List[str]=2 , UpperCAmelCase_ : str=3 , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : int=32 , UpperCAmelCase_ : Optional[Any]=2 , UpperCAmelCase_ : Optional[int]=4 , UpperCAmelCase_ : Union[str, Any]=37 , UpperCAmelCase_ : Optional[Any]="gelu" , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : Dict=0.1 , UpperCAmelCase_ : Dict=10 , UpperCAmelCase_ : Optional[Any]=0.02 , UpperCAmelCase_ : Optional[Any]=3 , UpperCAmelCase_ : Tuple=None , ):
SCREAMING_SNAKE_CASE : Union[str, Any] = parent
SCREAMING_SNAKE_CASE : Dict = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = image_size
SCREAMING_SNAKE_CASE : List[str] = patch_size
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : int = is_training
SCREAMING_SNAKE_CASE : Any = use_labels
SCREAMING_SNAKE_CASE : Tuple = hidden_size
SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Dict = num_attention_heads
SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : Any = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE : Dict = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE : Union[str, Any] = num_patches + 1
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : str = None
if self.use_labels:
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : Tuple = self.get_config()
return config, pixel_values, labels
def _A ( self : Union[str, Any] ):
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCAmelCase_ , initializer_range=self.initializer_range , )
def _A ( self : Optional[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Any , UpperCAmelCase_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE : List[str] = TFViTModel(config=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = model(UpperCAmelCase_ , training=UpperCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
SCREAMING_SNAKE_CASE : Any = self.image_size // 2
SCREAMING_SNAKE_CASE : Optional[Any] = pixel_values[:, :, :image_size, :image_size]
SCREAMING_SNAKE_CASE : int = model(UpperCAmelCase_ , interpolate_pos_encoding=UpperCAmelCase_ , training=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def _A ( self : List[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Optional[Any] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE : List[str] = TFViTForImageClassification(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = model(UpperCAmelCase_ , labels=UpperCAmelCase_ , training=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
SCREAMING_SNAKE_CASE : str = self.image_size // 2
SCREAMING_SNAKE_CASE : Union[str, Any] = pixel_values[:, :, :image_size, :image_size]
SCREAMING_SNAKE_CASE : int = model(UpperCAmelCase_ , interpolate_pos_encoding=UpperCAmelCase_ , training=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE : Union[str, Any] = 1
SCREAMING_SNAKE_CASE : Tuple = TFViTForImageClassification(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : List[Any] = model(UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : int = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
UpperCamelCase_ : List[str] = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
UpperCamelCase_ : Union[str, Any] = False
UpperCamelCase_ : Any = False
UpperCamelCase_ : List[Any] = False
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : Any = TFViTModelTester(self )
SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=UpperCAmelCase_ , has_text_modality=UpperCAmelCase_ , hidden_size=37 )
def _A ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def _A ( self : Optional[int] ):
pass
@unittest.skip(reason="ViT does not use inputs_embeds" )
def _A ( self : int ):
pass
def _A ( self : Any ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : List[str] = model_class(UpperCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
SCREAMING_SNAKE_CASE : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase_ , tf.keras.layers.Layer ) )
def _A ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Tuple = model_class(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : Optional[Any] = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCAmelCase_ )
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
@slow
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : List[Any] = TFViTModel.from_pretrained("google/vit-base-patch16-224" )
self.assertIsNotNone(UpperCAmelCase_ )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _A ( self : Optional[int] ):
return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224" ) if is_vision_available() else None
@slow
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Optional[int] = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224" )
SCREAMING_SNAKE_CASE : List[Any] = self.default_image_processor
SCREAMING_SNAKE_CASE : int = prepare_img()
SCREAMING_SNAKE_CASE : int = image_processor(images=UpperCAmelCase_ , return_tensors="tf" )
# forward pass
SCREAMING_SNAKE_CASE : Optional[int] = model(**UpperCAmelCase_ )
# verify the logits
SCREAMING_SNAKE_CASE : int = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = tf.constant([-0.2_744, 0.8_215, -0.0_836] )
tf.debugging.assert_near(outputs.logits[0, :3] , UpperCAmelCase_ , atol=1E-4 )
| 319
|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case = ["""small""", """medium""", """large"""]
snake_case = """lm_head.decoder.weight"""
snake_case = """lm_head.weight"""
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = torch.load(lowercase )
SCREAMING_SNAKE_CASE : Any = d.pop(lowercase )
os.makedirs(lowercase , exist_ok=lowercase )
torch.save(lowercase , os.path.join(lowercase , lowercase ) )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
snake_case = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
snake_case = os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
snake_case = F"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 319
| 1
|
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
SCREAMING_SNAKE_CASE : List[Any] = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
SCREAMING_SNAKE_CASE : Any = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
SCREAMING_SNAKE_CASE : str = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
SCREAMING_SNAKE_CASE : int = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
SCREAMING_SNAKE_CASE : List[str] = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
SCREAMING_SNAKE_CASE : int = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
SCREAMING_SNAKE_CASE : Tuple = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
SCREAMING_SNAKE_CASE : Optional[int] = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
SCREAMING_SNAKE_CASE : List[str] = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
SCREAMING_SNAKE_CASE : Union[str, Any] = key.replace("image_encoder.module" , "flava.image_model" )
SCREAMING_SNAKE_CASE : Optional[int] = key.replace("text_encoder.module" , "flava.text_model" )
SCREAMING_SNAKE_CASE : Optional[Any] = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
SCREAMING_SNAKE_CASE : Dict = key.replace("mm_encoder.module" , "flava.multimodal_model" )
SCREAMING_SNAKE_CASE : Union[str, Any] = key.replace("text_projection" , "flava.text_projection" )
SCREAMING_SNAKE_CASE : Tuple = key.replace("image_projection" , "flava.image_projection" )
SCREAMING_SNAKE_CASE : int = value.float()
for key, value in codebook_state_dict.items():
SCREAMING_SNAKE_CASE : str = value
return upgrade
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase=None ):
"""simple docstring"""
if config_path is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = FlavaConfig.from_pretrained(lowercase )
else:
SCREAMING_SNAKE_CASE : str = FlavaConfig()
SCREAMING_SNAKE_CASE : List[str] = FlavaForPreTraining(lowercase ).eval()
SCREAMING_SNAKE_CASE : List[Any] = convert_dalle_checkpoint(lowercase , lowercase , save_checkpoint=lowercase )
if os.path.exists(lowercase ):
SCREAMING_SNAKE_CASE : Optional[int] = torch.load(lowercase , map_location="cpu" )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.hub.load_state_dict_from_url(lowercase , map_location="cpu" )
SCREAMING_SNAKE_CASE : Dict = upgrade_state_dict(lowercase , lowercase )
hf_model.load_state_dict(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = hf_model.state_dict()
SCREAMING_SNAKE_CASE : List[Any] = count_parameters(lowercase )
SCREAMING_SNAKE_CASE : Tuple = count_parameters(lowercase ) + count_parameters(lowercase )
assert torch.allclose(lowercase , lowercase , atol=1E-3 )
hf_model.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
snake_case = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 319
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""MLukeTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = GPTSanJapaneseTokenizer
UpperCamelCase_ : Any = False
UpperCamelCase_ : Union[str, Any] = {'''do_clean_text''': False, '''add_prefix_space''': False}
def _A ( self : List[str] ):
super().setUp()
# fmt: off
SCREAMING_SNAKE_CASE : str = ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
# fmt: on
SCREAMING_SNAKE_CASE : List[str] = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}} # 😀
SCREAMING_SNAKE_CASE : Any = {"unk_token": "<unk>"}
SCREAMING_SNAKE_CASE : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["emoji_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
with open(self.emoji_file , "w" ) as emoji_writer:
emoji_writer.write(json.dumps(UpperCAmelCase_ ) )
def _A ( self : Optional[int] , **UpperCAmelCase_ : List[str] ):
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : Dict = "こんにちは、世界。 \nこんばんは、㔺界。😀"
SCREAMING_SNAKE_CASE : Optional[int] = "こんにちは、世界。 \nこんばんは、世界。😀"
return input_text, output_text
def _A ( self : Optional[Any] , UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = self.get_input_output_texts(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = tokenizer.decode(UpperCAmelCase_ , clean_up_tokenization_spaces=UpperCAmelCase_ )
return text, ids
def _A ( self : List[Any] ):
pass # TODO add if relevant
def _A ( self : Tuple ):
pass # TODO add if relevant
def _A ( self : Union[str, Any] ):
pass # TODO add if relevant
def _A ( self : List[Any] ):
SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
# Testing tokenization
SCREAMING_SNAKE_CASE : Dict = "こんにちは、世界。 こんばんは、㔺界。"
SCREAMING_SNAKE_CASE : int = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.tokenize(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
# Testing conversion to ids without special tokens
SCREAMING_SNAKE_CASE : Optional[int] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
SCREAMING_SNAKE_CASE : Tuple = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
# Testing conversion to ids with special tokens
SCREAMING_SNAKE_CASE : Union[str, Any] = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE : Optional[int] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
SCREAMING_SNAKE_CASE : Any = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
# Testing tokenization
SCREAMING_SNAKE_CASE : str = "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
SCREAMING_SNAKE_CASE : int = "こんにちは、、、、世界。こんばんは、、、、世界。"
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.encode(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.decode(UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : List[Any] ):
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
# Testing tokenization
SCREAMING_SNAKE_CASE : List[Any] = "こんにちは、世界。"
SCREAMING_SNAKE_CASE : List[str] = "こんばんは、㔺界。😀"
SCREAMING_SNAKE_CASE : Dict = "こんにちは、世界。こんばんは、世界。😀"
SCREAMING_SNAKE_CASE : Dict = tokenizer.encode(prefix_text + input_text )
SCREAMING_SNAKE_CASE : str = tokenizer.encode("" , prefix_text=prefix_text + input_text )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.encode(UpperCAmelCase_ , prefix_text=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.decode(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = tokenizer.decode(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.decode(UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : str ):
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
# Testing tokenization
SCREAMING_SNAKE_CASE : Optional[int] = "こんにちは、世界。"
SCREAMING_SNAKE_CASE : List[Any] = "こんばんは、㔺界。😀"
SCREAMING_SNAKE_CASE : Any = len(tokenizer.encode(UpperCAmelCase_ ) ) - 2
SCREAMING_SNAKE_CASE : List[Any] = len(tokenizer.encode(UpperCAmelCase_ ) ) - 2
SCREAMING_SNAKE_CASE : List[Any] = [1] + [0] * (len_prefix + len_text + 1)
SCREAMING_SNAKE_CASE : Union[str, Any] = [1] * (len_prefix + len_text + 1) + [0]
SCREAMING_SNAKE_CASE : int = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer(prefix_text + input_text ).token_type_ids
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer("" , prefix_text=prefix_text + input_text ).token_type_ids
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(UpperCAmelCase_ , prefix_text=UpperCAmelCase_ ).token_type_ids
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : Any = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.encode("あンいワ" )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.encode("" , prefix_text="あンいワ" )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.encode("いワ" , prefix_text="あン" )
self.assertEqual(tokenizer.decode(UpperCAmelCase_ ) , tokenizer.decode(UpperCAmelCase_ ) )
self.assertEqual(tokenizer.decode(UpperCAmelCase_ ) , tokenizer.decode(UpperCAmelCase_ ) )
self.assertNotEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertNotEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )
SCREAMING_SNAKE_CASE : str = [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
SCREAMING_SNAKE_CASE : List[Any] = tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = tokenizer.batch_encode_plus(UpperCAmelCase_ , padding=UpperCAmelCase_ )
# fmt: off
SCREAMING_SNAKE_CASE : Optional[Any] = [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
SCREAMING_SNAKE_CASE : Dict = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
SCREAMING_SNAKE_CASE : str = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , UpperCAmelCase_ )
self.assertListEqual(x_token.token_type_ids , UpperCAmelCase_ )
self.assertListEqual(x_token.attention_mask , UpperCAmelCase_ )
self.assertListEqual(x_token_a.input_ids , UpperCAmelCase_ )
self.assertListEqual(x_token_a.token_type_ids , UpperCAmelCase_ )
self.assertListEqual(x_token_a.attention_mask , UpperCAmelCase_ )
def _A ( self : Any ):
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def _A ( self : List[str] ):
# tokenizer has no padding token
pass
| 319
|
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase__ ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 319
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
|
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : Union[str, Any] = set_counts
SCREAMING_SNAKE_CASE : Any = max(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = [1] * num_sets
SCREAMING_SNAKE_CASE : List[str] = list(range(UpperCAmelCase_ ) )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[Any] = self.get_parent(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self.get_parent(UpperCAmelCase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
SCREAMING_SNAKE_CASE : List[str] = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Tuple = src_parent
SCREAMING_SNAKE_CASE : Optional[int] = self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Optional[Any] = max(self.max_set , UpperCAmelCase_ )
return True
def _A ( self : Tuple , UpperCAmelCase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
SCREAMING_SNAKE_CASE : Tuple = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
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import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Any = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : Optional[int] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Any = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(lowercase , cache_dir=lowercase , split=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = parquet_path
elif issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [parquet_path]
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Tuple = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=("train",) ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
for split in splits:
SCREAMING_SNAKE_CASE : Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Dict = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(
{"train": parquet_path} , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[int] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Dict = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : str = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : Optional[Any] = ParquetDatasetReader({"train": parquet_path} , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if split:
SCREAMING_SNAKE_CASE : Any = {split: parquet_path}
else:
SCREAMING_SNAKE_CASE : Tuple = "train"
SCREAMING_SNAKE_CASE : int = {"train": parquet_path, "test": parquet_path}
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : str = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : int = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : Tuple = pq.ParquetFile(tmp_path / "foo.parquet" )
SCREAMING_SNAKE_CASE : List[Any] = pf.read()
assert dataset.data.table == output_table
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = str(shared_datadir / "test_image_rgb.jpg" )
SCREAMING_SNAKE_CASE : Union[str, Any] = {"image": [image_path]}
SCREAMING_SNAKE_CASE : Union[str, Any] = Features({"image": Image()} )
SCREAMING_SNAKE_CASE : int = Dataset.from_dict(lowercase , features=lowercase )
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : str = Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
assert dataset.features == reloaded_dataset.features
SCREAMING_SNAKE_CASE : Any = ParquetDatasetReader(str(tmp_path / "foo.parquet" ) , streaming=lowercase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32" )} ), None),
(Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert get_writer_batch_size(lowercase ) == expected
| 319
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''timm_backbone'''
def __init__( self : List[Any] , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : List[str]=3 , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : Union[str, Any]=None , **UpperCAmelCase_ : Optional[Any] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = backbone
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = features_only
SCREAMING_SNAKE_CASE : Dict = use_pretrained_backbone
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : List[Any] = out_indices if out_indices is not None else (-1,)
| 319
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|
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
snake_case = numpy.array([0, 0])
snake_case = numpy.array([0.5, 0.8660254])
snake_case = numpy.array([1, 0])
snake_case = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = initial_vectors
for _ in range(lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = iteration_step(lowercase )
return vectors
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for i, start_vector in enumerate(vectors[:-1] ):
SCREAMING_SNAKE_CASE : Dict = vectors[i + 1]
new_vectors.append(lowercase )
SCREAMING_SNAKE_CASE : List[Any] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = numpy.radians(lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = numpy.cos(lowercase ), numpy.sin(lowercase )
SCREAMING_SNAKE_CASE : int = numpy.array(((c, -s), (s, c)) )
return numpy.dot(lowercase , lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = plt.gca()
axes.set_aspect("equal" )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = zip(*lowercase )
plt.plot(lowercase , lowercase )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 319
|
from math import sqrt
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for i in range(1 , int(sqrt(lowercase ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase ):
total += i + n // i
elif i == sqrt(lowercase ):
total += i
return total - n
def lowerCamelCase__ ( lowercase = 10000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = sum(
i
for i in range(1 , lowercase )
if sum_of_divisors(sum_of_divisors(lowercase ) ) == i and sum_of_divisors(lowercase ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 319
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|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case = ["""small""", """medium""", """large"""]
snake_case = """lm_head.decoder.weight"""
snake_case = """lm_head.weight"""
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = torch.load(lowercase )
SCREAMING_SNAKE_CASE : Any = d.pop(lowercase )
os.makedirs(lowercase , exist_ok=lowercase )
torch.save(lowercase , os.path.join(lowercase , lowercase ) )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
snake_case = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
snake_case = os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
snake_case = F"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 319
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EncodecModel""",
"""EncodecPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
from manim import *
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def _A ( self : str ):
SCREAMING_SNAKE_CASE : int = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE : Optional[int] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE : List[Any] = Rectangle(height=0.25 , width=0.25 )
SCREAMING_SNAKE_CASE : Tuple = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE : List[Any] = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE : Tuple = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
SCREAMING_SNAKE_CASE : Optional[int] = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = VGroup(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
SCREAMING_SNAKE_CASE : Dict = Text("CPU" , font_size=24 )
SCREAMING_SNAKE_CASE : Union[str, Any] = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE : Union[str, Any] = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
SCREAMING_SNAKE_CASE : int = Text("GPU" , font_size=24 )
SCREAMING_SNAKE_CASE : Union[str, Any] = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
gpu.move_to([-1, -1, 0] )
self.add(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE : Dict = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
SCREAMING_SNAKE_CASE : Tuple = Text("Model" , font_size=24 )
SCREAMING_SNAKE_CASE : Dict = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
model.move_to([3, -1.0, 0] )
self.add(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : str = []
for i, rect in enumerate(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : List[Any] = fill.copy().set_fill(UpperCAmelCase_ , opacity=0.8 )
target.move_to(UpperCAmelCase_ )
model_arr.append(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(UpperCAmelCase_ , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(UpperCAmelCase_ )
self.add(*UpperCAmelCase_ , *UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = [meta_mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE : str = [meta_mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE : Optional[int] = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
SCREAMING_SNAKE_CASE : List[str] = VGroup(*UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
SCREAMING_SNAKE_CASE : List[str] = VGroup(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0 )
SCREAMING_SNAKE_CASE : List[str] = Text("Disk" , font_size=24 )
SCREAMING_SNAKE_CASE : Union[str, Any] = Group(UpperCAmelCase_ , UpperCAmelCase_ ).arrange(UpperCAmelCase_ , buff=0.5 , aligned_edge=UpperCAmelCase_ )
disk.move_to([-4, -1.25, 0] )
self.add(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE : Any = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = MarkupText(
f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(UpperCAmelCase_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = MarkupText(
f'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Dict = Square(0.3 )
input.set_fill(UpperCAmelCase_ , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , UpperCAmelCase_ , buff=0.5 )
self.play(Write(UpperCAmelCase_ ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=UpperCAmelCase_ , buff=0.02 )
self.play(MoveToTarget(UpperCAmelCase_ ) )
self.play(FadeOut(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = Arrow(start=UpperCAmelCase_ , end=UpperCAmelCase_ , color=UpperCAmelCase_ , buff=0.5 )
a.next_to(model_arr[0].get_left() , UpperCAmelCase_ , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
SCREAMING_SNAKE_CASE : List[str] = MarkupText(
f'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(UpperCAmelCase_ , run_time=3 ) )
SCREAMING_SNAKE_CASE : int = {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}
self.play(
Write(UpperCAmelCase_ ) , Circumscribe(model_arr[0] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , Circumscribe(model_cpu_arr[0] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , Circumscribe(gpu_rect[0] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
SCREAMING_SNAKE_CASE : Any = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , UpperCAmelCase_ , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
SCREAMING_SNAKE_CASE : int = AnimationGroup(
FadeOut(UpperCAmelCase_ , run_time=0.5 ) , MoveToTarget(UpperCAmelCase_ , run_time=0.5 ) , FadeIn(UpperCAmelCase_ , run_time=0.5 ) , lag_ratio=0.2 )
self.play(UpperCAmelCase_ )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
SCREAMING_SNAKE_CASE : List[str] = 0.7
self.play(
Circumscribe(model_arr[i] , **UpperCAmelCase_ ) , Circumscribe(cpu_left_col_base[i] , **UpperCAmelCase_ ) , Circumscribe(cpu_left_col_base[i + 1] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , Circumscribe(gpu_rect[0] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , Circumscribe(model_arr[i + 1] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , Circumscribe(cpu_left_col_base[-1] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , Circumscribe(gpu_rect[0] , color=UpperCAmelCase_ , **UpperCAmelCase_ ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
SCREAMING_SNAKE_CASE : Tuple = a_c
SCREAMING_SNAKE_CASE : Any = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(UpperCAmelCase_ ) , FadeOut(UpperCAmelCase_ , run_time=0.5 ) , )
SCREAMING_SNAKE_CASE : Optional[int] = MarkupText(f'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(UpperCAmelCase_ , run_time=3 ) , MoveToTarget(UpperCAmelCase_ ) )
self.wait()
| 319
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
snake_case = None
snake_case = logging.get_logger(__name__)
snake_case = """▁"""
snake_case = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
snake_case = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
snake_case = {
"""google/pegasus-xsum""": 512,
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES
UpperCamelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : int = PegasusTokenizer
UpperCamelCase_ : str = ['''input_ids''', '''attention_mask''']
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Optional[int]="<pad>" , UpperCAmelCase_ : int="</s>" , UpperCAmelCase_ : str="<unk>" , UpperCAmelCase_ : str="<mask_2>" , UpperCAmelCase_ : Optional[int]="<mask_1>" , UpperCAmelCase_ : int=None , UpperCAmelCase_ : str=103 , **UpperCAmelCase_ : Optional[int] , ):
SCREAMING_SNAKE_CASE : Optional[Any] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError(
f'''additional_special_tokens should be of type {type(UpperCAmelCase_ )}, but is'''
f''' {type(UpperCAmelCase_ )}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(UpperCAmelCase_ ) , self.offset - 1 )
]
if len(set(UpperCAmelCase_ ) ) != len(UpperCAmelCase_ ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
SCREAMING_SNAKE_CASE : int = additional_special_tokens_extended
else:
SCREAMING_SNAKE_CASE : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
super().__init__(
UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , mask_token_sent=UpperCAmelCase_ , offset=UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : str = vocab_file
SCREAMING_SNAKE_CASE : str = False if not self.vocab_file else True
def _A ( self : Optional[Any] , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Optional[int] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
f''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : int , UpperCAmelCase_ : List , UpperCAmelCase_ : Optional[List] = None , UpperCAmelCase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(UpperCAmelCase_ )
elif token_ids_a is None:
return self._special_token_mask(UpperCAmelCase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Any=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : str , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
UpperCAmelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase_ ):
copyfile(self.vocab_file , UpperCAmelCase_ )
return (out_vocab_file,)
| 319
| 1
|
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case = 0
snake_case = [
[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 = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
snake_case = tuple[int, int]
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : Node | None , ):
SCREAMING_SNAKE_CASE : Optional[int] = pos_x
SCREAMING_SNAKE_CASE : str = pos_y
SCREAMING_SNAKE_CASE : Optional[Any] = (pos_y, pos_x)
SCREAMING_SNAKE_CASE : Dict = goal_x
SCREAMING_SNAKE_CASE : List[str] = goal_y
SCREAMING_SNAKE_CASE : Optional[Any] = g_cost
SCREAMING_SNAKE_CASE : str = parent
SCREAMING_SNAKE_CASE : Dict = self.calculate_heuristic()
SCREAMING_SNAKE_CASE : Dict = self.g_cost + self.h_cost
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : Optional[int] = self.pos_x - self.goal_x
SCREAMING_SNAKE_CASE : List[str] = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(UpperCAmelCase_ ) + abs(UpperCAmelCase_ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : Optional[int] , UpperCAmelCase_ : Node ):
return self.f_cost < other.f_cost
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : TPosition , UpperCAmelCase_ : TPosition ):
SCREAMING_SNAKE_CASE : Any = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9999 , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = [self.start]
SCREAMING_SNAKE_CASE : list[Node] = []
SCREAMING_SNAKE_CASE : int = False
def _A ( self : Tuple ):
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
SCREAMING_SNAKE_CASE : str = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(UpperCAmelCase_ )
self.closed_nodes.append(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = self.get_successors(UpperCAmelCase_ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(UpperCAmelCase_ )
else:
# retrieve the best current path
SCREAMING_SNAKE_CASE : Dict = self.open_nodes.pop(self.open_nodes.index(UpperCAmelCase_ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(UpperCAmelCase_ )
else:
self.open_nodes.append(UpperCAmelCase_ )
return [self.start.pos]
def _A ( self : List[Any] , UpperCAmelCase_ : Node ):
SCREAMING_SNAKE_CASE : Dict = []
for action in delta:
SCREAMING_SNAKE_CASE : int = parent.pos_x + action[1]
SCREAMING_SNAKE_CASE : Optional[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCAmelCase_ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
UpperCAmelCase_ , UpperCAmelCase_ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , UpperCAmelCase_ , ) )
return successors
def _A ( self : str , UpperCAmelCase_ : Node | None ):
SCREAMING_SNAKE_CASE : Dict = node
SCREAMING_SNAKE_CASE : Optional[Any] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
SCREAMING_SNAKE_CASE : str = current_node.parent
path.reverse()
return path
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , UpperCAmelCase_ : TPosition , UpperCAmelCase_ : TPosition ):
SCREAMING_SNAKE_CASE : Union[str, Any] = AStar(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = AStar(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = False
def _A ( self : Union[str, Any] ):
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
SCREAMING_SNAKE_CASE : List[str] = self.fwd_astar.open_nodes.pop(0 )
SCREAMING_SNAKE_CASE : str = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
UpperCAmelCase_ , UpperCAmelCase_ )
self.fwd_astar.closed_nodes.append(UpperCAmelCase_ )
self.bwd_astar.closed_nodes.append(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = current_bwd_node
SCREAMING_SNAKE_CASE : Optional[int] = current_fwd_node
SCREAMING_SNAKE_CASE : Optional[int] = {
self.fwd_astar: self.fwd_astar.get_successors(UpperCAmelCase_ ),
self.bwd_astar: self.bwd_astar.get_successors(UpperCAmelCase_ ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(UpperCAmelCase_ )
else:
# retrieve the best current path
SCREAMING_SNAKE_CASE : Dict = astar.open_nodes.pop(
astar.open_nodes.index(UpperCAmelCase_ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(UpperCAmelCase_ )
else:
astar.open_nodes.append(UpperCAmelCase_ )
return [self.fwd_astar.start.pos]
def _A ( self : Optional[Any] , UpperCAmelCase_ : Node , UpperCAmelCase_ : Node ):
SCREAMING_SNAKE_CASE : List[Any] = self.fwd_astar.retrace_path(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self.bwd_astar.retrace_path(UpperCAmelCase_ )
bwd_path.pop()
bwd_path.reverse()
SCREAMING_SNAKE_CASE : Dict = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
snake_case = (0, 0)
snake_case = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
snake_case = time.time()
snake_case = AStar(init, goal)
snake_case = a_star.search()
snake_case = time.time() - start_time
print(F"""AStar execution time = {end_time:f} seconds""")
snake_case = time.time()
snake_case = BidirectionalAStar(init, goal)
snake_case = time.time() - bd_start_time
print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")
| 319
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""SpeechEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""FlaxSpeechEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 319
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
snake_case = 16
snake_case = 32
def lowerCamelCase__ ( lowercase , lowercase = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained("bert-base-cased" )
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset("glue" , "mrpc" )
def tokenize_function(lowercase ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=lowercase , max_length=lowercase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE : List[Any] = datasets.map(
lowercase , batched=lowercase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE : Tuple = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE : str = 16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE : Optional[Any] = 8
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = None
return tokenizer.pad(
lowercase , padding="longest" , max_length=lowercase , pad_to_multiple_of=lowercase , return_tensors="pt" , )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE : Optional[int] = DataLoader(
tokenized_datasets["train"] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
SCREAMING_SNAKE_CASE : Dict = DataLoader(
tokenized_datasets["validation"] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
snake_case = mocked_dataloaders # noqa: F811
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , lowercase ) == "1":
SCREAMING_SNAKE_CASE : int = 2
# New Code #
SCREAMING_SNAKE_CASE : Union[str, Any] = int(args.gradient_accumulation_steps )
# Initialize accelerator
SCREAMING_SNAKE_CASE : Tuple = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE : Any = config["lr"]
SCREAMING_SNAKE_CASE : Optional[Any] = int(config["num_epochs"] )
SCREAMING_SNAKE_CASE : List[Any] = int(config["seed"] )
SCREAMING_SNAKE_CASE : Union[str, Any] = int(config["batch_size"] )
SCREAMING_SNAKE_CASE : Optional[Any] = evaluate.load("glue" , "mrpc" )
set_seed(lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = get_dataloaders(lowercase , lowercase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE : List[Any] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=lowercase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE : Any = model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE : Any = AdamW(params=model.parameters() , lr=lowercase )
# Instantiate scheduler
SCREAMING_SNAKE_CASE : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=lowercase , num_warmup_steps=100 , num_training_steps=(len(lowercase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = accelerator.prepare(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Now we train the model
for epoch in range(lowercase ):
model.train()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase ):
SCREAMING_SNAKE_CASE : Any = model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = output.loss
accelerator.backward(lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[Any] = model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=lowercase , references=lowercase , )
SCREAMING_SNAKE_CASE : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowercase )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=lowercase , default=lowercase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=lowercase , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
SCREAMING_SNAKE_CASE : Dict = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(lowercase , lowercase )
if __name__ == "__main__":
main()
| 319
| 1
|
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : int ):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
SCREAMING_SNAKE_CASE : Optional[Any] = deepcopy(UpperCAmelCase_ )
elif os.path.exists(UpperCAmelCase_ ):
with io.open(UpperCAmelCase_ , "r" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE : Union[str, Any] = json.load(UpperCAmelCase_ )
else:
try:
SCREAMING_SNAKE_CASE : List[str] = baseaa.urlsafe_baadecode(UpperCAmelCase_ ).decode("utf-8" )
SCREAMING_SNAKE_CASE : str = json.loads(UpperCAmelCase_ )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = config
self.set_stage_and_offload()
def _A ( self : int ):
# zero stage - this is done as early as possible, before model is created, to allow
# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
SCREAMING_SNAKE_CASE : int = self.get_value("zero_optimization.stage" , -1 )
# offload
SCREAMING_SNAKE_CASE : List[str] = False
if self.is_zeroa() or self.is_zeroa():
SCREAMING_SNAKE_CASE : str = set(["cpu", "nvme"] )
SCREAMING_SNAKE_CASE : Any = set(
[
self.get_value("zero_optimization.offload_optimizer.device" ),
self.get_value("zero_optimization.offload_param.device" ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
SCREAMING_SNAKE_CASE : Optional[int] = True
def _A ( self : List[str] , UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE : Optional[int] = self.config
# find the config node of interest if it exists
SCREAMING_SNAKE_CASE : Optional[Any] = ds_key_long.split("." )
SCREAMING_SNAKE_CASE : str = nodes.pop()
for node in nodes:
SCREAMING_SNAKE_CASE : str = config.get(UpperCAmelCase_ )
if config is None:
return None, ds_key
return config, ds_key
def _A ( self : Dict , UpperCAmelCase_ : int , UpperCAmelCase_ : int=None ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = self.find_config_node(UpperCAmelCase_ )
if config is None:
return default
return config.get(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Dict , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple=False ):
SCREAMING_SNAKE_CASE : Tuple = self.config
# find the config node of interest if it exists
SCREAMING_SNAKE_CASE : List[Any] = ds_key_long.split("." )
for node in nodes:
SCREAMING_SNAKE_CASE : Tuple = config
SCREAMING_SNAKE_CASE : List[str] = config.get(UpperCAmelCase_ )
if config is None:
if must_exist:
raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}''' )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(UpperCAmelCase_ )
def _A ( self : List[Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_value(UpperCAmelCase_ )
return False if value is None else bool(UpperCAmelCase_ )
def _A ( self : List[Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : Optional[int] = self.get_value(UpperCAmelCase_ )
return False if value is None else not bool(UpperCAmelCase_ )
def _A ( self : Optional[Any] ):
return self._stage == 2
def _A ( self : str ):
return self._stage == 3
def _A ( self : Optional[int] ):
return self._offload
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Any , UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE : Optional[Any] = engine
def _A ( self : Dict , UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ):
# runs backpropagation and handles mixed precision
self.engine.backward(UpperCAmelCase_ , **UpperCAmelCase_ )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Tuple ):
super().__init__(UpperCAmelCase_ , device_placement=UpperCAmelCase_ , scaler=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = hasattr(self.optimizer , "overflow" )
def _A ( self : Optional[Any] , UpperCAmelCase_ : int=None ):
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def _A ( self : Any ):
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def _A ( self : List[str] ):
if self.__has_overflow__:
return self.optimizer.overflow
return False
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Dict ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : str ):
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : Any , UpperCAmelCase_ : int=0.001 , UpperCAmelCase_ : Dict=0 , **UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = params
SCREAMING_SNAKE_CASE : Optional[Any] = lr
SCREAMING_SNAKE_CASE : List[Any] = weight_decay
SCREAMING_SNAKE_CASE : int = kwargs
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : Dict=0 , **UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE : Tuple = optimizer
SCREAMING_SNAKE_CASE : List[Any] = total_num_steps
SCREAMING_SNAKE_CASE : Dict = warmup_num_steps
SCREAMING_SNAKE_CASE : Any = kwargs
| 319
|
import functools
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ) or not all(isinstance(lowercase , lowercase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(lowercase ) != 3 or not all(isinstance(lowercase , lowercase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(lowercase ) == 0:
return 0
if min(lowercase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(lowercase ) >= 366:
raise ValueError("All days elements should be less than 366" )
SCREAMING_SNAKE_CASE : Dict = set(lowercase )
@functools.cache
def dynamic_programming(lowercase ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
| 1
|
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , lowercase = None , lowercase = None , lowercase = None , ):
"""simple docstring"""
if config_name_or_path is None:
SCREAMING_SNAKE_CASE : int = "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"
if generator_tokenizer_name_or_path is None:
SCREAMING_SNAKE_CASE : List[Any] = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
SCREAMING_SNAKE_CASE : Optional[Any] = question_encoder_name_or_path
SCREAMING_SNAKE_CASE : Dict = RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration
# Save model.
SCREAMING_SNAKE_CASE : Optional[int] = RagConfig.from_pretrained(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(lowercase )
SCREAMING_SNAKE_CASE : List[str] = gen_config
SCREAMING_SNAKE_CASE : List[Any] = question_encoder_config
SCREAMING_SNAKE_CASE : Optional[int] = model_class.from_pretrained_question_encoder_generator(
lowercase , lowercase , config=lowercase )
rag_model.save_pretrained(lowercase )
# Sanity check.
model_class.from_pretrained(lowercase )
# Save tokenizers.
SCREAMING_SNAKE_CASE : int = AutoTokenizer.from_pretrained(lowercase )
gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/" )
SCREAMING_SNAKE_CASE : Optional[Any] = AutoTokenizer.from_pretrained(lowercase )
question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/" )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
snake_case = parser.parse_args()
snake_case = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 319
|
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 319
| 1
|
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
snake_case = TypeVar("""KEY""")
snake_case = TypeVar("""VAL""")
@dataclass(frozen=lowerCAmelCase , slots=lowerCAmelCase )
class SCREAMING_SNAKE_CASE ( Generic[KEY, VAL] ):
'''simple docstring'''
UpperCamelCase_ : KEY
UpperCamelCase_ : VAL
class SCREAMING_SNAKE_CASE ( _Item ):
'''simple docstring'''
def __init__( self : Optional[int] ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
def __bool__( self : List[str] ):
return False
snake_case = _DeletedItem()
class SCREAMING_SNAKE_CASE ( MutableMapping[KEY, VAL] ):
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : int = 8 , UpperCAmelCase_ : float = 0.75 ):
SCREAMING_SNAKE_CASE : Optional[Any] = initial_block_size
SCREAMING_SNAKE_CASE : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
SCREAMING_SNAKE_CASE : str = capacity_factor
SCREAMING_SNAKE_CASE : Optional[Any] = 0
def _A ( self : Union[str, Any] , UpperCAmelCase_ : KEY ):
return hash(UpperCAmelCase_ ) % len(self._buckets )
def _A ( self : Optional[Any] , UpperCAmelCase_ : int ):
return (ind + 1) % len(self._buckets )
def _A ( self : Dict , UpperCAmelCase_ : int , UpperCAmelCase_ : KEY , UpperCAmelCase_ : VAL ):
SCREAMING_SNAKE_CASE : Optional[int] = self._buckets[ind]
if not stored:
SCREAMING_SNAKE_CASE : Tuple = _Item(UpperCAmelCase_ , UpperCAmelCase_ )
self._len += 1
return True
elif stored.key == key:
SCREAMING_SNAKE_CASE : Tuple = _Item(UpperCAmelCase_ , UpperCAmelCase_ )
return True
else:
return False
def _A ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(UpperCAmelCase_ )
def _A ( self : Any ):
if len(self._buckets ) <= self._initial_block_size:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self : Union[str, Any] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = self._buckets
SCREAMING_SNAKE_CASE : List[Any] = [None] * new_size
SCREAMING_SNAKE_CASE : str = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self : Any ):
self._resize(len(self._buckets ) * 2 )
def _A ( self : Tuple ):
self._resize(len(self._buckets ) // 2 )
def _A ( self : Tuple , UpperCAmelCase_ : KEY ):
SCREAMING_SNAKE_CASE : Optional[Any] = self._get_bucket_index(UpperCAmelCase_ )
for _ in range(len(self._buckets ) ):
yield ind
SCREAMING_SNAKE_CASE : Optional[int] = self._get_next_ind(UpperCAmelCase_ )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : KEY , UpperCAmelCase_ : VAL ):
for ind in self._iterate_buckets(UpperCAmelCase_ ):
if self._try_set(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
break
def __setitem__( self : Optional[int] , UpperCAmelCase_ : KEY , UpperCAmelCase_ : VAL ):
if self._is_full():
self._size_up()
self._add_item(UpperCAmelCase_ , UpperCAmelCase_ )
def __delitem__( self : Union[str, Any] , UpperCAmelCase_ : KEY ):
for ind in self._iterate_buckets(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Optional[int] = self._buckets[ind]
if item is None:
raise KeyError(UpperCAmelCase_ )
if item is _deleted:
continue
if item.key == key:
SCREAMING_SNAKE_CASE : int = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : Optional[int] , UpperCAmelCase_ : KEY ):
for ind in self._iterate_buckets(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(UpperCAmelCase_ )
def __len__( self : Optional[int] ):
return self._len
def __iter__( self : Union[str, Any] ):
yield from (item.key for item in self._buckets if item)
def __repr__( self : List[Any] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = " ,".join(
f'''{item.key}: {item.val}''' for item in self._buckets if item )
return f'''HashMap({val_string})'''
| 319
|
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
snake_case = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = torch.load(lowercase , map_location="cpu" )
return sd
def lowerCamelCase__ ( lowercase , lowercase , lowercase=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = OrderedDict()
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE : Optional[Any] = key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE : Tuple = new_key.replace(name_pair[0] , name_pair[1] )
SCREAMING_SNAKE_CASE : Union[str, Any] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE : Union[str, Any] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE : str = "pretraining"
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE : str = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE : Optional[int] = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.''' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE : Optional[Any] = {"visual_embedding_dim": 512}
SCREAMING_SNAKE_CASE : Union[str, Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE : int = {"visual_embedding_dim": 2048}
SCREAMING_SNAKE_CASE : Any = "vqa_advanced"
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE : Any = {"visual_embedding_dim": 2048, "num_labels": 3129}
SCREAMING_SNAKE_CASE : Tuple = "vqa"
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE : int = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
SCREAMING_SNAKE_CASE : Union[str, Any] = "nlvr"
SCREAMING_SNAKE_CASE : List[Any] = VisualBertConfig(**lowercase )
# Load State Dict
SCREAMING_SNAKE_CASE : Union[str, Any] = load_state_dict(lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = get_new_dict(lowercase , lowercase )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE : Union[str, Any] = VisualBertForPreTraining(lowercase )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE : Optional[Any] = VisualBertForQuestionAnswering(lowercase )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE : Optional[Any] = VisualBertForVisualReasoning(lowercase )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE : List[Any] = VisualBertForMultipleChoice(lowercase )
model.load_state_dict(lowercase )
# Save Checkpoints
Path(lowercase ).mkdir(exist_ok=lowercase )
model.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
snake_case = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 319
| 1
|
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
snake_case = imread(r"""digital_image_processing/image_data/lena_small.jpg""")
snake_case = cvtColor(img, COLOR_BGR2GRAY)
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = cn.convert_to_negative(lowercase )
# assert negative_img array for at least one True
assert negative_img.any()
def lowerCamelCase__ ( ):
"""simple docstring"""
with Image.open("digital_image_processing/image_data/lena_small.jpg" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(lowercase , 110 ) ).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at" )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = imread("digital_image_processing/image_data/lena_small.jpg" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
SCREAMING_SNAKE_CASE : int = canny.canny(lowercase )
# assert canny array for at least one True
assert canny_array.any()
def lowerCamelCase__ ( ):
"""simple docstring"""
assert gg.gaussian_filter(lowercase , 5 , sigma=0.9 ).all()
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
SCREAMING_SNAKE_CASE : Tuple = conv.img_convolve(lowercase , lowercase ).astype(lowercase )
assert res.any()
def lowerCamelCase__ ( ):
"""simple docstring"""
assert med.median_filter(lowercase , 3 ).any()
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = sob.sobel_filter(lowercase )
assert grad.any() and theta.any()
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = sp.make_sepia(lowercase , 20 )
assert sepia.all()
def lowerCamelCase__ ( lowercase = "digital_image_processing/image_data/lena_small.jpg" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = bs.Burkes(imread(lowercase , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def lowerCamelCase__ ( lowercase = "digital_image_processing/image_data/lena_small.jpg" , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = rs.NearestNeighbour(imread(lowercase , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = "digital_image_processing/image_data/lena.jpg"
# Reading the image and converting it to grayscale.
SCREAMING_SNAKE_CASE : Dict = imread(lowercase , 0 )
# Test for get_neighbors_pixel function() return not None
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : List[str] = image[x_coordinate][y_coordinate]
SCREAMING_SNAKE_CASE : Optional[Any] = lbp.get_neighbors_pixel(
lowercase , lowercase , lowercase , lowercase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
SCREAMING_SNAKE_CASE : Union[str, Any] = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
SCREAMING_SNAKE_CASE : List[Any] = lbp.local_binary_value(lowercase , lowercase , lowercase )
assert lbp_image.any()
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from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''ClapFeatureExtractor'''
UpperCamelCase_ : Any = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : List[str]=None , **UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("sampling_rate" , UpperCAmelCase_ )
if text is None and audios is None:
raise ValueError("You have to specify either text or audios. Both cannot be none." )
if text is not None:
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if audios is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.feature_extractor(
UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None and audios is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase_ ) , tensor_type=UpperCAmelCase_ )
def _A ( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : str ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : List[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Any ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@property
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Any = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE : List[Any] = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 319
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|
import math
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = [True] * n
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : str = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
SCREAMING_SNAKE_CASE : str = i * 2
while index < n:
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CASE : str = index + i
SCREAMING_SNAKE_CASE : Any = [2]
for i in range(3 , lowercase , 2 ):
if is_prime[i]:
primes.append(lowercase )
return primes
def lowerCamelCase__ ( lowercase = 999966663333 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = math.floor(math.sqrt(lowercase ) ) + 100
SCREAMING_SNAKE_CASE : Union[str, Any] = prime_sieve(lowercase )
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : List[str] = primes[prime_index]
while (last_prime**2) <= limit:
SCREAMING_SNAKE_CASE : Any = primes[prime_index + 1]
SCREAMING_SNAKE_CASE : str = last_prime**2
SCREAMING_SNAKE_CASE : Any = next_prime**2
# Get numbers divisible by lps(current)
SCREAMING_SNAKE_CASE : Tuple = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
SCREAMING_SNAKE_CASE : Optional[int] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
SCREAMING_SNAKE_CASE : Dict = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
SCREAMING_SNAKE_CASE : List[str] = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
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|
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Any = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : Optional[int] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Any = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(lowercase , cache_dir=lowercase , split=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = parquet_path
elif issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [parquet_path]
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Tuple = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=("train",) ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
for split in splits:
SCREAMING_SNAKE_CASE : Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Dict = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(
{"train": parquet_path} , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[int] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Dict = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : str = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : Optional[Any] = ParquetDatasetReader({"train": parquet_path} , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if split:
SCREAMING_SNAKE_CASE : Any = {split: parquet_path}
else:
SCREAMING_SNAKE_CASE : Tuple = "train"
SCREAMING_SNAKE_CASE : int = {"train": parquet_path, "test": parquet_path}
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : str = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : int = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : Tuple = pq.ParquetFile(tmp_path / "foo.parquet" )
SCREAMING_SNAKE_CASE : List[Any] = pf.read()
assert dataset.data.table == output_table
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = str(shared_datadir / "test_image_rgb.jpg" )
SCREAMING_SNAKE_CASE : Union[str, Any] = {"image": [image_path]}
SCREAMING_SNAKE_CASE : Union[str, Any] = Features({"image": Image()} )
SCREAMING_SNAKE_CASE : int = Dataset.from_dict(lowercase , features=lowercase )
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : str = Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
assert dataset.features == reloaded_dataset.features
SCREAMING_SNAKE_CASE : Any = ParquetDatasetReader(str(tmp_path / "foo.parquet" ) , streaming=lowercase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32" )} ), None),
(Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert get_writer_batch_size(lowercase ) == expected
| 319
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|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
snake_case = """Create a default config file for Accelerate with only a few flags set."""
def lowerCamelCase__ ( lowercase="no" , lowercase = default_json_config_file , lowercase = False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = Path(lowercase )
path.parent.mkdir(parents=lowercase , exist_ok=lowercase )
if path.exists():
print(
F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.''' )
return False
SCREAMING_SNAKE_CASE : Any = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}''' )
SCREAMING_SNAKE_CASE : Any = {
"compute_environment": "LOCAL_MACHINE",
"mixed_precision": mixed_precision,
}
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE : Any = torch.cuda.device_count()
SCREAMING_SNAKE_CASE : Dict = num_gpus
SCREAMING_SNAKE_CASE : Dict = False
if num_gpus > 1:
SCREAMING_SNAKE_CASE : Any = "MULTI_GPU"
else:
SCREAMING_SNAKE_CASE : List[Any] = "NO"
elif is_xpu_available() and use_xpu:
SCREAMING_SNAKE_CASE : Optional[Any] = torch.xpu.device_count()
SCREAMING_SNAKE_CASE : List[str] = num_xpus
SCREAMING_SNAKE_CASE : List[str] = False
if num_xpus > 1:
SCREAMING_SNAKE_CASE : Tuple = "MULTI_XPU"
else:
SCREAMING_SNAKE_CASE : Optional[Any] = "NO"
elif is_npu_available():
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.npu.device_count()
SCREAMING_SNAKE_CASE : Optional[int] = num_npus
SCREAMING_SNAKE_CASE : Tuple = False
if num_npus > 1:
SCREAMING_SNAKE_CASE : List[str] = "MULTI_NPU"
else:
SCREAMING_SNAKE_CASE : Dict = "NO"
else:
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : Tuple = 1
SCREAMING_SNAKE_CASE : Optional[Any] = "NO"
SCREAMING_SNAKE_CASE : Any = ClusterConfig(**lowercase )
config.to_json_file(lowercase )
return path
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parser.add_parser("default" , parents=lowercase , help=lowercase , formatter_class=lowercase )
parser.add_argument(
"--config_file" , default=lowercase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , dest="save_location" , )
parser.add_argument(
"--mixed_precision" , choices=["no", "fp16", "bf16"] , type=lowercase , help="Whether or not to use mixed precision training. "
"Choose between FP16 and BF16 (bfloat16) training. "
"BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later." , default="no" , )
parser.set_defaults(func=lowercase )
return parser
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F'''accelerate configuration saved at {config_file}''' )
| 319
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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| 1
|
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , lowercase=True , lowercase="pt" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = {"add_prefix_space": True} if isinstance(lowercase , lowercase ) and not line.startswith(" " ) else {}
SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase , padding="max_length" if pad_to_max_length else None , truncation=lowercase , return_tensors=lowercase , add_special_tokens=lowercase , **lowercase , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = input_ids.ne(lowercase ).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 SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Dict="train" , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : str=None , UpperCAmelCase_ : List[str]="" , ):
super().__init__()
SCREAMING_SNAKE_CASE : List[Any] = Path(UpperCAmelCase_ ).joinpath(type_path + ".source" )
SCREAMING_SNAKE_CASE : Tuple = Path(UpperCAmelCase_ ).joinpath(type_path + ".target" )
SCREAMING_SNAKE_CASE : Dict = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE : Tuple = max_source_length
SCREAMING_SNAKE_CASE : Union[str, Any] = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer
SCREAMING_SNAKE_CASE : List[str] = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE : int = src_lang
SCREAMING_SNAKE_CASE : List[Any] = tgt_lang
def __len__( self : Any ):
return len(self.src_lens )
def __getitem__( self : Tuple , UpperCAmelCase_ : Dict ):
SCREAMING_SNAKE_CASE : str = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE : Tuple = self.prefix + linecache.getline(str(self.src_file ) , UpperCAmelCase_ ).rstrip("\n" )
SCREAMING_SNAKE_CASE : int = 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
SCREAMING_SNAKE_CASE : Tuple = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , UpperCAmelCase_ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer.generator if isinstance(self.tokenizer , UpperCAmelCase_ ) else self.tokenizer
SCREAMING_SNAKE_CASE : Any = encode_line(UpperCAmelCase_ , UpperCAmelCase_ , self.max_source_length , "right" )
SCREAMING_SNAKE_CASE : Dict = encode_line(UpperCAmelCase_ , UpperCAmelCase_ , self.max_target_length , "right" )
SCREAMING_SNAKE_CASE : Optional[Any] = source_inputs["input_ids"].squeeze()
SCREAMING_SNAKE_CASE : int = target_inputs["input_ids"].squeeze()
SCREAMING_SNAKE_CASE : int = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _A ( UpperCAmelCase_ : Optional[Any] ):
return [len(UpperCAmelCase_ ) for x in Path(UpperCAmelCase_ ).open().readlines()]
def _A ( self : int , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : Optional[int] = torch.stack([x["input_ids"] for x in batch] )
SCREAMING_SNAKE_CASE : str = torch.stack([x["attention_mask"] for x in batch] )
SCREAMING_SNAKE_CASE : Tuple = torch.stack([x["decoder_input_ids"] for x in batch] )
SCREAMING_SNAKE_CASE : List[Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , UpperCAmelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : str = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , UpperCAmelCase_ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE : Any = trim_batch(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = trim_batch(UpperCAmelCase_ , UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
snake_case = getLogger(__name__)
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return list(itertools.chain.from_iterable(lowercase ) )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = get_git_info()
save_json(lowercase , os.path.join(lowercase , "git_log.json" ) )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=4 , **lowercase ):
"""simple docstring"""
with open(lowercase , "w" ) as f:
json.dump(lowercase , lowercase , indent=lowercase , **lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
with open(lowercase ) as f:
return json.load(lowercase )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase )
SCREAMING_SNAKE_CASE : List[str] = {
"repo_id": str(lowercase ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return list(map(lowercase , lowercase ) )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
with open(lowercase , "wb" ) as f:
return pickle.dump(lowercase , lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
def remove_articles(lowercase ):
return re.sub(R"\b(a|an|the)\b" , " " , lowercase )
def white_space_fix(lowercase ):
return " ".join(text.split() )
def remove_punc(lowercase ):
SCREAMING_SNAKE_CASE : Any = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase ) ) ) )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = normalize_answer(lowercase ).split()
SCREAMING_SNAKE_CASE : int = normalize_answer(lowercase ).split()
SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase ) & Counter(lowercase )
SCREAMING_SNAKE_CASE : Dict = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE : Tuple = 1.0 * num_same / len(lowercase )
SCREAMING_SNAKE_CASE : str = 1.0 * num_same / len(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return normalize_answer(lowercase ) == normalize_answer(lowercase )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert len(lowercase ) == len(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for hypo, pred in zip(lowercase , lowercase ):
em += exact_match_score(lowercase , lowercase )
if len(lowercase ) > 0:
em /= len(lowercase )
return {"em": em}
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return model_prefix.startswith("rag" )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE : Optional[Any] = "dropout_rate"
for p in extra_params:
if getattr(lowercase , lowercase , lowercase ):
if not hasattr(lowercase , lowercase ) and not hasattr(lowercase , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(lowercase ) )
delattr(lowercase , lowercase )
continue
SCREAMING_SNAKE_CASE : Any = p if hasattr(lowercase , lowercase ) else equivalent_param[p]
setattr(lowercase , lowercase , getattr(lowercase , lowercase ) )
delattr(lowercase , lowercase )
return hparams, config
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def lowerCamelCase__ ( lowercase , lowercase = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = length or len(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = list_data[i + 1], list_data[i]
SCREAMING_SNAKE_CASE : str = True
return list_data if not swapped else bubble_sort(lowercase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
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import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _A ( self : str ):
SCREAMING_SNAKE_CASE : int = inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE : str = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
SCREAMING_SNAKE_CASE : List[str] = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def _A ( self : int ):
SCREAMING_SNAKE_CASE : int = f'''
{self.test_dir}/xla_spawn.py
--num_cores 8
{self.test_file_path}
'''.split()
SCREAMING_SNAKE_CASE : List[str] = [sys.executable] + distributed_args
execute_subprocess_async(UpperCAmelCase_ , env=os.environ.copy() )
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import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case = get_logger(__name__)
snake_case = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : str , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[int] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ):
for processor in self:
SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(processor.__call__ ).parameters
if len(UpperCAmelCase_ ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
f'''Make sure that all the required parameters: {list(function_args.keys() )} for '''
f'''{processor.__class__} are passed to the logits processor.''' )
SCREAMING_SNAKE_CASE : int = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , **UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : Dict = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : float ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not (temperature > 0):
raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}''' )
SCREAMING_SNAKE_CASE : Optional[int] = temperature
def __call__( self : List[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = scores / self.temperature
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : float , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (top_p < 0 or top_p > 1.0):
raise ValueError(f'''`top_p` has to be a float > 0 and < 1, but is {top_p}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (min_tokens_to_keep < 1):
raise ValueError(f'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}''' )
SCREAMING_SNAKE_CASE : Optional[int] = top_p
SCREAMING_SNAKE_CASE : str = filter_value
SCREAMING_SNAKE_CASE : List[str] = min_tokens_to_keep
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = lax.top_k(UpperCAmelCase_ , scores.shape[-1] )
SCREAMING_SNAKE_CASE : str = jnp.full_like(UpperCAmelCase_ , self.filter_value )
SCREAMING_SNAKE_CASE : Optional[int] = jax.nn.softmax(UpperCAmelCase_ , axis=-1 ).cumsum(axis=-1 )
SCREAMING_SNAKE_CASE : Tuple = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
SCREAMING_SNAKE_CASE : Optional[int] = jnp.roll(UpperCAmelCase_ , 1 )
score_mask |= score_mask.at[:, 0].set(UpperCAmelCase_ )
# min tokens to keep
SCREAMING_SNAKE_CASE : Union[str, Any] = score_mask.at[:, : self.min_tokens_to_keep].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = jnp.where(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jax.lax.sort_key_val(UpperCAmelCase_ , UpperCAmelCase_ )[-1]
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or top_k <= 0:
raise ValueError(f'''`top_k` has to be a strictly positive integer, but is {top_k}''' )
SCREAMING_SNAKE_CASE : List[str] = max(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = filter_value
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = scores.shape
SCREAMING_SNAKE_CASE : List[str] = jnp.full(batch_size * vocab_size , self.filter_value )
SCREAMING_SNAKE_CASE : List[str] = min(self.top_k , scores.shape[-1] ) # Safety check
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = lax.top_k(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = jnp.broadcast_to((jnp.arange(UpperCAmelCase_ ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
SCREAMING_SNAKE_CASE : List[str] = topk_scores.flatten()
SCREAMING_SNAKE_CASE : List[Any] = topk_indices.flatten() + shift
SCREAMING_SNAKE_CASE : Dict = next_scores_flat.at[topk_indices_flat].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = next_scores_flat.reshape(UpperCAmelCase_ , UpperCAmelCase_ )
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = bos_token_id
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.bool_(cur_len - 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.bos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = max_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : List[str] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : str = 1 - jnp.bool_(cur_len - self.max_length + 1 )
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.eos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or min_length < 0:
raise ValueError(f'''`min_length` has to be a positive integer, but is {min_length}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or eos_token_id < 0:
raise ValueError(f'''`eos_token_id` has to be a positive integer, but is {eos_token_id}''' )
SCREAMING_SNAKE_CASE : List[str] = min_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
# create boolean flag to decide if min length penalty should be applied
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
SCREAMING_SNAKE_CASE : Optional[int] = jnp.where(UpperCAmelCase_ , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = list(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = begin_index
def __call__( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Union[str, Any] = 1 - jnp.bool_(cur_len - self.begin_index )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(UpperCAmelCase_ , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : List[Any] = list(UpperCAmelCase_ )
def __call__( self : Any , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Tuple = scores.at[..., self.suppress_tokens].set(-float("inf" ) )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : List[Any] = dict(UpperCAmelCase_ )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
SCREAMING_SNAKE_CASE : Any = force_token_array.at[index].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = jnp.intaa(UpperCAmelCase_ )
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
def _force_token(UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : List[str] = scores.shape[0]
SCREAMING_SNAKE_CASE : Optional[int] = self.force_token_array[generation_idx]
SCREAMING_SNAKE_CASE : Tuple = jnp.ones_like(UpperCAmelCase_ , dtype=scores.dtype ) * -float("inf" )
SCREAMING_SNAKE_CASE : Dict = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
SCREAMING_SNAKE_CASE : Optional[Any] = lax.dynamic_update_slice(UpperCAmelCase_ , UpperCAmelCase_ , (0, current_token) )
return new_scores
SCREAMING_SNAKE_CASE : Any = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(UpperCAmelCase_ ) , lambda: scores , ) , )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Union[str, Any] = generate_config.eos_token_id
SCREAMING_SNAKE_CASE : Tuple = generate_config.no_timestamps_token_id
SCREAMING_SNAKE_CASE : List[Any] = generate_config.no_timestamps_token_id + 1
SCREAMING_SNAKE_CASE : Dict = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(UpperCAmelCase_ , "max_initial_timestamp_index" ):
SCREAMING_SNAKE_CASE : List[Any] = generate_config.max_initial_timestamp_index
else:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
if self.max_initial_timestamp_index is None:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
def __call__( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
# suppress <|notimestamps|> which is handled by without_timestamps
SCREAMING_SNAKE_CASE : int = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) )
def handle_pairs(UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) >= 1 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) < 2 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , UpperCAmelCase_ , UpperCAmelCase_ , )
return jnp.where(
UpperCAmelCase_ , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Optional[Any] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(cur_len == self.begin_index , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = self.timestamp_begin + self.max_initial_timestamp_index
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(
UpperCAmelCase_ , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , UpperCAmelCase_ , )
# if sum of probability over timestamps is above any other token, sample timestamp
SCREAMING_SNAKE_CASE : List[Any] = jax.nn.log_softmax(UpperCAmelCase_ , axis=-1 )
def handle_cumulative_probs(UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
return scores
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# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
snake_case = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
print("""Torch version:""", None)
try:
import transformers
print("""transformers version:""", transformers.__version__)
except ImportError:
print("""transformers version:""", None)
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| 1
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from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any]=0.0 , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : str = "geglu" , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : str = "layer_norm" , UpperCAmelCase_ : bool = False , ):
super().__init__()
SCREAMING_SNAKE_CASE : int = only_cross_attention
SCREAMING_SNAKE_CASE : Union[str, Any] = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
SCREAMING_SNAKE_CASE : Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.''' )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
SCREAMING_SNAKE_CASE : Any = AdaLayerNorm(UpperCAmelCase_ , UpperCAmelCase_ )
elif self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE : Dict = AdaLayerNormZero(UpperCAmelCase_ , UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : int = nn.LayerNorm(UpperCAmelCase_ , elementwise_affine=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = Attention(
query_dim=UpperCAmelCase_ , heads=UpperCAmelCase_ , dim_head=UpperCAmelCase_ , dropout=UpperCAmelCase_ , bias=UpperCAmelCase_ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=UpperCAmelCase_ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
SCREAMING_SNAKE_CASE : List[str] = (
AdaLayerNorm(UpperCAmelCase_ , UpperCAmelCase_ )
if self.use_ada_layer_norm
else nn.LayerNorm(UpperCAmelCase_ , elementwise_affine=UpperCAmelCase_ )
)
SCREAMING_SNAKE_CASE : int = Attention(
query_dim=UpperCAmelCase_ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=UpperCAmelCase_ , dim_head=UpperCAmelCase_ , dropout=UpperCAmelCase_ , bias=UpperCAmelCase_ , upcast_attention=UpperCAmelCase_ , ) # is self-attn if encoder_hidden_states is none
else:
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
# 3. Feed-forward
SCREAMING_SNAKE_CASE : Any = nn.LayerNorm(UpperCAmelCase_ , elementwise_affine=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = FeedForward(UpperCAmelCase_ , dropout=UpperCAmelCase_ , activation_fn=UpperCAmelCase_ , final_dropout=UpperCAmelCase_ )
# let chunk size default to None
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[str] = 0
def _A ( self : Any , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int ):
# Sets chunk feed-forward
SCREAMING_SNAKE_CASE : Union[str, Any] = chunk_size
SCREAMING_SNAKE_CASE : Tuple = dim
def _A ( self : str , UpperCAmelCase_ : torch.FloatTensor , UpperCAmelCase_ : Optional[torch.FloatTensor] = None , UpperCAmelCase_ : Optional[torch.FloatTensor] = None , UpperCAmelCase_ : Optional[torch.FloatTensor] = None , UpperCAmelCase_ : Optional[torch.LongTensor] = None , UpperCAmelCase_ : Dict[str, Any] = None , UpperCAmelCase_ : Optional[torch.LongTensor] = None , ):
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
SCREAMING_SNAKE_CASE : str = self.norma(UpperCAmelCase_ , UpperCAmelCase_ )
elif self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = self.norma(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , hidden_dtype=hidden_states.dtype )
else:
SCREAMING_SNAKE_CASE : Dict = self.norma(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
SCREAMING_SNAKE_CASE : str = self.attna(
UpperCAmelCase_ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=UpperCAmelCase_ , **UpperCAmelCase_ , )
if self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE : Tuple = gate_msa.unsqueeze(1 ) * attn_output
SCREAMING_SNAKE_CASE : int = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = (
self.norma(UpperCAmelCase_ , UpperCAmelCase_ ) if self.use_ada_layer_norm else self.norma(UpperCAmelCase_ )
)
SCREAMING_SNAKE_CASE : Optional[Any] = self.attna(
UpperCAmelCase_ , encoder_hidden_states=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Optional[Any] = attn_output + hidden_states
# 3. Feed-forward
SCREAMING_SNAKE_CASE : Dict = self.norma(UpperCAmelCase_ )
if self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE : Any = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.''' )
SCREAMING_SNAKE_CASE : List[str] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
SCREAMING_SNAKE_CASE : int = torch.cat(
[self.ff(UpperCAmelCase_ ) for hid_slice in norm_hidden_states.chunk(UpperCAmelCase_ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
SCREAMING_SNAKE_CASE : int = self.ff(UpperCAmelCase_ )
if self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE : List[Any] = gate_mlp.unsqueeze(1 ) * ff_output
SCREAMING_SNAKE_CASE : Union[str, Any] = ff_output + hidden_states
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : int = 4 , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : str = "geglu" , UpperCAmelCase_ : bool = False , ):
super().__init__()
SCREAMING_SNAKE_CASE : Optional[int] = int(dim * mult )
SCREAMING_SNAKE_CASE : str = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
SCREAMING_SNAKE_CASE : List[str] = GELU(UpperCAmelCase_ , UpperCAmelCase_ )
if activation_fn == "gelu-approximate":
SCREAMING_SNAKE_CASE : Dict = GELU(UpperCAmelCase_ , UpperCAmelCase_ , approximate="tanh" )
elif activation_fn == "geglu":
SCREAMING_SNAKE_CASE : Tuple = GEGLU(UpperCAmelCase_ , UpperCAmelCase_ )
elif activation_fn == "geglu-approximate":
SCREAMING_SNAKE_CASE : str = ApproximateGELU(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = nn.ModuleList([] )
# project in
self.net.append(UpperCAmelCase_ )
# project dropout
self.net.append(nn.Dropout(UpperCAmelCase_ ) )
# project out
self.net.append(nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(UpperCAmelCase_ ) )
def _A ( self : Optional[int] , UpperCAmelCase_ : Dict ):
for module in self.net:
SCREAMING_SNAKE_CASE : Optional[Any] = module(UpperCAmelCase_ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : str = "none" ):
super().__init__()
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = approximate
def _A ( self : List[str] , UpperCAmelCase_ : Any ):
if gate.device.type != "mps":
return F.gelu(UpperCAmelCase_ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _A ( self : Optional[Any] , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : List[str] = self.proj(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = self.gelu(UpperCAmelCase_ )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
super().__init__()
SCREAMING_SNAKE_CASE : Any = nn.Linear(UpperCAmelCase_ , dim_out * 2 )
def _A ( self : Optional[Any] , UpperCAmelCase_ : Any ):
if gate.device.type != "mps":
return F.gelu(UpperCAmelCase_ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _A ( self : Any , UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = self.proj(UpperCAmelCase_ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(UpperCAmelCase_ )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
super().__init__()
SCREAMING_SNAKE_CASE : Any = nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Dict , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : str = self.proj(UpperCAmelCase_ )
return x * torch.sigmoid(1.702 * x )
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str ):
super().__init__()
SCREAMING_SNAKE_CASE : Optional[int] = nn.Embedding(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = nn.SiLU()
SCREAMING_SNAKE_CASE : Dict = nn.Linear(UpperCAmelCase_ , embedding_dim * 2 )
SCREAMING_SNAKE_CASE : List[Any] = nn.LayerNorm(UpperCAmelCase_ , elementwise_affine=UpperCAmelCase_ )
def _A ( self : Optional[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : List[Any] = self.linear(self.silu(self.emb(UpperCAmelCase_ ) ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = torch.chunk(UpperCAmelCase_ , 2 )
SCREAMING_SNAKE_CASE : Tuple = self.norm(UpperCAmelCase_ ) * (1 + scale) + shift
return x
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Any ):
super().__init__()
SCREAMING_SNAKE_CASE : int = CombinedTimestepLabelEmbeddings(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = nn.SiLU()
SCREAMING_SNAKE_CASE : Optional[int] = nn.Linear(UpperCAmelCase_ , 6 * embedding_dim , bias=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = nn.LayerNorm(UpperCAmelCase_ , elementwise_affine=UpperCAmelCase_ , eps=1E-6 )
def _A ( self : Optional[int] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int=None ):
SCREAMING_SNAKE_CASE : Tuple = self.linear(self.silu(self.emb(UpperCAmelCase_ , UpperCAmelCase_ , hidden_dtype=UpperCAmelCase_ ) ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = emb.chunk(6 , dim=1 )
SCREAMING_SNAKE_CASE : List[str] = self.norm(UpperCAmelCase_ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[str] = None , UpperCAmelCase_ : float = 1E-5 ):
super().__init__()
SCREAMING_SNAKE_CASE : str = num_groups
SCREAMING_SNAKE_CASE : Optional[Any] = eps
if act_fn is None:
SCREAMING_SNAKE_CASE : Optional[int] = None
else:
SCREAMING_SNAKE_CASE : Any = get_activation(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = nn.Linear(UpperCAmelCase_ , out_dim * 2 )
def _A ( self : Optional[int] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : int ):
if self.act:
SCREAMING_SNAKE_CASE : str = self.act(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = self.linear(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = emb[:, :, None, None]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = emb.chunk(2 , dim=1 )
SCREAMING_SNAKE_CASE : str = F.group_norm(UpperCAmelCase_ , self.num_groups , eps=self.eps )
SCREAMING_SNAKE_CASE : int = x * (1 + scale) + shift
return x
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# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
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import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = args.pruning_method
SCREAMING_SNAKE_CASE : str = args.threshold
SCREAMING_SNAKE_CASE : int = args.model_name_or_path.rstrip("/" )
SCREAMING_SNAKE_CASE : Tuple = args.target_model_path
print(F'''Load fine-pruned model from {model_name_or_path}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.load(os.path.join(lowercase , "pytorch_model.bin" ) )
SCREAMING_SNAKE_CASE : Tuple = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
SCREAMING_SNAKE_CASE : int = tensor
print(F'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
SCREAMING_SNAKE_CASE : Optional[Any] = tensor
print(F'''Copied layer {name}''' )
elif "bias" in name:
SCREAMING_SNAKE_CASE : List[Any] = tensor
print(F'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
SCREAMING_SNAKE_CASE : Tuple = MagnitudeBinarizer.apply(inputs=lowercase , threshold=lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
SCREAMING_SNAKE_CASE : int = name[:-6]
SCREAMING_SNAKE_CASE : int = model[F'''{prefix_}mask_scores''']
SCREAMING_SNAKE_CASE : List[Any] = TopKBinarizer.apply(lowercase , lowercase )
SCREAMING_SNAKE_CASE : List[Any] = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
SCREAMING_SNAKE_CASE : List[str] = name[:-6]
SCREAMING_SNAKE_CASE : Tuple = model[F'''{prefix_}mask_scores''']
SCREAMING_SNAKE_CASE : List[str] = ThresholdBinarizer.apply(lowercase , lowercase , lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
SCREAMING_SNAKE_CASE : Any = name[:-6]
SCREAMING_SNAKE_CASE : Any = model[F'''{prefix_}mask_scores''']
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = -0.1, 1.1
SCREAMING_SNAKE_CASE : List[Any] = torch.sigmoid(lowercase )
SCREAMING_SNAKE_CASE : str = s * (r - l) + l
SCREAMING_SNAKE_CASE : Optional[Any] = s_bar.clamp(min=0.0 , max=1.0 )
SCREAMING_SNAKE_CASE : Dict = tensor * mask
print(F'''Pruned layer {name}''' )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
SCREAMING_SNAKE_CASE : int = os.path.join(
os.path.dirname(lowercase ) , F'''bertarized_{os.path.basename(lowercase )}''' )
if not os.path.isdir(lowercase ):
shutil.copytree(lowercase , lowercase )
print(F'''\nCreated folder {target_model_path}''' )
torch.save(lowercase , os.path.join(lowercase , "pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--pruning_method""",
choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
type=str,
required=True,
help=(
"""Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
""" sigmoied_threshold = Soft movement pruning)"""
),
)
parser.add_argument(
"""--threshold""",
type=float,
required=False,
help=(
"""For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
"""For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
"""Not needed for `l0`"""
),
)
parser.add_argument(
"""--model_name_or_path""",
type=str,
required=True,
help="""Folder containing the model that was previously fine-pruned""",
)
parser.add_argument(
"""--target_model_path""",
default=None,
type=str,
required=False,
help="""Folder containing the model that was previously fine-pruned""",
)
snake_case = parser.parse_args()
main(args)
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|
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
snake_case = {
"""b0""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1_408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1_536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1_792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2_304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2_560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = EfficientNetConfig()
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["hidden_dim"]
SCREAMING_SNAKE_CASE : Tuple = CONFIG_MAP[model_name]["width_coef"]
SCREAMING_SNAKE_CASE : Optional[int] = CONFIG_MAP[model_name]["depth_coef"]
SCREAMING_SNAKE_CASE : Union[str, Any] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = CONFIG_MAP[model_name]["dropout_rate"]
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["dw_padding"]
SCREAMING_SNAKE_CASE : str = "huggingface/label-files"
SCREAMING_SNAKE_CASE : str = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE : str = 1000
SCREAMING_SNAKE_CASE : List[Any] = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE : Tuple = {int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Union[str, Any] = idalabel
SCREAMING_SNAKE_CASE : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE : List[Any] = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : int = EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=lowercase , )
return preprocessor
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
SCREAMING_SNAKE_CASE : List[str] = sorted(set(lowercase ) )
SCREAMING_SNAKE_CASE : List[str] = len(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = {b: str(lowercase ) for b, i in zip(lowercase , range(lowercase ) )}
SCREAMING_SNAKE_CASE : Dict = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
SCREAMING_SNAKE_CASE : Tuple = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
SCREAMING_SNAKE_CASE : int = {}
for item in rename_keys:
if item[0] in original_param_names:
SCREAMING_SNAKE_CASE : Any = "efficientnet." + item[1]
SCREAMING_SNAKE_CASE : Optional[Any] = "classifier.weight"
SCREAMING_SNAKE_CASE : List[str] = "classifier.bias"
return key_mapping
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
SCREAMING_SNAKE_CASE : str = key_mapping[key]
if "_conv" in key and "kernel" in key:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
SCREAMING_SNAKE_CASE : int = torch.from_numpy(lowercase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
SCREAMING_SNAKE_CASE : List[str] = torch.from_numpy(np.transpose(lowercase ) )
else:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(lowercase )
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = model_classes[model_name](
include_top=lowercase , weights="imagenet" , input_tensor=lowercase , input_shape=lowercase , pooling=lowercase , classes=1000 , classifier_activation="softmax" , )
SCREAMING_SNAKE_CASE : List[Any] = original_model.trainable_variables
SCREAMING_SNAKE_CASE : Dict = original_model.non_trainable_variables
SCREAMING_SNAKE_CASE : Dict = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
SCREAMING_SNAKE_CASE : Tuple = param.numpy()
SCREAMING_SNAKE_CASE : Tuple = list(tf_params.keys() )
# Load HuggingFace model
SCREAMING_SNAKE_CASE : Tuple = get_efficientnet_config(lowercase )
SCREAMING_SNAKE_CASE : str = EfficientNetForImageClassification(lowercase ).eval()
SCREAMING_SNAKE_CASE : Dict = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
SCREAMING_SNAKE_CASE : Dict = rename_keys(lowercase )
replace_params(lowercase , lowercase , lowercase )
# Initialize preprocessor and preprocess input image
SCREAMING_SNAKE_CASE : Optional[int] = convert_image_processor(lowercase )
SCREAMING_SNAKE_CASE : int = preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = hf_model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.logits.detach().numpy()
# Original model inference
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
SCREAMING_SNAKE_CASE : Tuple = image.img_to_array(lowercase )
SCREAMING_SNAKE_CASE : Tuple = np.expand_dims(lowercase , axis=0 )
SCREAMING_SNAKE_CASE : Any = original_model.predict(lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(lowercase , lowercase , atol=1E-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(lowercase ):
os.mkdir(lowercase )
# Save converted model and image processor
hf_model.save_pretrained(lowercase )
preprocessor.save_pretrained(lowercase )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(lowercase )
hf_model.push_to_hub(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
snake_case = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
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import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Union[str, Any] ):
warnings.warn(
"The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use BeitImageProcessor instead." , UpperCAmelCase_ , )
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_ )
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|
def lowerCamelCase__ ( ):
"""simple docstring"""
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
snake_case = generate_large_matrix()
snake_case = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
assert all(row == sorted(lowercase , reverse=lowercase ) for row in grid )
assert all(list(lowercase ) == sorted(lowercase , reverse=lowercase ) for col in zip(*lowercase ) )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowercase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
SCREAMING_SNAKE_CASE : List[Any] = (left + right) // 2
SCREAMING_SNAKE_CASE : Optional[int] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
SCREAMING_SNAKE_CASE : List[Any] = mid + 1
else:
SCREAMING_SNAKE_CASE : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : List[str] = len(grid[0] )
for i in range(len(lowercase ) ):
SCREAMING_SNAKE_CASE : Any = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowercase ) * len(grid[0] )) - total
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return len([number for row in grid for number in row if number < 0] )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = 0
for row in grid:
for i, number in enumerate(lowercase ):
if number < 0:
total += len(lowercase ) - i
break
return total
def lowerCamelCase__ ( ):
"""simple docstring"""
from timeit import timeit
print("Running benchmarks" )
SCREAMING_SNAKE_CASE : List[str] = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
SCREAMING_SNAKE_CASE : Union[str, Any] = timeit(F'''{func}(grid=grid)''' , setup=lowercase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
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| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
"""YituTech/conv-bert-medium-small""": (
"""https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
),
"""YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[Any] = '''convbert'''
def __init__( self : Any , UpperCAmelCase_ : List[Any]=3_0522 , UpperCAmelCase_ : Optional[int]=768 , UpperCAmelCase_ : Optional[Any]=12 , UpperCAmelCase_ : List[Any]=12 , UpperCAmelCase_ : Dict=3072 , UpperCAmelCase_ : List[str]="gelu" , UpperCAmelCase_ : Union[str, Any]=0.1 , UpperCAmelCase_ : Tuple=0.1 , UpperCAmelCase_ : List[str]=512 , UpperCAmelCase_ : Union[str, Any]=2 , UpperCAmelCase_ : Dict=0.02 , UpperCAmelCase_ : Optional[Any]=1E-12 , UpperCAmelCase_ : List[str]=1 , UpperCAmelCase_ : Dict=0 , UpperCAmelCase_ : List[Any]=2 , UpperCAmelCase_ : Optional[int]=768 , UpperCAmelCase_ : int=2 , UpperCAmelCase_ : Optional[int]=9 , UpperCAmelCase_ : Any=1 , UpperCAmelCase_ : Optional[int]=None , **UpperCAmelCase_ : int , ):
super().__init__(
pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = intermediate_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE : int = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
SCREAMING_SNAKE_CASE : Any = initializer_range
SCREAMING_SNAKE_CASE : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE : str = embedding_size
SCREAMING_SNAKE_CASE : Optional[int] = head_ratio
SCREAMING_SNAKE_CASE : List[Any] = conv_kernel_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_groups
SCREAMING_SNAKE_CASE : int = classifier_dropout
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@property
def _A ( self : List[Any] ):
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[int] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
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|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case = ["""small""", """medium""", """large"""]
snake_case = """lm_head.decoder.weight"""
snake_case = """lm_head.weight"""
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = torch.load(lowercase )
SCREAMING_SNAKE_CASE : Any = d.pop(lowercase )
os.makedirs(lowercase , exist_ok=lowercase )
torch.save(lowercase , os.path.join(lowercase , lowercase ) )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
snake_case = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
snake_case = os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
snake_case = F"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
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|
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case = """src/transformers"""
snake_case = """docs/source/en"""
snake_case = """."""
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
with open(lowercase , "r" , encoding="utf-8" , newline="\n" ) as f:
SCREAMING_SNAKE_CASE : int = f.readlines()
# Find the start prompt.
SCREAMING_SNAKE_CASE : Dict = 0
while not lines[start_index].startswith(lowercase ):
start_index += 1
start_index += 1
SCREAMING_SNAKE_CASE : List[str] = start_index
while not lines[end_index].startswith(lowercase ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
snake_case = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
snake_case = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
snake_case = re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case = re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
snake_case = direct_transformers_import(TRANSFORMERS_PATH)
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" , lowercase )
return [m.group(0 ) for m in matches]
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = 2 if text == "✅" or text == "❌" else len(lowercase )
SCREAMING_SNAKE_CASE : List[str] = (width - text_length) // 2
SCREAMING_SNAKE_CASE : Union[str, Any] = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE : Dict = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
SCREAMING_SNAKE_CASE : Dict = {name: config.replace("Config" , "" ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
SCREAMING_SNAKE_CASE : Union[str, Any] = collections.defaultdict(lowercase )
SCREAMING_SNAKE_CASE : List[Any] = collections.defaultdict(lowercase )
SCREAMING_SNAKE_CASE : Tuple = collections.defaultdict(lowercase )
SCREAMING_SNAKE_CASE : List[Any] = collections.defaultdict(lowercase )
SCREAMING_SNAKE_CASE : List[str] = collections.defaultdict(lowercase )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = None
if attr_name.endswith("Tokenizer" ):
SCREAMING_SNAKE_CASE : str = slow_tokenizers
SCREAMING_SNAKE_CASE : Any = attr_name[:-9]
elif attr_name.endswith("TokenizerFast" ):
SCREAMING_SNAKE_CASE : Tuple = fast_tokenizers
SCREAMING_SNAKE_CASE : Tuple = attr_name[:-13]
elif _re_tf_models.match(lowercase ) is not None:
SCREAMING_SNAKE_CASE : Any = tf_models
SCREAMING_SNAKE_CASE : int = _re_tf_models.match(lowercase ).groups()[0]
elif _re_flax_models.match(lowercase ) is not None:
SCREAMING_SNAKE_CASE : Tuple = flax_models
SCREAMING_SNAKE_CASE : str = _re_flax_models.match(lowercase ).groups()[0]
elif _re_pt_models.match(lowercase ) is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = pt_models
SCREAMING_SNAKE_CASE : Optional[Any] = _re_pt_models.match(lowercase ).groups()[0]
if lookup_dict is not None:
while len(lowercase ) > 0:
if attr_name in model_name_to_prefix.values():
SCREAMING_SNAKE_CASE : Dict = True
break
# Try again after removing the last word in the name
SCREAMING_SNAKE_CASE : str = "".join(camel_case_split(lowercase )[:-1] )
# Let's build that table!
SCREAMING_SNAKE_CASE : Optional[Any] = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
SCREAMING_SNAKE_CASE : Optional[Any] = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
SCREAMING_SNAKE_CASE : Any = [len(lowercase ) + 2 for c in columns]
SCREAMING_SNAKE_CASE : Optional[int] = max([len(lowercase ) for name in model_names] ) + 2
# Build the table per se
SCREAMING_SNAKE_CASE : str = "|" + "|".join([_center_text(lowercase , lowercase ) for c, w in zip(lowercase , lowercase )] ) + "|\n"
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths] ) + "|\n"
SCREAMING_SNAKE_CASE : List[Any] = {True: "✅", False: "❌"}
for name in model_names:
SCREAMING_SNAKE_CASE : List[str] = model_name_to_prefix[name]
SCREAMING_SNAKE_CASE : int = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(lowercase , lowercase ) for l, w in zip(lowercase , lowercase )] ) + "|\n"
return table
def lowerCamelCase__ ( lowercase=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = _find_text_in_file(
filename=os.path.join(lowercase , "index.md" ) , start_prompt="<!--This table is updated automatically from the auto modules" , end_prompt="<!-- End table-->" , )
SCREAMING_SNAKE_CASE : List[str] = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowercase , "index.md" ) , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
"The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this." )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
snake_case = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 319
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""MLukeTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case = {
"""configuration_clip""": [
"""CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPConfig""",
"""CLIPOnnxConfig""",
"""CLIPTextConfig""",
"""CLIPVisionConfig""",
],
"""processing_clip""": ["""CLIPProcessor"""],
"""tokenization_clip""": ["""CLIPTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""CLIPTokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""CLIPFeatureExtractor"""]
snake_case = ["""CLIPImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPModel""",
"""CLIPPreTrainedModel""",
"""CLIPTextModel""",
"""CLIPTextModelWithProjection""",
"""CLIPVisionModel""",
"""CLIPVisionModelWithProjection""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCLIPModel""",
"""TFCLIPPreTrainedModel""",
"""TFCLIPTextModel""",
"""TFCLIPVisionModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FlaxCLIPModel""",
"""FlaxCLIPPreTrainedModel""",
"""FlaxCLIPTextModel""",
"""FlaxCLIPTextPreTrainedModel""",
"""FlaxCLIPVisionModel""",
"""FlaxCLIPVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
|
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase__ ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 319
| 1
|
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
snake_case = """pt"""
elif is_tf_available():
snake_case = """tf"""
else:
snake_case = """jax"""
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : str = ByTaTokenizer
UpperCamelCase_ : str = False
def _A ( self : Tuple ):
super().setUp()
SCREAMING_SNAKE_CASE : Tuple = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _A ( self : Tuple ):
return ByTaTokenizer.from_pretrained("google/byt5-small" )
def _A ( self : str , **UpperCAmelCase_ : List[Any] ):
return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def _A ( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : Any=False , UpperCAmelCase_ : int=20 , UpperCAmelCase_ : List[str]=5 ):
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for ByT5 because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
SCREAMING_SNAKE_CASE : Any = []
for i in range(len(UpperCAmelCase_ ) ):
try:
SCREAMING_SNAKE_CASE : int = tokenizer.decode([i] , clean_up_tokenization_spaces=UpperCAmelCase_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
SCREAMING_SNAKE_CASE : Optional[Any] = list(filter(lambda UpperCAmelCase_ : re.match(r"^[ a-zA-Z]+$" , t[1] ) , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : List[Any] = list(filter(lambda UpperCAmelCase_ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=UpperCAmelCase_ ) , UpperCAmelCase_ ) )
if max_length is not None and len(UpperCAmelCase_ ) > max_length:
SCREAMING_SNAKE_CASE : Dict = toks[:max_length]
if min_length is not None and len(UpperCAmelCase_ ) < min_length and len(UpperCAmelCase_ ) > 0:
while len(UpperCAmelCase_ ) < min_length:
SCREAMING_SNAKE_CASE : Dict = toks + toks
# toks_str = [t[1] for t in toks]
SCREAMING_SNAKE_CASE : Any = [t[0] for t in toks]
# Ensure consistency
SCREAMING_SNAKE_CASE : str = tokenizer.decode(UpperCAmelCase_ , clean_up_tokenization_spaces=UpperCAmelCase_ )
if " " not in output_txt and len(UpperCAmelCase_ ) > 1:
SCREAMING_SNAKE_CASE : Any = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=UpperCAmelCase_ )
+ " "
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=UpperCAmelCase_ )
)
if with_prefix_space:
SCREAMING_SNAKE_CASE : int = " " + output_txt
SCREAMING_SNAKE_CASE : Any = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
return output_txt, output_ids
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : List[str] = self.ta_base_tokenizer
SCREAMING_SNAKE_CASE : List[str] = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"] )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer(["hi", "I went to the gym", ""] )
self.assertListEqual(batch_with_eos_added["input_ids"] , batch_without_eos_added["input_ids"] )
def _A ( self : int ):
SCREAMING_SNAKE_CASE : Optional[int] = self.ta_base_tokenizer
SCREAMING_SNAKE_CASE : List[str] = "Unicode €."
SCREAMING_SNAKE_CASE : int = tokenizer(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
self.assertEqual(encoded["input_ids"] , UpperCAmelCase_ )
# decoding
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.decode(UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , "Unicode €.</s>" )
SCREAMING_SNAKE_CASE : str = tokenizer("e è é ê ë" )
SCREAMING_SNAKE_CASE : Optional[Any] = [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
self.assertEqual(encoded["input_ids"] , UpperCAmelCase_ )
# decoding
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.decode(UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , "e è é ê ë</s>" )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë" ) ) , "e è é ê ë</s>" )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : Any = self.ta_base_tokenizer
SCREAMING_SNAKE_CASE : Tuple = ["A long paragraph for summarization.", "Another paragraph for summarization."]
# fmt: off
SCREAMING_SNAKE_CASE : List[Any] = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
# fmt: on
SCREAMING_SNAKE_CASE : Dict = tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
if FRAMEWORK != "jax":
SCREAMING_SNAKE_CASE : Union[str, Any] = list(batch.input_ids.numpy()[0] )
else:
SCREAMING_SNAKE_CASE : List[Any] = list(batch.input_ids.tolist()[0] )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def _A ( self : int ):
SCREAMING_SNAKE_CASE : List[str] = self.ta_base_tokenizer
SCREAMING_SNAKE_CASE : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
SCREAMING_SNAKE_CASE : Tuple = tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn("input_ids" , UpperCAmelCase_ )
self.assertIn("attention_mask" , UpperCAmelCase_ )
self.assertNotIn("decoder_input_ids" , UpperCAmelCase_ )
self.assertNotIn("decoder_attention_mask" , UpperCAmelCase_ )
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Optional[Any] = self.ta_base_tokenizer
SCREAMING_SNAKE_CASE : Union[str, Any] = [
"Summary of the text.",
"Another summary.",
]
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(
text_target=UpperCAmelCase_ , max_length=32 , padding="max_length" , truncation=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ )
self.assertEqual(32 , targets["input_ids"].shape[1] )
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : str = self.ta_base_tokenizer
SCREAMING_SNAKE_CASE : Any = ["A long paragraph for summarization. </s>"]
SCREAMING_SNAKE_CASE : Dict = ["Summary of the text. </s>"]
# fmt: off
SCREAMING_SNAKE_CASE : List[Any] = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
SCREAMING_SNAKE_CASE : List[str] = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
# fmt: on
SCREAMING_SNAKE_CASE : int = tokenizer(UpperCAmelCase_ , text_target=UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , batch["input_ids"][0] )
self.assertEqual(UpperCAmelCase_ , batch["labels"][0] )
def _A ( self : Optional[Any] ):
# safety check on max_len default value so we are sure the test works
SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
SCREAMING_SNAKE_CASE : Optional[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : List[Any] = " He is very happy, UNwant\u00E9d,running"
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
tokenizer.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.__class__.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = after_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
shutil.rmtree(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
SCREAMING_SNAKE_CASE : int = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : Tuple = " He is very happy, UNwant\u00E9d,running"
tokenizer.add_tokens(["bim", "bambam"] )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token" )
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens} )
SCREAMING_SNAKE_CASE : Any = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
tokenizer.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = tokenizer.__class__.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = after_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertIn("new_additional_special_token" , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
SCREAMING_SNAKE_CASE : int = tokenizer.__class__.from_pretrained(UpperCAmelCase_ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(UpperCAmelCase_ )
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : Optional[int] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(UpperCAmelCase_ )
with open(os.path.join(UpperCAmelCase_ , "special_tokens_map.json" ) , encoding="utf-8" ) as json_file:
SCREAMING_SNAKE_CASE : List[Any] = json.load(UpperCAmelCase_ )
with open(os.path.join(UpperCAmelCase_ , "tokenizer_config.json" ) , encoding="utf-8" ) as json_file:
SCREAMING_SNAKE_CASE : List[str] = json.load(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = [f'''<extra_id_{i}>''' for i in range(125 )]
SCREAMING_SNAKE_CASE : List[str] = added_tokens_extra_ids + [
"an_additional_special_token"
]
SCREAMING_SNAKE_CASE : Optional[int] = added_tokens_extra_ids + [
"an_additional_special_token"
]
with open(os.path.join(UpperCAmelCase_ , "special_tokens_map.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
with open(os.path.join(UpperCAmelCase_ , "tokenizer_config.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_class.from_pretrained(
UpperCAmelCase_ , )
self.assertIn(
"an_additional_special_token" , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
["an_additional_special_token"] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
SCREAMING_SNAKE_CASE : str = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token" , lstrip=UpperCAmelCase_ )]
SCREAMING_SNAKE_CASE : Dict = tokenizer_class.from_pretrained(
UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , )
self.assertIn("a_new_additional_special_token" , tokenizer.additional_special_tokens )
self.assertEqual(
["a_new_additional_special_token"] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"] ) ) , )
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : Any = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer_class.from_pretrained(UpperCAmelCase_ )
self.assertTrue(tokenizer.decode([255] ) == "" )
def _A ( self : Optional[int] ):
pass
def _A ( self : Optional[Any] ):
pass
def _A ( self : Tuple ):
pass
def _A ( self : Optional[int] ):
pass
def _A ( self : Tuple ):
# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
# and special added tokens as tokens
SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizers(fast=UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE : Optional[int] = ["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.convert_tokens_to_string(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE : Optional[int] = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.convert_ids_to_tokens(
UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
for attr in attributes_list:
setattr(UpperCAmelCase_ , attr + "_id" , UpperCAmelCase_ )
self.assertEqual(getattr(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(getattr(UpperCAmelCase_ , attr + "_id" ) , UpperCAmelCase_ )
setattr(UpperCAmelCase_ , attr + "_id" , UpperCAmelCase_ )
self.assertEqual(getattr(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(getattr(UpperCAmelCase_ , attr + "_id" ) , UpperCAmelCase_ )
setattr(UpperCAmelCase_ , "additional_special_tokens_ids" , [] )
self.assertListEqual(getattr(UpperCAmelCase_ , "additional_special_tokens" ) , [] )
self.assertListEqual(getattr(UpperCAmelCase_ , "additional_special_tokens_ids" ) , [] )
setattr(UpperCAmelCase_ , "additional_special_tokens_ids" , [token_id_to_test_setters] )
self.assertListEqual(getattr(UpperCAmelCase_ , "additional_special_tokens" ) , [token_to_test_setters] )
self.assertListEqual(getattr(UpperCAmelCase_ , "additional_special_tokens_ids" ) , [token_id_to_test_setters] )
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class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : Union[str, Any] = set_counts
SCREAMING_SNAKE_CASE : Any = max(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = [1] * num_sets
SCREAMING_SNAKE_CASE : List[str] = list(range(UpperCAmelCase_ ) )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[Any] = self.get_parent(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self.get_parent(UpperCAmelCase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
SCREAMING_SNAKE_CASE : List[str] = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Tuple = src_parent
SCREAMING_SNAKE_CASE : Optional[int] = self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Optional[Any] = max(self.max_set , UpperCAmelCase_ )
return True
def _A ( self : Tuple , UpperCAmelCase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
SCREAMING_SNAKE_CASE : Tuple = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
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| 1
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import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
def lowerCamelCase__ ( lowercase , lowercase=False , lowercase=False , lowercase=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''transformer.blocks.{i}.norm1.weight''', F'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.norm1.bias''', F'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''transformer.blocks.{i}.attn.proj.weight''', F'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(F'''transformer.blocks.{i}.attn.proj.bias''', F'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''transformer.blocks.{i}.norm2.weight''', F'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.norm2.bias''', F'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(F'''transformer.blocks.{i}.mlp.fc1.weight''', F'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc1.bias''', F'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.weight''', F'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.bias''', F'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
("text_embeddings.word_embeddings.weight", "vilt.embeddings.text_embeddings.word_embeddings.weight"),
(
"text_embeddings.position_embeddings.weight",
"vilt.embeddings.text_embeddings.position_embeddings.weight",
),
("text_embeddings.position_ids", "vilt.embeddings.text_embeddings.position_ids"),
(
"text_embeddings.token_type_embeddings.weight",
"vilt.embeddings.text_embeddings.token_type_embeddings.weight",
),
("text_embeddings.LayerNorm.weight", "vilt.embeddings.text_embeddings.LayerNorm.weight"),
("text_embeddings.LayerNorm.bias", "vilt.embeddings.text_embeddings.LayerNorm.bias"),
# patch embeddings
("transformer.cls_token", "vilt.embeddings.cls_token"),
("transformer.patch_embed.proj.weight", "vilt.embeddings.patch_embeddings.projection.weight"),
("transformer.patch_embed.proj.bias", "vilt.embeddings.patch_embeddings.projection.bias"),
("transformer.pos_embed", "vilt.embeddings.position_embeddings"),
# token type embeddings
("token_type_embeddings.weight", "vilt.embeddings.token_type_embeddings.weight"),
] )
# final layernorm + pooler
rename_keys.extend(
[
("transformer.norm.weight", "vilt.layernorm.weight"),
("transformer.norm.bias", "vilt.layernorm.bias"),
("pooler.dense.weight", "vilt.pooler.dense.weight"),
("pooler.dense.bias", "vilt.pooler.dense.bias"),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
("vqa_classifier.0.weight", "classifier.0.weight"),
("vqa_classifier.0.bias", "classifier.0.bias"),
("vqa_classifier.1.weight", "classifier.1.weight"),
("vqa_classifier.1.bias", "classifier.1.bias"),
("vqa_classifier.3.weight", "classifier.3.weight"),
("vqa_classifier.3.bias", "classifier.3.bias"),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
("nlvr2_classifier.0.weight", "classifier.0.weight"),
("nlvr2_classifier.0.bias", "classifier.0.bias"),
("nlvr2_classifier.1.weight", "classifier.1.weight"),
("nlvr2_classifier.1.bias", "classifier.1.bias"),
("nlvr2_classifier.3.weight", "classifier.3.weight"),
("nlvr2_classifier.3.bias", "classifier.3.bias"),
] )
else:
pass
return rename_keys
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
SCREAMING_SNAKE_CASE : int = "vilt."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE : Dict = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE : str = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE : str = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE : int = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE : Any = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE : int = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE : Optional[int] = in_proj_bias[-config.hidden_size :]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = dct.pop(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = val
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ViltConfig(image_size=384 , patch_size=32 , tie_word_embeddings=lowercase )
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : str = False
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Tuple = False
if "vqa" in checkpoint_url:
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Tuple = 3129
SCREAMING_SNAKE_CASE : Union[str, Any] = "huggingface/label-files"
SCREAMING_SNAKE_CASE : int = "vqa2-id2label.json"
SCREAMING_SNAKE_CASE : Any = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE : Optional[Any] = {int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : List[str] = idalabel
SCREAMING_SNAKE_CASE : Optional[Any] = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Tuple = ViltForQuestionAnswering(lowercase )
elif "nlvr" in checkpoint_url:
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : List[str] = 2
SCREAMING_SNAKE_CASE : List[Any] = {0: "False", 1: "True"}
SCREAMING_SNAKE_CASE : Any = {v: k for k, v in config.idalabel.items()}
SCREAMING_SNAKE_CASE : Any = 3
SCREAMING_SNAKE_CASE : List[Any] = ViltForImagesAndTextClassification(lowercase )
elif "irtr" in checkpoint_url:
SCREAMING_SNAKE_CASE : Dict = True
SCREAMING_SNAKE_CASE : List[Any] = ViltForImageAndTextRetrieval(lowercase )
elif "mlm_itm" in checkpoint_url:
SCREAMING_SNAKE_CASE : List[Any] = True
SCREAMING_SNAKE_CASE : Any = ViltForMaskedLM(lowercase )
else:
raise ValueError("Unknown model type" )
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE : str = torch.hub.load_state_dict_from_url(lowercase , map_location="cpu" )["state_dict"]
SCREAMING_SNAKE_CASE : int = create_rename_keys(lowercase , lowercase , lowercase , lowercase )
for src, dest in rename_keys:
rename_key(lowercase , lowercase , lowercase )
read_in_q_k_v(lowercase , lowercase )
if mlm_model or irtr_model:
SCREAMING_SNAKE_CASE : Optional[int] = ["itm_score.fc.weight", "itm_score.fc.bias"]
for k in ignore_keys:
state_dict.pop(lowercase , lowercase )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = model.load_state_dict(lowercase , strict=lowercase )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowercase )
# Define processor
SCREAMING_SNAKE_CASE : Tuple = ViltImageProcessor(size=384 )
SCREAMING_SNAKE_CASE : int = BertTokenizer.from_pretrained("bert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = ViltProcessor(lowercase , lowercase )
# Forward pass on example inputs (image + text)
if nlvr_model:
SCREAMING_SNAKE_CASE : Optional[Any] = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg" , stream=lowercase ).raw )
SCREAMING_SNAKE_CASE : Optional[int] = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg" , stream=lowercase ).raw )
SCREAMING_SNAKE_CASE : Union[str, Any] = (
"The left image contains twice the number of dogs as the right image, and at least two dogs in total are"
" standing."
)
SCREAMING_SNAKE_CASE : Union[str, Any] = processor(lowercase , lowercase , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Optional[int] = processor(lowercase , lowercase , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Dict = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
SCREAMING_SNAKE_CASE : Any = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg" , stream=lowercase ).raw )
if mlm_model:
SCREAMING_SNAKE_CASE : Dict = "a bunch of [MASK] laying on a [MASK]."
else:
SCREAMING_SNAKE_CASE : str = "How many cats are there?"
SCREAMING_SNAKE_CASE : List[Any] = processor(lowercase , lowercase , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(**lowercase )
# Verify outputs
if mlm_model:
SCREAMING_SNAKE_CASE : List[Any] = torch.Size([1, 11, 30522] )
SCREAMING_SNAKE_CASE : Any = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase , atol=1E-4 )
# verify masked token prediction equals "cats"
SCREAMING_SNAKE_CASE : str = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
SCREAMING_SNAKE_CASE : Dict = torch.Size([1, 3129] )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , lowercase , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase , atol=1E-4 )
# verify vqa prediction equals "2"
SCREAMING_SNAKE_CASE : List[str] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
SCREAMING_SNAKE_CASE : Optional[Any] = torch.Size([1, 2] )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , lowercase , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(lowercase ).mkdir(exist_ok=lowercase )
print(F'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase )
processor.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt""",
type=str,
help="""URL of the checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
snake_case = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
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|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''timm_backbone'''
def __init__( self : List[Any] , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : List[str]=3 , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : Union[str, Any]=None , **UpperCAmelCase_ : Optional[Any] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = backbone
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = features_only
SCREAMING_SNAKE_CASE : Dict = use_pretrained_backbone
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : List[Any] = out_indices if out_indices is not None else (-1,)
| 319
| 1
|
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase=1024 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = [], []
SCREAMING_SNAKE_CASE : Union[str, Any] = list(zip(lowercase , lowercase ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = sorted_examples[0]
def is_too_big(lowercase ):
return tok(lowercase , return_tensors="pt" ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = new_src + " " + src
SCREAMING_SNAKE_CASE : Optional[Any] = new_tgt + " " + tgt
if is_too_big(lowercase ) or is_too_big(lowercase ): # cant fit, finalize example
finished_src.append(lowercase )
finished_tgt.append(lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = src, tgt
else: # can fit, keep adding
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(lowercase )
finished_tgt.append(lowercase )
return finished_src, finished_tgt
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = Path(lowercase )
save_path.mkdir(exist_ok=lowercase )
for split in ["train"]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
SCREAMING_SNAKE_CASE : int = [x.rstrip() for x in Path(lowercase ).open().readlines()]
SCREAMING_SNAKE_CASE : List[Any] = [x.rstrip() for x in Path(lowercase ).open().readlines()]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = pack_examples(lowercase , lowercase , lowercase , lowercase )
print(F'''packed {split} split from {len(lowercase )} examples -> {len(lowercase )}.''' )
Path(save_path / F'''{split}.source''' ).open("w" ).write("\n".join(lowercase ) )
Path(save_path / F'''{split}.target''' ).open("w" ).write("\n".join(lowercase ) )
for split in ["val", "test"]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
shutil.copyfile(lowercase , save_path / F'''{split}.source''' )
shutil.copyfile(lowercase , save_path / F'''{split}.target''' )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
parser.add_argument("--tok_name" , type=lowercase , help="like facebook/bart-large-cnn,t5-base, etc." )
parser.add_argument("--max_seq_len" , type=lowercase , default=128 )
parser.add_argument("--data_dir" , type=lowercase )
parser.add_argument("--save_path" , type=lowercase )
SCREAMING_SNAKE_CASE : Tuple = parser.parse_args()
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(lowercase , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 319
|
from math import sqrt
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for i in range(1 , int(sqrt(lowercase ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase ):
total += i + n // i
elif i == sqrt(lowercase ):
total += i
return total - n
def lowerCamelCase__ ( lowercase = 10000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = sum(
i
for i in range(1 , lowercase )
if sum_of_divisors(sum_of_divisors(lowercase ) ) == i and sum_of_divisors(lowercase ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 319
| 1
|
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
snake_case = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
snake_case = 250_004
snake_case = 250_020
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : List[Any] = MBartTokenizer
UpperCamelCase_ : Dict = MBartTokenizerFast
UpperCamelCase_ : Optional[int] = True
UpperCamelCase_ : Optional[int] = True
def _A ( self : Union[str, Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE : List[Any] = MBartTokenizer(UpperCAmelCase_ , keep_accents=UpperCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : Union[str, Any] = MBartTokenizer(UpperCAmelCase_ , keep_accents=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = tokenizer.tokenize("This is a test" )
self.assertListEqual(UpperCAmelCase_ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
UpperCAmelCase_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
SCREAMING_SNAKE_CASE : int = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
SCREAMING_SNAKE_CASE : int = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _A ( self : Dict ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
SCREAMING_SNAKE_CASE : Union[str, Any] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE : Optional[int] = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = self.tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
SCREAMING_SNAKE_CASE : List[str] = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(UpperCAmelCase_ , UpperCAmelCase_ )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCAmelCase_ )
# Save tokenizer rust, legacy_format=True
SCREAMING_SNAKE_CASE : Optional[int] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : int = tokenizer_r.save_pretrained(UpperCAmelCase_ , legacy_format=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCAmelCase_ , UpperCAmelCase_ )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : str = tokenizer_r.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
shutil.rmtree(UpperCAmelCase_ )
# Save tokenizer rust, legacy_format=False
SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer_r.save_pretrained(UpperCAmelCase_ , legacy_format=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
shutil.rmtree(UpperCAmelCase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = '''facebook/mbart-large-en-ro'''
UpperCamelCase_ : Any = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
UpperCamelCase_ : Optional[Any] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
UpperCamelCase_ : List[Any] = [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
@classmethod
def _A ( cls : Union[str, Any] ):
SCREAMING_SNAKE_CASE : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO" )
SCREAMING_SNAKE_CASE : int = 1
return cls
def _A ( self : Union[str, Any] ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 25_0001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 25_0004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 25_0020 )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase_ )
def _A ( self : Tuple ):
self.assertIn(UpperCAmelCase_ , self.tokenizer.all_special_ids )
SCREAMING_SNAKE_CASE : Tuple = [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer.decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase_ )
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = 10
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer(UpperCAmelCase_ , max_length=UpperCAmelCase_ , truncation=UpperCAmelCase_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , UpperCAmelCase_ )
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
def _A ( self : Optional[int] ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [25_0026, 25_0001] )
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : List[str] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = MBartTokenizer.from_pretrained(UpperCAmelCase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCAmelCase_ )
@require_torch
def _A ( self : int ):
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase_ , return_tensors="pt" )
SCREAMING_SNAKE_CASE : int = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
SCREAMING_SNAKE_CASE : str = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
SCREAMING_SNAKE_CASE : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer(self.src_text , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , max_length=3 , return_tensors="pt" )
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer(
text_target=self.tgt_text , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , max_length=10 , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Tuple = targets["input_ids"]
SCREAMING_SNAKE_CASE : Optional[int] = shift_tokens_right(UpperCAmelCase_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR" )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , {
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 25_0004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 25_0001,
} , )
| 319
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EncodecModel""",
"""EncodecPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
snake_case = logging.get_logger(__name__)
if is_vision_available():
import PIL
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ['''pixel_values''']
def __init__( self : List[Any] , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Dict[str, int] = None , UpperCAmelCase_ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Dict[str, int] = None , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Union[int, float] = 1 / 255 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : bool = True , **UpperCAmelCase_ : Optional[Any] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = size if size is not None else {"shortest_edge": 224}
SCREAMING_SNAKE_CASE : Tuple = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = crop_size if crop_size is not None else {"height": 224, "width": 224}
SCREAMING_SNAKE_CASE : str = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ , param_name="crop_size" )
SCREAMING_SNAKE_CASE : Optional[Any] = do_resize
SCREAMING_SNAKE_CASE : Optional[Any] = size
SCREAMING_SNAKE_CASE : Optional[Any] = resample
SCREAMING_SNAKE_CASE : Optional[int] = do_center_crop
SCREAMING_SNAKE_CASE : Optional[Any] = crop_size
SCREAMING_SNAKE_CASE : str = do_rescale
SCREAMING_SNAKE_CASE : Any = rescale_factor
SCREAMING_SNAKE_CASE : Dict = do_normalize
SCREAMING_SNAKE_CASE : Dict = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
SCREAMING_SNAKE_CASE : str = image_std if image_std is not None else OPENAI_CLIP_STD
SCREAMING_SNAKE_CASE : Dict = do_convert_rgb
def _A ( self : int , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Dict[str, int] , UpperCAmelCase_ : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : Tuple , ):
SCREAMING_SNAKE_CASE : str = get_size_dict(UpperCAmelCase_ , default_to_square=UpperCAmelCase_ )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = get_resize_output_image_size(UpperCAmelCase_ , size=size["shortest_edge"] , default_to_square=UpperCAmelCase_ )
return resize(UpperCAmelCase_ , size=UpperCAmelCase_ , resample=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : str , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Dict[str, int] , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : int , ):
SCREAMING_SNAKE_CASE : List[str] = get_size_dict(UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(UpperCAmelCase_ , size=(size["height"], size["width"]) , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : Optional[int] , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Union[int, float] , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : str , ):
return rescale(UpperCAmelCase_ , scale=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : Union[float, List[float]] , UpperCAmelCase_ : Union[float, List[float]] , UpperCAmelCase_ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase_ : Optional[int] , ):
return normalize(UpperCAmelCase_ , mean=UpperCAmelCase_ , std=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : Optional[Any] , UpperCAmelCase_ : ImageInput , UpperCAmelCase_ : bool = None , UpperCAmelCase_ : Dict[str, int] = None , UpperCAmelCase_ : PILImageResampling = None , UpperCAmelCase_ : bool = None , UpperCAmelCase_ : int = None , UpperCAmelCase_ : bool = None , UpperCAmelCase_ : float = None , UpperCAmelCase_ : bool = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : Optional[Union[float, List[float]]] = None , UpperCAmelCase_ : bool = None , UpperCAmelCase_ : Optional[Union[str, TensorType]] = None , UpperCAmelCase_ : Optional[ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase_ : Dict , ):
SCREAMING_SNAKE_CASE : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE : Dict = size if size is not None else self.size
SCREAMING_SNAKE_CASE : Dict = get_size_dict(UpperCAmelCase_ , param_name="size" , default_to_square=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE : List[str] = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE : Dict = get_size_dict(UpperCAmelCase_ , param_name="crop_size" , default_to_square=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE : Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE : Optional[Any] = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE : List[Any] = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE : str = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
SCREAMING_SNAKE_CASE : Tuple = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
SCREAMING_SNAKE_CASE : int = [convert_to_rgb(UpperCAmelCase_ ) for image in images]
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE : List[Any] = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE : int = [self.resize(image=UpperCAmelCase_ , size=UpperCAmelCase_ , resample=UpperCAmelCase_ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE : List[str] = [self.center_crop(image=UpperCAmelCase_ , size=UpperCAmelCase_ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE : List[str] = [self.rescale(image=UpperCAmelCase_ , scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE : Dict = [self.normalize(image=UpperCAmelCase_ , mean=UpperCAmelCase_ , std=UpperCAmelCase_ ) for image in images]
SCREAMING_SNAKE_CASE : int = [to_channel_dimension_format(UpperCAmelCase_ , UpperCAmelCase_ ) for image in images]
SCREAMING_SNAKE_CASE : List[Any] = {"pixel_values": images}
return BatchFeature(data=UpperCAmelCase_ , tensor_type=UpperCAmelCase_ )
| 319
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
snake_case = None
snake_case = logging.get_logger(__name__)
snake_case = """▁"""
snake_case = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
snake_case = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
snake_case = {
"""google/pegasus-xsum""": 512,
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES
UpperCamelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : int = PegasusTokenizer
UpperCamelCase_ : str = ['''input_ids''', '''attention_mask''']
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Optional[int]="<pad>" , UpperCAmelCase_ : int="</s>" , UpperCAmelCase_ : str="<unk>" , UpperCAmelCase_ : str="<mask_2>" , UpperCAmelCase_ : Optional[int]="<mask_1>" , UpperCAmelCase_ : int=None , UpperCAmelCase_ : str=103 , **UpperCAmelCase_ : Optional[int] , ):
SCREAMING_SNAKE_CASE : Optional[Any] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError(
f'''additional_special_tokens should be of type {type(UpperCAmelCase_ )}, but is'''
f''' {type(UpperCAmelCase_ )}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(UpperCAmelCase_ ) , self.offset - 1 )
]
if len(set(UpperCAmelCase_ ) ) != len(UpperCAmelCase_ ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
SCREAMING_SNAKE_CASE : int = additional_special_tokens_extended
else:
SCREAMING_SNAKE_CASE : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
super().__init__(
UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , mask_token_sent=UpperCAmelCase_ , offset=UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : str = vocab_file
SCREAMING_SNAKE_CASE : str = False if not self.vocab_file else True
def _A ( self : Optional[Any] , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Optional[int] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
f''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : int , UpperCAmelCase_ : List , UpperCAmelCase_ : Optional[List] = None , UpperCAmelCase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(UpperCAmelCase_ )
elif token_ids_a is None:
return self._special_token_mask(UpperCAmelCase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Any=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : str , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
UpperCAmelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase_ ):
copyfile(self.vocab_file , UpperCAmelCase_ )
return (out_vocab_file,)
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import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = '''mask2former'''
UpperCamelCase_ : Optional[Any] = ['''swin''']
UpperCamelCase_ : List[Any] = {'''hidden_size''': '''hidden_dim'''}
def __init__( self : List[Any] , UpperCAmelCase_ : Optional[Dict] = None , UpperCAmelCase_ : int = 256 , UpperCAmelCase_ : int = 256 , UpperCAmelCase_ : int = 256 , UpperCAmelCase_ : int = 1024 , UpperCAmelCase_ : str = "relu" , UpperCAmelCase_ : int = 6 , UpperCAmelCase_ : int = 10 , UpperCAmelCase_ : int = 8 , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : int = 2048 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : int = 4 , UpperCAmelCase_ : int = 255 , UpperCAmelCase_ : int = 100 , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : float = 2.0 , UpperCAmelCase_ : float = 5.0 , UpperCAmelCase_ : float = 5.0 , UpperCAmelCase_ : int = 1_2544 , UpperCAmelCase_ : float = 3.0 , UpperCAmelCase_ : float = 0.75 , UpperCAmelCase_ : float = 0.02 , UpperCAmelCase_ : float = 1.0 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : List[int] = [4, 8, 16, 32] , UpperCAmelCase_ : bool = None , **UpperCAmelCase_ : List[str] , ):
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
SCREAMING_SNAKE_CASE : Tuple = CONFIG_MAPPING["swin"](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=UpperCAmelCase_ , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = backbone_config.pop("model_type" )
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE : List[Any] = config_class.from_dict(UpperCAmelCase_ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
f'''Supported model types: {','.join(self.backbones_supported )}''' )
SCREAMING_SNAKE_CASE : List[str] = backbone_config
SCREAMING_SNAKE_CASE : Optional[int] = feature_size
SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
SCREAMING_SNAKE_CASE : str = hidden_dim
SCREAMING_SNAKE_CASE : Tuple = encoder_feedforward_dim
SCREAMING_SNAKE_CASE : Optional[int] = activation_function
SCREAMING_SNAKE_CASE : Optional[int] = encoder_layers
SCREAMING_SNAKE_CASE : str = decoder_layers
SCREAMING_SNAKE_CASE : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE : str = dropout
SCREAMING_SNAKE_CASE : List[str] = dim_feedforward
SCREAMING_SNAKE_CASE : List[Any] = pre_norm
SCREAMING_SNAKE_CASE : List[str] = enforce_input_projection
SCREAMING_SNAKE_CASE : List[Any] = common_stride
SCREAMING_SNAKE_CASE : int = ignore_value
SCREAMING_SNAKE_CASE : List[Any] = num_queries
SCREAMING_SNAKE_CASE : Any = no_object_weight
SCREAMING_SNAKE_CASE : List[Any] = class_weight
SCREAMING_SNAKE_CASE : List[Any] = mask_weight
SCREAMING_SNAKE_CASE : Dict = dice_weight
SCREAMING_SNAKE_CASE : Any = train_num_points
SCREAMING_SNAKE_CASE : Tuple = oversample_ratio
SCREAMING_SNAKE_CASE : Optional[int] = importance_sample_ratio
SCREAMING_SNAKE_CASE : List[Any] = init_std
SCREAMING_SNAKE_CASE : List[Any] = init_xavier_std
SCREAMING_SNAKE_CASE : int = use_auxiliary_loss
SCREAMING_SNAKE_CASE : Dict = feature_strides
SCREAMING_SNAKE_CASE : Optional[Any] = output_auxiliary_logits
SCREAMING_SNAKE_CASE : Tuple = decoder_layers
super().__init__(**UpperCAmelCase_ )
@classmethod
def _A ( cls : str , UpperCAmelCase_ : PretrainedConfig , **UpperCAmelCase_ : int ):
return cls(
backbone_config=UpperCAmelCase_ , **UpperCAmelCase_ , )
def _A ( self : List[Any] ):
SCREAMING_SNAKE_CASE : Dict = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE : int = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE : Tuple = self.__class__.model_type
return output
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|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""SpeechEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""FlaxSpeechEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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|
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
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|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
snake_case = 16
snake_case = 32
def lowerCamelCase__ ( lowercase , lowercase = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained("bert-base-cased" )
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset("glue" , "mrpc" )
def tokenize_function(lowercase ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=lowercase , max_length=lowercase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE : List[Any] = datasets.map(
lowercase , batched=lowercase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE : Tuple = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE : str = 16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE : Optional[Any] = 8
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = None
return tokenizer.pad(
lowercase , padding="longest" , max_length=lowercase , pad_to_multiple_of=lowercase , return_tensors="pt" , )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE : Optional[int] = DataLoader(
tokenized_datasets["train"] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
SCREAMING_SNAKE_CASE : Dict = DataLoader(
tokenized_datasets["validation"] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
snake_case = mocked_dataloaders # noqa: F811
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , lowercase ) == "1":
SCREAMING_SNAKE_CASE : int = 2
# New Code #
SCREAMING_SNAKE_CASE : Union[str, Any] = int(args.gradient_accumulation_steps )
# Initialize accelerator
SCREAMING_SNAKE_CASE : Tuple = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE : Any = config["lr"]
SCREAMING_SNAKE_CASE : Optional[Any] = int(config["num_epochs"] )
SCREAMING_SNAKE_CASE : List[Any] = int(config["seed"] )
SCREAMING_SNAKE_CASE : Union[str, Any] = int(config["batch_size"] )
SCREAMING_SNAKE_CASE : Optional[Any] = evaluate.load("glue" , "mrpc" )
set_seed(lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = get_dataloaders(lowercase , lowercase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE : List[Any] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=lowercase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE : Any = model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE : Any = AdamW(params=model.parameters() , lr=lowercase )
# Instantiate scheduler
SCREAMING_SNAKE_CASE : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=lowercase , num_warmup_steps=100 , num_training_steps=(len(lowercase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = accelerator.prepare(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Now we train the model
for epoch in range(lowercase ):
model.train()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase ):
SCREAMING_SNAKE_CASE : Any = model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = output.loss
accelerator.backward(lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[Any] = model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=lowercase , references=lowercase , )
SCREAMING_SNAKE_CASE : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowercase )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=lowercase , default=lowercase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=lowercase , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
SCREAMING_SNAKE_CASE : Dict = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(lowercase , lowercase )
if __name__ == "__main__":
main()
| 319
| 1
|
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase__ ( lowercase , lowercase , lowercase=None , **lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = [x.strip() for x in open(lowercase ).readlines()]
SCREAMING_SNAKE_CASE : int = [x.strip() for x in open(lowercase ).readlines()][: len(lowercase )]
SCREAMING_SNAKE_CASE : Dict = calculate_rouge(lowercase , lowercase , **lowercase )
if save_path is not None:
save_json(lowercase , lowercase , indent=lowercase )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 319
|
import functools
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ) or not all(isinstance(lowercase , lowercase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(lowercase ) != 3 or not all(isinstance(lowercase , lowercase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(lowercase ) == 0:
return 0
if min(lowercase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(lowercase ) >= 366:
raise ValueError("All days elements should be less than 366" )
SCREAMING_SNAKE_CASE : Dict = set(lowercase )
@functools.cache
def dynamic_programming(lowercase ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
| 1
|
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
snake_case = logging.get_logger(__name__)
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if isinstance(lowercase , np.ndarray ):
return list(tensor.shape )
SCREAMING_SNAKE_CASE : Optional[Any] = tf.shape(lowercase )
if tensor.shape == tf.TensorShape(lowercase ):
return dynamic
SCREAMING_SNAKE_CASE : Optional[Any] = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(lowercase )]
def lowerCamelCase__ ( lowercase , lowercase = None , lowercase = None ):
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1E-9 , axis=lowercase , name=lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase=1E-5 , lowercase=-1 ):
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowercase , lowercase ):
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 : Dict = tf.nn.moments(lowercase , axes=[axis] , keepdims=lowercase )
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 : Optional[int] = [1] * inputs.shape.rank
SCREAMING_SNAKE_CASE : int = shape_list(lowercase )[axis]
SCREAMING_SNAKE_CASE : Tuple = tf.reshape(lowercase , lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = tf.reshape(lowercase , lowercase )
# Compute layer normalization using the batch_normalization
# function.
SCREAMING_SNAKE_CASE : List[Any] = tf.nn.batch_normalization(
lowercase , lowercase , lowercase , offset=lowercase , scale=lowercase , variance_epsilon=lowercase , )
return outputs
def lowerCamelCase__ ( lowercase , lowercase=0 , lowercase=-1 ):
"""simple docstring"""
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 : List[str] = tf.shape(lowercase )
SCREAMING_SNAKE_CASE : Any = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
SCREAMING_SNAKE_CASE : Tuple = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(lowercase , lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , tf.Tensor ):
SCREAMING_SNAKE_CASE : Optional[int] = tf.convert_to_tensor(lowercase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
SCREAMING_SNAKE_CASE : Optional[int] = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
SCREAMING_SNAKE_CASE : 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 : str = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase__ ( lowercase , lowercase , lowercase = "input_ids" ):
"""simple docstring"""
tf.debugging.assert_less(
lowercase , tf.cast(lowercase , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(lowercase )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = 64512
# 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 : Optional[int] = [x for x in data if len(lowercase ) > 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 : Dict = np.asarray(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = 1
SCREAMING_SNAKE_CASE : List[Any] = np.array_split(lowercase , lowercase )
# 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 : Dict = np.array_split(lowercase , lowercase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(lowercase ):
SCREAMING_SNAKE_CASE : List[str] = chunk_data
else:
SCREAMING_SNAKE_CASE : List[Any] = data
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if name in group.attrs:
SCREAMING_SNAKE_CASE : int = [n.decode("utf8" ) if hasattr(lowercase , "decode" ) else n for n in group.attrs[name]]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : int = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("utf8" ) if hasattr(lowercase , "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
def _expand_single_ad_tensor(lowercase ):
if isinstance(lowercase , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(lowercase , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , lowercase )
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|
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 319
| 1
|
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCamelCase__ ( lowercase = 8 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ascii_letters + digits + punctuation
return "".join(secrets.choice(lowercase ) for _ in range(lowercase ) )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
i -= len(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = i // 3
SCREAMING_SNAKE_CASE : List[Any] = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
SCREAMING_SNAKE_CASE : Any = (
chars_incl
+ random(lowercase , quotient + remainder )
+ random(lowercase , lowercase )
+ random(lowercase , lowercase )
)
SCREAMING_SNAKE_CASE : Dict = list(lowercase )
shuffle(lowercase )
return "".join(lowercase )
# random is a generalised function for letters, characters and numbers
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return "".join(secrets.choice(lowercase ) for _ in range(lowercase ) )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
pass # Put your code here...
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
pass # Put your code here...
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
pass # Put your code here...
def lowerCamelCase__ ( lowercase , lowercase = 8 ):
"""simple docstring"""
if len(lowercase ) < min_length:
# Your Password must be at least 8 characters long
return False
SCREAMING_SNAKE_CASE : Tuple = any(char in ascii_uppercase for char in password )
SCREAMING_SNAKE_CASE : Optional[int] = any(char in ascii_lowercase for char in password )
SCREAMING_SNAKE_CASE : Optional[int] = any(char in digits for char in password )
SCREAMING_SNAKE_CASE : str = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = int(input("Please indicate the max length of your password: " ).strip() )
SCREAMING_SNAKE_CASE : List[str] = input(
"Please indicate the characters that must be in your password: " ).strip()
print("Password generated:" , password_generator(lowercase ) )
print(
"Alternative Password generated:" , alternative_password_generator(lowercase , lowercase ) , )
print("[If you are thinking of using this passsword, You better save it.]" )
if __name__ == "__main__":
main()
| 319
|
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
snake_case = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = torch.load(lowercase , map_location="cpu" )
return sd
def lowerCamelCase__ ( lowercase , lowercase , lowercase=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = OrderedDict()
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE : Optional[Any] = key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE : Tuple = new_key.replace(name_pair[0] , name_pair[1] )
SCREAMING_SNAKE_CASE : Union[str, Any] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE : Union[str, Any] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE : str = "pretraining"
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE : str = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE : Optional[int] = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.''' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE : Optional[Any] = {"visual_embedding_dim": 512}
SCREAMING_SNAKE_CASE : Union[str, Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE : int = {"visual_embedding_dim": 2048}
SCREAMING_SNAKE_CASE : Any = "vqa_advanced"
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE : Any = {"visual_embedding_dim": 2048, "num_labels": 3129}
SCREAMING_SNAKE_CASE : Tuple = "vqa"
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE : int = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
SCREAMING_SNAKE_CASE : Union[str, Any] = "nlvr"
SCREAMING_SNAKE_CASE : List[Any] = VisualBertConfig(**lowercase )
# Load State Dict
SCREAMING_SNAKE_CASE : Union[str, Any] = load_state_dict(lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = get_new_dict(lowercase , lowercase )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE : Union[str, Any] = VisualBertForPreTraining(lowercase )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE : Optional[Any] = VisualBertForQuestionAnswering(lowercase )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE : Optional[Any] = VisualBertForVisualReasoning(lowercase )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE : List[Any] = VisualBertForMultipleChoice(lowercase )
model.load_state_dict(lowercase )
# Save Checkpoints
Path(lowercase ).mkdir(exist_ok=lowercase )
model.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
snake_case = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 319
| 1
|
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(lowercase , lowercase ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
SCREAMING_SNAKE_CASE : List[Any] = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(lowercase )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
|
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''ClapFeatureExtractor'''
UpperCamelCase_ : Any = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : List[str]=None , **UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("sampling_rate" , UpperCAmelCase_ )
if text is None and audios is None:
raise ValueError("You have to specify either text or audios. Both cannot be none." )
if text is not None:
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if audios is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.feature_extractor(
UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None and audios is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase_ ) , tensor_type=UpperCAmelCase_ )
def _A ( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : str ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : List[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Any ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@property
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Any = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE : List[Any] = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 319
| 1
|
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case = """
import os
"""
snake_case = """
def foo():
import os
return False
"""
snake_case = """
def foo():
def bar():
if True:
import os
return False
return bar()
"""
snake_case = """
import os
try:
import bar
except ImportError:
raise ValueError()
"""
snake_case = """
import os
def foo():
try:
import bar
except ImportError:
raise ValueError()
"""
snake_case = """
import os
try:
import bar
except (ImportError, AttributeError):
raise ValueError()
"""
snake_case = """
import os
try:
import bar
except ImportError as e:
raise ValueError()
"""
snake_case = """
import os
try:
import bar
except:
raise ValueError()
"""
snake_case = """
import os
try:
import bar
import baz
except ImportError:
raise ValueError()
"""
snake_case = """
import os
try:
import bar
import baz
except ImportError:
x = 1
raise ValueError()
"""
snake_case = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("case" , lowercase )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(lowercase , "test_file.py" )
with open(lowercase , "w" ) as _tmp_file:
_tmp_file.write(lowercase )
SCREAMING_SNAKE_CASE : int = get_imports(lowercase )
assert parsed_imports == ["os"]
| 319
|
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Any = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : Optional[int] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Any = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(lowercase , cache_dir=lowercase , split=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = parquet_path
elif issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [parquet_path]
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Tuple = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=("train",) ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
for split in splits:
SCREAMING_SNAKE_CASE : Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Dict = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(
{"train": parquet_path} , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[int] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Dict = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : str = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : Optional[Any] = ParquetDatasetReader({"train": parquet_path} , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if split:
SCREAMING_SNAKE_CASE : Any = {split: parquet_path}
else:
SCREAMING_SNAKE_CASE : Tuple = "train"
SCREAMING_SNAKE_CASE : int = {"train": parquet_path, "test": parquet_path}
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : str = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : int = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : Tuple = pq.ParquetFile(tmp_path / "foo.parquet" )
SCREAMING_SNAKE_CASE : List[Any] = pf.read()
assert dataset.data.table == output_table
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = str(shared_datadir / "test_image_rgb.jpg" )
SCREAMING_SNAKE_CASE : Union[str, Any] = {"image": [image_path]}
SCREAMING_SNAKE_CASE : Union[str, Any] = Features({"image": Image()} )
SCREAMING_SNAKE_CASE : int = Dataset.from_dict(lowercase , features=lowercase )
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : str = Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
assert dataset.features == reloaded_dataset.features
SCREAMING_SNAKE_CASE : Any = ParquetDatasetReader(str(tmp_path / "foo.parquet" ) , streaming=lowercase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32" )} ), None),
(Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert get_writer_batch_size(lowercase ) == expected
| 319
| 1
|
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()
snake_case = logging.get_logger(__name__)
snake_case = """Hello, World!"""
snake_case = """en_XX"""
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = Path("data_bin" )
SCREAMING_SNAKE_CASE : int = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(lowercase ).parent ) , checkpoint_file=Path(lowercase ).name , _name="xmod_base" , arch="xmod_base" , task="multilingual_masked_lm" , data_name_or_path=str(lowercase ) , bpe="sentencepiece" , sentencepiece_model=str(Path(lowercase ).parent / "sentencepiece.bpe.model" ) , src_dict=str(data_dir / "dict.txt" ) , )
xmod.eval() # disable dropout
print(lowercase )
SCREAMING_SNAKE_CASE : int = xmod.model.encoder.sentence_encoder
SCREAMING_SNAKE_CASE : 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:
SCREAMING_SNAKE_CASE : List[Any] = xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our X-MOD config:" , lowercase )
SCREAMING_SNAKE_CASE : str = XmodForSequenceClassification(lowercase ) if classification_head else XmodForMaskedLM(lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
SCREAMING_SNAKE_CASE : Dict = xmod_sent_encoder.embed_tokens.weight
SCREAMING_SNAKE_CASE : Union[str, Any] = xmod_sent_encoder.embed_positions.weight
SCREAMING_SNAKE_CASE : Dict = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
SCREAMING_SNAKE_CASE : List[str] = xmod_sent_encoder.layernorm_embedding.weight
SCREAMING_SNAKE_CASE : Optional[Any] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
SCREAMING_SNAKE_CASE : List[Any] = model.roberta.encoder.layer[i]
SCREAMING_SNAKE_CASE : Dict = xmod_sent_encoder.layers[i]
# self attention
SCREAMING_SNAKE_CASE : Dict = 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." )
SCREAMING_SNAKE_CASE : Optional[int] = xmod_layer.self_attn.q_proj.weight
SCREAMING_SNAKE_CASE : Tuple = xmod_layer.self_attn.q_proj.bias
SCREAMING_SNAKE_CASE : Optional[int] = xmod_layer.self_attn.k_proj.weight
SCREAMING_SNAKE_CASE : Union[str, Any] = xmod_layer.self_attn.k_proj.bias
SCREAMING_SNAKE_CASE : Optional[Any] = xmod_layer.self_attn.v_proj.weight
SCREAMING_SNAKE_CASE : Tuple = xmod_layer.self_attn.v_proj.bias
# self-attention output
SCREAMING_SNAKE_CASE : Union[str, 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." )
SCREAMING_SNAKE_CASE : Dict = xmod_layer.self_attn.out_proj.weight
SCREAMING_SNAKE_CASE : str = xmod_layer.self_attn.out_proj.bias
SCREAMING_SNAKE_CASE : str = xmod_layer.self_attn_layer_norm.weight
SCREAMING_SNAKE_CASE : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
SCREAMING_SNAKE_CASE : List[Any] = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of intermediate weights do not match." )
SCREAMING_SNAKE_CASE : Union[str, Any] = xmod_layer.fca.weight
SCREAMING_SNAKE_CASE : List[Any] = xmod_layer.fca.bias
# output
SCREAMING_SNAKE_CASE : int = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of feed-forward weights do not match." )
SCREAMING_SNAKE_CASE : Dict = xmod_layer.fca.weight
SCREAMING_SNAKE_CASE : Tuple = xmod_layer.fca.bias
SCREAMING_SNAKE_CASE : Optional[int] = xmod_layer.final_layer_norm.weight
SCREAMING_SNAKE_CASE : Any = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = xmod_layer.adapter_layer_norm.weight
SCREAMING_SNAKE_CASE : int = 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():
SCREAMING_SNAKE_CASE : Tuple = bert_output.adapter_modules[lang_code]
SCREAMING_SNAKE_CASE : int = xmod_layer.adapter_modules[lang_code]
SCREAMING_SNAKE_CASE : List[str] = from_adapter.fca.weight
SCREAMING_SNAKE_CASE : Tuple = from_adapter.fca.bias
SCREAMING_SNAKE_CASE : Any = from_adapter.fca.weight
SCREAMING_SNAKE_CASE : Dict = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
SCREAMING_SNAKE_CASE : List[Any] = xmod_sent_encoder.layer_norm.weight
SCREAMING_SNAKE_CASE : Optional[int] = xmod_sent_encoder.layer_norm.bias
if classification_head:
SCREAMING_SNAKE_CASE : Any = xmod.model.classification_heads["mnli"].dense.weight
SCREAMING_SNAKE_CASE : List[str] = xmod.model.classification_heads["mnli"].dense.bias
SCREAMING_SNAKE_CASE : Dict = xmod.model.classification_heads["mnli"].out_proj.weight
SCREAMING_SNAKE_CASE : Tuple = xmod.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
SCREAMING_SNAKE_CASE : Union[str, Any] = xmod.model.encoder.lm_head.dense.weight
SCREAMING_SNAKE_CASE : int = xmod.model.encoder.lm_head.dense.bias
SCREAMING_SNAKE_CASE : Optional[int] = xmod.model.encoder.lm_head.layer_norm.weight
SCREAMING_SNAKE_CASE : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.bias
SCREAMING_SNAKE_CASE : Tuple = xmod.model.encoder.lm_head.weight
SCREAMING_SNAKE_CASE : int = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
SCREAMING_SNAKE_CASE : Dict = xmod.encode(lowercase ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(lowercase )
SCREAMING_SNAKE_CASE : List[str] = model(lowercase )[0]
if classification_head:
SCREAMING_SNAKE_CASE : str = xmod.model.classification_heads["mnli"](xmod.extract_features(lowercase ) )
else:
SCREAMING_SNAKE_CASE : Tuple = xmod.model(lowercase , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
SCREAMING_SNAKE_CASE : Optional[Any] = torch.allclose(lowercase , lowercase , atol=1E-3 )
print("Do both models output the same tensors?" , "🔥" if success else "💩" )
if not success:
raise Exception("Something went wRoNg" )
Path(lowercase ).mkdir(parents=lowercase , exist_ok=lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = 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."""
)
snake_case = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 319
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = '''gptj'''
UpperCamelCase_ : Tuple = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : str , UpperCAmelCase_ : Optional[int]=5_0400 , UpperCAmelCase_ : Tuple=2048 , UpperCAmelCase_ : int=4096 , UpperCAmelCase_ : Union[str, Any]=28 , UpperCAmelCase_ : Tuple=16 , UpperCAmelCase_ : int=64 , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : Dict="gelu_new" , UpperCAmelCase_ : int=0.0 , UpperCAmelCase_ : Any=0.0 , UpperCAmelCase_ : Any=0.0 , UpperCAmelCase_ : List[str]=1E-5 , UpperCAmelCase_ : str=0.02 , UpperCAmelCase_ : Union[str, Any]=True , UpperCAmelCase_ : str=5_0256 , UpperCAmelCase_ : Tuple=5_0256 , UpperCAmelCase_ : int=False , **UpperCAmelCase_ : Optional[Any] , ):
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : Any = n_positions
SCREAMING_SNAKE_CASE : List[Any] = n_embd
SCREAMING_SNAKE_CASE : Optional[Any] = n_layer
SCREAMING_SNAKE_CASE : Union[str, Any] = n_head
SCREAMING_SNAKE_CASE : Any = n_inner
SCREAMING_SNAKE_CASE : Tuple = rotary_dim
SCREAMING_SNAKE_CASE : Any = activation_function
SCREAMING_SNAKE_CASE : Any = resid_pdrop
SCREAMING_SNAKE_CASE : int = embd_pdrop
SCREAMING_SNAKE_CASE : Optional[Any] = attn_pdrop
SCREAMING_SNAKE_CASE : int = layer_norm_epsilon
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = use_cache
SCREAMING_SNAKE_CASE : List[Any] = bos_token_id
SCREAMING_SNAKE_CASE : Optional[int] = eos_token_id
super().__init__(
bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , tie_word_embeddings=UpperCAmelCase_ , **UpperCAmelCase_ )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : PretrainedConfig , UpperCAmelCase_ : str = "default" , UpperCAmelCase_ : List[PatchingSpec] = None , UpperCAmelCase_ : bool = False , ):
super().__init__(UpperCAmelCase_ , task=UpperCAmelCase_ , patching_specs=UpperCAmelCase_ , use_past=UpperCAmelCase_ )
if not getattr(self._config , "pad_token_id" , UpperCAmelCase_ ):
# TODO: how to do that better?
SCREAMING_SNAKE_CASE : List[str] = 0
@property
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : List[str] = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase_ , direction="inputs" )
SCREAMING_SNAKE_CASE : Any = {0: "batch", 1: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE : Tuple = {0: "batch", 1: "sequence"}
return common_inputs
@property
def _A ( self : Any ):
return self._config.n_layer
@property
def _A ( self : Dict ):
return self._config.n_head
def _A ( self : List[str] , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[TensorType] = None , ):
SCREAMING_SNAKE_CASE : str = super(UpperCAmelCase_ , self ).generate_dummy_inputs(
UpperCAmelCase_ , batch_size=UpperCAmelCase_ , seq_length=UpperCAmelCase_ , is_pair=UpperCAmelCase_ , framework=UpperCAmelCase_ )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE : str = OrderedDict({"input_ids": common_inputs["input_ids"]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE : Optional[Any] = seqlen + 2
SCREAMING_SNAKE_CASE : Dict = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
SCREAMING_SNAKE_CASE : Tuple = [
(torch.zeros(UpperCAmelCase_ ), torch.zeros(UpperCAmelCase_ )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE : Any = common_inputs["attention_mask"]
if self.use_past:
SCREAMING_SNAKE_CASE : Union[str, Any] = ordered_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE : Any = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(UpperCAmelCase_ , UpperCAmelCase_ , dtype=UpperCAmelCase_ )] , dim=1 )
return ordered_inputs
@property
def _A ( self : str ):
return 13
| 319
|
def lowerCamelCase__ ( lowercase , lowercase = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = length or len(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = list_data[i + 1], list_data[i]
SCREAMING_SNAKE_CASE : str = True
return list_data if not swapped else bubble_sort(lowercase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""",
],
"""processing_clipseg""": ["""CLIPSegProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPSegModel""",
"""CLIPSegPreTrainedModel""",
"""CLIPSegTextModel""",
"""CLIPSegVisionModel""",
"""CLIPSegForImageSegmentation""",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
|
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case = get_logger(__name__)
snake_case = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : str , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[int] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ):
for processor in self:
SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(processor.__call__ ).parameters
if len(UpperCAmelCase_ ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
f'''Make sure that all the required parameters: {list(function_args.keys() )} for '''
f'''{processor.__class__} are passed to the logits processor.''' )
SCREAMING_SNAKE_CASE : int = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , **UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : Dict = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : float ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not (temperature > 0):
raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}''' )
SCREAMING_SNAKE_CASE : Optional[int] = temperature
def __call__( self : List[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = scores / self.temperature
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : float , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (top_p < 0 or top_p > 1.0):
raise ValueError(f'''`top_p` has to be a float > 0 and < 1, but is {top_p}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (min_tokens_to_keep < 1):
raise ValueError(f'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}''' )
SCREAMING_SNAKE_CASE : Optional[int] = top_p
SCREAMING_SNAKE_CASE : str = filter_value
SCREAMING_SNAKE_CASE : List[str] = min_tokens_to_keep
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = lax.top_k(UpperCAmelCase_ , scores.shape[-1] )
SCREAMING_SNAKE_CASE : str = jnp.full_like(UpperCAmelCase_ , self.filter_value )
SCREAMING_SNAKE_CASE : Optional[int] = jax.nn.softmax(UpperCAmelCase_ , axis=-1 ).cumsum(axis=-1 )
SCREAMING_SNAKE_CASE : Tuple = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
SCREAMING_SNAKE_CASE : Optional[int] = jnp.roll(UpperCAmelCase_ , 1 )
score_mask |= score_mask.at[:, 0].set(UpperCAmelCase_ )
# min tokens to keep
SCREAMING_SNAKE_CASE : Union[str, Any] = score_mask.at[:, : self.min_tokens_to_keep].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = jnp.where(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jax.lax.sort_key_val(UpperCAmelCase_ , UpperCAmelCase_ )[-1]
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or top_k <= 0:
raise ValueError(f'''`top_k` has to be a strictly positive integer, but is {top_k}''' )
SCREAMING_SNAKE_CASE : List[str] = max(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = filter_value
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = scores.shape
SCREAMING_SNAKE_CASE : List[str] = jnp.full(batch_size * vocab_size , self.filter_value )
SCREAMING_SNAKE_CASE : List[str] = min(self.top_k , scores.shape[-1] ) # Safety check
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = lax.top_k(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = jnp.broadcast_to((jnp.arange(UpperCAmelCase_ ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
SCREAMING_SNAKE_CASE : List[str] = topk_scores.flatten()
SCREAMING_SNAKE_CASE : List[Any] = topk_indices.flatten() + shift
SCREAMING_SNAKE_CASE : Dict = next_scores_flat.at[topk_indices_flat].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = next_scores_flat.reshape(UpperCAmelCase_ , UpperCAmelCase_ )
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = bos_token_id
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.bool_(cur_len - 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.bos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = max_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : List[str] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : str = 1 - jnp.bool_(cur_len - self.max_length + 1 )
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.eos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or min_length < 0:
raise ValueError(f'''`min_length` has to be a positive integer, but is {min_length}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or eos_token_id < 0:
raise ValueError(f'''`eos_token_id` has to be a positive integer, but is {eos_token_id}''' )
SCREAMING_SNAKE_CASE : List[str] = min_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
# create boolean flag to decide if min length penalty should be applied
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
SCREAMING_SNAKE_CASE : Optional[int] = jnp.where(UpperCAmelCase_ , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = list(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = begin_index
def __call__( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Union[str, Any] = 1 - jnp.bool_(cur_len - self.begin_index )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(UpperCAmelCase_ , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : List[Any] = list(UpperCAmelCase_ )
def __call__( self : Any , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Tuple = scores.at[..., self.suppress_tokens].set(-float("inf" ) )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : List[Any] = dict(UpperCAmelCase_ )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
SCREAMING_SNAKE_CASE : Any = force_token_array.at[index].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = jnp.intaa(UpperCAmelCase_ )
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
def _force_token(UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : List[str] = scores.shape[0]
SCREAMING_SNAKE_CASE : Optional[int] = self.force_token_array[generation_idx]
SCREAMING_SNAKE_CASE : Tuple = jnp.ones_like(UpperCAmelCase_ , dtype=scores.dtype ) * -float("inf" )
SCREAMING_SNAKE_CASE : Dict = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
SCREAMING_SNAKE_CASE : Optional[Any] = lax.dynamic_update_slice(UpperCAmelCase_ , UpperCAmelCase_ , (0, current_token) )
return new_scores
SCREAMING_SNAKE_CASE : Any = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(UpperCAmelCase_ ) , lambda: scores , ) , )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Union[str, Any] = generate_config.eos_token_id
SCREAMING_SNAKE_CASE : Tuple = generate_config.no_timestamps_token_id
SCREAMING_SNAKE_CASE : List[Any] = generate_config.no_timestamps_token_id + 1
SCREAMING_SNAKE_CASE : Dict = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(UpperCAmelCase_ , "max_initial_timestamp_index" ):
SCREAMING_SNAKE_CASE : List[Any] = generate_config.max_initial_timestamp_index
else:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
if self.max_initial_timestamp_index is None:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
def __call__( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
# suppress <|notimestamps|> which is handled by without_timestamps
SCREAMING_SNAKE_CASE : int = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) )
def handle_pairs(UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) >= 1 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) < 2 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , UpperCAmelCase_ , UpperCAmelCase_ , )
return jnp.where(
UpperCAmelCase_ , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Optional[Any] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(cur_len == self.begin_index , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = self.timestamp_begin + self.max_initial_timestamp_index
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(
UpperCAmelCase_ , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , UpperCAmelCase_ , )
# if sum of probability over timestamps is above any other token, sample timestamp
SCREAMING_SNAKE_CASE : List[Any] = jax.nn.log_softmax(UpperCAmelCase_ , axis=-1 )
def handle_cumulative_probs(UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
return scores
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|
from __future__ import annotations
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = len(lowercase )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(lowercase ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , lowercase , lowercase , )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : list[list[str]] = []
depth_first_search([] , [] , [] , lowercase , lowercase )
# Print all the boards
for board in boards:
for column in board:
print(lowercase )
print("" )
print(len(lowercase ) , "solutions were found." )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
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|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
snake_case = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
print("""Torch version:""", None)
try:
import transformers
print("""transformers version:""", transformers.__version__)
except ImportError:
print("""transformers version:""", None)
| 319
| 1
|
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : int = '''new-model'''
if is_tf_available():
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = NewModelConfig
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Optional[int] = "bert-base-cased"
SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : List[str] = "bert-base-cased"
SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = TFAutoModelForPreTraining.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : List[str] ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForCausalLM.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Optional[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Dict = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : int ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Dict = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForMaskedLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Tuple ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
SCREAMING_SNAKE_CASE : Tuple = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = TFAutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : List[str] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModelForQuestionAnswering.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
@require_tensorflow_probability
def _A ( self : Optional[Any] ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForTableQuestionAnswering.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForTableQuestionAnswering.from_pretrained(
UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : List[str] = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCAmelCase_ ) , 1_4410 )
def _A ( self : int ):
SCREAMING_SNAKE_CASE : int = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCAmelCase_ ) , 1_4410 )
def _A ( self : int ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
SCREAMING_SNAKE_CASE : Tuple = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny" )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = copy.deepcopy(model.config )
SCREAMING_SNAKE_CASE : Optional[Any] = ["FunnelBaseModel"]
SCREAMING_SNAKE_CASE : str = TFAutoModel.from_config(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = TFAutoModel.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Optional[int] ):
try:
AutoConfig.register("new-model" , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(UpperCAmelCase_ ):
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_ )
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCAmelCase_ ):
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE : Tuple = BertModelTester(self ).get_config()
SCREAMING_SNAKE_CASE : Optional[int] = NewModelConfig(**tiny_config.to_dict() )
SCREAMING_SNAKE_CASE : Optional[Any] = auto_class.from_config(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = auto_class.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def _A ( self : Optional[int] ):
with self.assertRaisesRegex(
UpperCAmelCase_ , "bert-base is not a local folder and is not a valid model identifier" ):
SCREAMING_SNAKE_CASE : Dict = TFAutoModel.from_pretrained("bert-base" )
def _A ( self : Dict ):
with self.assertRaisesRegex(
UpperCAmelCase_ , r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModel.from_pretrained(UpperCAmelCase_ , revision="aaaaaa" )
def _A ( self : Tuple ):
with self.assertRaisesRegex(
UpperCAmelCase_ , "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin" , ):
SCREAMING_SNAKE_CASE : Dict = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model" )
def _A ( self : Optional[int] ):
with self.assertRaisesRegex(UpperCAmelCase_ , "Use `from_pt=True` to load this model" ):
SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only" )
def _A ( self : Tuple ):
# Make sure we have cached the model.
SCREAMING_SNAKE_CASE : int = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert" )
with RequestCounter() as counter:
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
SCREAMING_SNAKE_CASE : Any = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded" )
with RequestCounter() as counter:
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
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# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
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import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = '''Wav2Vec2FeatureExtractor'''
UpperCamelCase_ : Any = '''AutoTokenizer'''
def __init__( self : List[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = self.feature_extractor
SCREAMING_SNAKE_CASE : Dict = False
@classmethod
def _A ( cls : Optional[Any] , UpperCAmelCase_ : str , **UpperCAmelCase_ : Dict ):
try:
return super().from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
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: " , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
return cls(feature_extractor=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ )
def __call__( self : Optional[Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*UpperCAmelCase_ , **UpperCAmelCase_ )
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
SCREAMING_SNAKE_CASE : List[str] = kwargs.pop("raw_speech" )
else:
SCREAMING_SNAKE_CASE : List[str] = kwargs.pop("audio" , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = kwargs.pop("sampling_rate" , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = kwargs.pop("text" , UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
SCREAMING_SNAKE_CASE : int = args[0]
SCREAMING_SNAKE_CASE : List[Any] = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
SCREAMING_SNAKE_CASE : str = self.feature_extractor(UpperCAmelCase_ , *UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer(UpperCAmelCase_ , **UpperCAmelCase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
SCREAMING_SNAKE_CASE : Optional[Any] = encodings["input_ids"]
return inputs
def _A ( self : Union[str, Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Dict ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = kwargs.pop("input_features" , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = kwargs.pop("labels" , UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
SCREAMING_SNAKE_CASE : Optional[int] = args[0]
SCREAMING_SNAKE_CASE : int = args[1:]
if input_features is not None:
SCREAMING_SNAKE_CASE : List[str] = self.feature_extractor.pad(UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
if labels is not None:
SCREAMING_SNAKE_CASE : str = self.tokenizer.pad(UpperCAmelCase_ , **UpperCAmelCase_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
SCREAMING_SNAKE_CASE : str = labels["input_ids"]
return input_features
def _A ( self : Dict , *UpperCAmelCase_ : str , **UpperCAmelCase_ : List[Any] ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : Optional[Any] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : str ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@contextmanager
def _A ( self : Union[str, Any] ):
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 : Union[str, Any] = True
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer
yield
SCREAMING_SNAKE_CASE : Dict = self.feature_extractor
SCREAMING_SNAKE_CASE : int = False
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import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
snake_case = {
"""b0""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1_408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1_536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1_792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2_304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2_560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = EfficientNetConfig()
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["hidden_dim"]
SCREAMING_SNAKE_CASE : Tuple = CONFIG_MAP[model_name]["width_coef"]
SCREAMING_SNAKE_CASE : Optional[int] = CONFIG_MAP[model_name]["depth_coef"]
SCREAMING_SNAKE_CASE : Union[str, Any] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = CONFIG_MAP[model_name]["dropout_rate"]
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["dw_padding"]
SCREAMING_SNAKE_CASE : str = "huggingface/label-files"
SCREAMING_SNAKE_CASE : str = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE : str = 1000
SCREAMING_SNAKE_CASE : List[Any] = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE : Tuple = {int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Union[str, Any] = idalabel
SCREAMING_SNAKE_CASE : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE : List[Any] = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : int = EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=lowercase , )
return preprocessor
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
SCREAMING_SNAKE_CASE : List[str] = sorted(set(lowercase ) )
SCREAMING_SNAKE_CASE : List[str] = len(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = {b: str(lowercase ) for b, i in zip(lowercase , range(lowercase ) )}
SCREAMING_SNAKE_CASE : Dict = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
SCREAMING_SNAKE_CASE : Tuple = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
SCREAMING_SNAKE_CASE : int = {}
for item in rename_keys:
if item[0] in original_param_names:
SCREAMING_SNAKE_CASE : Any = "efficientnet." + item[1]
SCREAMING_SNAKE_CASE : Optional[Any] = "classifier.weight"
SCREAMING_SNAKE_CASE : List[str] = "classifier.bias"
return key_mapping
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
SCREAMING_SNAKE_CASE : str = key_mapping[key]
if "_conv" in key and "kernel" in key:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
SCREAMING_SNAKE_CASE : int = torch.from_numpy(lowercase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
SCREAMING_SNAKE_CASE : List[str] = torch.from_numpy(np.transpose(lowercase ) )
else:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(lowercase )
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = model_classes[model_name](
include_top=lowercase , weights="imagenet" , input_tensor=lowercase , input_shape=lowercase , pooling=lowercase , classes=1000 , classifier_activation="softmax" , )
SCREAMING_SNAKE_CASE : List[Any] = original_model.trainable_variables
SCREAMING_SNAKE_CASE : Dict = original_model.non_trainable_variables
SCREAMING_SNAKE_CASE : Dict = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
SCREAMING_SNAKE_CASE : Tuple = param.numpy()
SCREAMING_SNAKE_CASE : Tuple = list(tf_params.keys() )
# Load HuggingFace model
SCREAMING_SNAKE_CASE : Tuple = get_efficientnet_config(lowercase )
SCREAMING_SNAKE_CASE : str = EfficientNetForImageClassification(lowercase ).eval()
SCREAMING_SNAKE_CASE : Dict = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
SCREAMING_SNAKE_CASE : Dict = rename_keys(lowercase )
replace_params(lowercase , lowercase , lowercase )
# Initialize preprocessor and preprocess input image
SCREAMING_SNAKE_CASE : Optional[int] = convert_image_processor(lowercase )
SCREAMING_SNAKE_CASE : int = preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = hf_model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.logits.detach().numpy()
# Original model inference
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
SCREAMING_SNAKE_CASE : Tuple = image.img_to_array(lowercase )
SCREAMING_SNAKE_CASE : Tuple = np.expand_dims(lowercase , axis=0 )
SCREAMING_SNAKE_CASE : Any = original_model.predict(lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(lowercase , lowercase , atol=1E-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(lowercase ):
os.mkdir(lowercase )
# Save converted model and image processor
hf_model.save_pretrained(lowercase )
preprocessor.save_pretrained(lowercase )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(lowercase )
hf_model.push_to_hub(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
snake_case = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
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import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : Optional[int] = inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE : List[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
SCREAMING_SNAKE_CASE : List[Any] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["scripts", "test_distributed_data_loop.py"] )
SCREAMING_SNAKE_CASE : List[str] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_ops.py"] )
@require_multi_gpu
def _A ( self : Any ):
print(f'''Found {torch.cuda.device_count()} devices.''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = ["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCAmelCase_ , env=os.environ.copy() )
@require_multi_gpu
def _A ( self : str ):
print(f'''Found {torch.cuda.device_count()} devices.''' )
SCREAMING_SNAKE_CASE : List[Any] = ["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(f'''Command: {cmd}''' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCAmelCase_ , env=os.environ.copy() )
@require_multi_gpu
def _A ( self : List[str] ):
SCREAMING_SNAKE_CASE : List[Any] = ["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCAmelCase_ , env=os.environ.copy() )
@require_multi_gpu
def _A ( self : Union[str, Any] ):
print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = ["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices="0,1" ):
execute_subprocess_async(UpperCAmelCase_ , env=os.environ.copy() )
if __name__ == "__main__":
snake_case = Accelerator()
snake_case = (accelerator.state.process_index + 2, 10)
snake_case = torch.randint(0, 10, shape).to(accelerator.device)
snake_case = """"""
snake_case = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
snake_case = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
snake_case = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
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def lowerCamelCase__ ( ):
"""simple docstring"""
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
snake_case = generate_large_matrix()
snake_case = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
assert all(row == sorted(lowercase , reverse=lowercase ) for row in grid )
assert all(list(lowercase ) == sorted(lowercase , reverse=lowercase ) for col in zip(*lowercase ) )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowercase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
SCREAMING_SNAKE_CASE : List[Any] = (left + right) // 2
SCREAMING_SNAKE_CASE : Optional[int] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
SCREAMING_SNAKE_CASE : List[Any] = mid + 1
else:
SCREAMING_SNAKE_CASE : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : List[str] = len(grid[0] )
for i in range(len(lowercase ) ):
SCREAMING_SNAKE_CASE : Any = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowercase ) * len(grid[0] )) - total
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return len([number for row in grid for number in row if number < 0] )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = 0
for row in grid:
for i, number in enumerate(lowercase ):
if number < 0:
total += len(lowercase ) - i
break
return total
def lowerCamelCase__ ( ):
"""simple docstring"""
from timeit import timeit
print("Running benchmarks" )
SCREAMING_SNAKE_CASE : List[str] = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
SCREAMING_SNAKE_CASE : Union[str, Any] = timeit(F'''{func}(grid=grid)''' , setup=lowercase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
snake_case = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
print("""Torch version:""", None)
try:
import transformers
print("""transformers version:""", transformers.__version__)
except ImportError:
print("""transformers version:""", None)
| 319
|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case = ["""small""", """medium""", """large"""]
snake_case = """lm_head.decoder.weight"""
snake_case = """lm_head.weight"""
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = torch.load(lowercase )
SCREAMING_SNAKE_CASE : Any = d.pop(lowercase )
os.makedirs(lowercase , exist_ok=lowercase )
torch.save(lowercase , os.path.join(lowercase , lowercase ) )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
snake_case = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
snake_case = os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
snake_case = F"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 319
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|
import pickle
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : int=0.2 , UpperCAmelCase_ : Any=0.2 ):
SCREAMING_SNAKE_CASE : Optional[int] = bp_numa
SCREAMING_SNAKE_CASE : Optional[int] = bp_numa
SCREAMING_SNAKE_CASE : Optional[Any] = bp_numa
SCREAMING_SNAKE_CASE : List[Any] = conva_get[:2]
SCREAMING_SNAKE_CASE : Dict = conva_get[2]
SCREAMING_SNAKE_CASE : List[str] = size_pa
SCREAMING_SNAKE_CASE : List[Any] = rate_w
SCREAMING_SNAKE_CASE : List[str] = rate_t
SCREAMING_SNAKE_CASE : Any = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
SCREAMING_SNAKE_CASE : Union[str, Any] = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
SCREAMING_SNAKE_CASE : List[Any] = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
SCREAMING_SNAKE_CASE : List[Any] = -2 * np.random.rand(self.conva[1] ) + 1
SCREAMING_SNAKE_CASE : List[str] = -2 * np.random.rand(self.num_bpa ) + 1
SCREAMING_SNAKE_CASE : str = -2 * np.random.rand(self.num_bpa ) + 1
def _A ( self : int , UpperCAmelCase_ : List[str] ):
# save model dict with pickle
SCREAMING_SNAKE_CASE : List[Any] = {
"num_bp1": self.num_bpa,
"num_bp2": self.num_bpa,
"num_bp3": self.num_bpa,
"conv1": self.conva,
"step_conv1": self.step_conva,
"size_pooling1": self.size_poolinga,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conva,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conva,
"thre_bp2": self.thre_bpa,
"thre_bp3": self.thre_bpa,
}
with open(UpperCAmelCase_ , "wb" ) as f:
pickle.dump(UpperCAmelCase_ , UpperCAmelCase_ )
print(f'''Model saved： {save_path}''' )
@classmethod
def _A ( cls : int , UpperCAmelCase_ : Union[str, Any] ):
# read saved model
with open(UpperCAmelCase_ , "rb" ) as f:
SCREAMING_SNAKE_CASE : List[Any] = pickle.load(UpperCAmelCase_ ) # noqa: S301
SCREAMING_SNAKE_CASE : Optional[Any] = model_dic.get("conv1" )
conv_get.append(model_dic.get("step_conv1" ) )
SCREAMING_SNAKE_CASE : Optional[int] = model_dic.get("size_pooling1" )
SCREAMING_SNAKE_CASE : List[str] = model_dic.get("num_bp1" )
SCREAMING_SNAKE_CASE : Optional[Any] = model_dic.get("num_bp2" )
SCREAMING_SNAKE_CASE : Any = model_dic.get("num_bp3" )
SCREAMING_SNAKE_CASE : Dict = model_dic.get("rate_weight" )
SCREAMING_SNAKE_CASE : Optional[Any] = model_dic.get("rate_thre" )
# create model instance
SCREAMING_SNAKE_CASE : List[str] = CNN(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# modify model parameter
SCREAMING_SNAKE_CASE : Dict = model_dic.get("w_conv1" )
SCREAMING_SNAKE_CASE : List[str] = model_dic.get("wkj" )
SCREAMING_SNAKE_CASE : str = model_dic.get("vji" )
SCREAMING_SNAKE_CASE : Optional[int] = model_dic.get("thre_conv1" )
SCREAMING_SNAKE_CASE : Dict = model_dic.get("thre_bp2" )
SCREAMING_SNAKE_CASE : Union[str, Any] = model_dic.get("thre_bp3" )
return conv_ins
def _A ( self : Any , UpperCAmelCase_ : Any ):
return 1 / (1 + np.exp(-1 * x ))
def _A ( self : List[str] , UpperCAmelCase_ : int ):
return round(UpperCAmelCase_ , 3 )
def _A ( self : List[str] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Dict ):
# convolution process
SCREAMING_SNAKE_CASE : Tuple = convs[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = convs[1]
SCREAMING_SNAKE_CASE : List[str] = np.shape(UpperCAmelCase_ )[0]
# get the data slice of original image data, data_focus
SCREAMING_SNAKE_CASE : Optional[Any] = []
for i_focus in range(0 , size_data - size_conv + 1 , UpperCAmelCase_ ):
for j_focus in range(0 , size_data - size_conv + 1 , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : str = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(UpperCAmelCase_ )
# calculate the feature map of every single kernel, and saved as list of matrix
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : List[Any] = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Dict = []
for i_focus in range(len(UpperCAmelCase_ ) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : List[Any] = np.asmatrix(UpperCAmelCase_ ).reshape(
UpperCAmelCase_ , UpperCAmelCase_ )
data_featuremap.append(UpperCAmelCase_ )
# expanding the data slice to One dimenssion
SCREAMING_SNAKE_CASE : List[Any] = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Dict = np.asarray(UpperCAmelCase_ )
return focus_list, data_featuremap
def _A ( self : str , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any]="average_pool" ):
# pooling process
SCREAMING_SNAKE_CASE : List[str] = len(featuremaps[0] )
SCREAMING_SNAKE_CASE : Dict = int(size_map / size_pooling )
SCREAMING_SNAKE_CASE : int = []
for i_map in range(len(UpperCAmelCase_ ) ):
SCREAMING_SNAKE_CASE : Any = featuremaps[i_map]
SCREAMING_SNAKE_CASE : Dict = []
for i_focus in range(0 , UpperCAmelCase_ , UpperCAmelCase_ ):
for j_focus in range(0 , UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Any = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(UpperCAmelCase_ ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Tuple = np.asmatrix(UpperCAmelCase_ ).reshape(UpperCAmelCase_ , UpperCAmelCase_ )
featuremap_pooled.append(UpperCAmelCase_ )
return featuremap_pooled
def _A ( self : Optional[int] , UpperCAmelCase_ : Optional[Any] ):
# expanding three dimension data to one dimension list
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for i in range(len(UpperCAmelCase_ ) ):
SCREAMING_SNAKE_CASE : str = np.shape(data[i] )
SCREAMING_SNAKE_CASE : Tuple = data[i].reshape(1 , shapes[0] * shapes[1] )
SCREAMING_SNAKE_CASE : str = data_listed.getA().tolist()[0]
data_expanded.extend(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = np.asarray(UpperCAmelCase_ )
return data_expanded
def _A ( self : Optional[int] , UpperCAmelCase_ : Optional[Any] ):
# expanding matrix to one dimension list
SCREAMING_SNAKE_CASE : Dict = np.asarray(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = np.shape(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def _A ( self : Optional[int] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Dict ):
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Tuple = 0
for i_map in range(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = np.ones((size_map, size_map) )
for i in range(0 , UpperCAmelCase_ , UpperCAmelCase_ ):
for j in range(0 , UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = pd_pool[
i_pool
]
SCREAMING_SNAKE_CASE : List[Any] = i_pool + 1
SCREAMING_SNAKE_CASE : Any = np.multiply(
UpperCAmelCase_ , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(UpperCAmelCase_ )
return pd_all
def _A ( self : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[str]=bool ):
# model traning
print("----------------------Start Training-------------------------" )
print((" - - Shape: Train_Data ", np.shape(UpperCAmelCase_ )) )
print((" - - Shape: Teach_Data ", np.shape(UpperCAmelCase_ )) )
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : Optional[int] = 1_0000
while rp < n_repeat and mse >= error_accuracy:
SCREAMING_SNAKE_CASE : Optional[int] = 0
print(f'''-------------Learning Time {rp}--------------''' )
for p in range(len(UpperCAmelCase_ ) ):
# print('------------Learning Image: %d--------------'%p)
SCREAMING_SNAKE_CASE : Dict = np.asmatrix(datas_train[p] )
SCREAMING_SNAKE_CASE : List[str] = np.asarray(datas_teach[p] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = self.convolute(
UpperCAmelCase_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
SCREAMING_SNAKE_CASE : Tuple = self.pooling(UpperCAmelCase_ , self.size_poolinga )
SCREAMING_SNAKE_CASE : str = np.shape(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self._expand(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = data_bp_input
SCREAMING_SNAKE_CASE : List[Any] = np.dot(UpperCAmelCase_ , self.vji.T ) - self.thre_bpa
SCREAMING_SNAKE_CASE : Dict = self.sig(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = np.dot(UpperCAmelCase_ , self.wkj.T ) - self.thre_bpa
SCREAMING_SNAKE_CASE : Any = self.sig(UpperCAmelCase_ )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
SCREAMING_SNAKE_CASE : Dict = np.multiply(
(data_teach - bp_outa) , np.multiply(UpperCAmelCase_ , (1 - bp_outa) ) )
SCREAMING_SNAKE_CASE : Any = np.multiply(
np.dot(UpperCAmelCase_ , self.wkj ) , np.multiply(UpperCAmelCase_ , (1 - bp_outa) ) )
SCREAMING_SNAKE_CASE : Optional[Any] = np.dot(UpperCAmelCase_ , self.vji )
SCREAMING_SNAKE_CASE : Any = pd_i_all / (self.size_poolinga * self.size_poolinga)
SCREAMING_SNAKE_CASE : int = pd_conva_pooled.T.getA().tolist()
SCREAMING_SNAKE_CASE : int = self._calculate_gradient_from_pool(
UpperCAmelCase_ , UpperCAmelCase_ , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
SCREAMING_SNAKE_CASE : Optional[Any] = self._expand_mat(pd_conva_all[k_conv] )
SCREAMING_SNAKE_CASE : List[Any] = self.rate_weight * np.dot(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
SCREAMING_SNAKE_CASE : int = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
SCREAMING_SNAKE_CASE : Union[str, Any] = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
SCREAMING_SNAKE_CASE : Union[str, Any] = self.vji + pd_j_all.T * bp_outa * self.rate_weight
SCREAMING_SNAKE_CASE : Any = self.thre_bpa - pd_k_all * self.rate_thre
SCREAMING_SNAKE_CASE : List[str] = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
SCREAMING_SNAKE_CASE : Optional[int] = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
SCREAMING_SNAKE_CASE : str = rp + 1
SCREAMING_SNAKE_CASE : Optional[int] = error_count / patterns
all_mse.append(UpperCAmelCase_ )
def draw_error():
SCREAMING_SNAKE_CASE : Tuple = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(UpperCAmelCase_ , "+-" )
plt.plot(UpperCAmelCase_ , "r--" )
plt.xlabel("Learning Times" )
plt.ylabel("All_mse" )
plt.grid(UpperCAmelCase_ , alpha=0.5 )
plt.show()
print("------------------Training Complished---------------------" )
print((" - - Training epoch: ", rp, f''' - - Mse: {mse:.6f}''') )
if draw_e:
draw_error()
return mse
def _A ( self : Optional[Any] , UpperCAmelCase_ : Optional[Any] ):
# model predict
SCREAMING_SNAKE_CASE : List[str] = []
print("-------------------Start Testing-------------------------" )
print((" - - Shape: Test_Data ", np.shape(UpperCAmelCase_ )) )
for p in range(len(UpperCAmelCase_ ) ):
SCREAMING_SNAKE_CASE : Optional[Any] = np.asmatrix(datas_test[p] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = self.convolute(
UpperCAmelCase_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.pooling(UpperCAmelCase_ , self.size_poolinga )
SCREAMING_SNAKE_CASE : Tuple = self._expand(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = data_bp_input
SCREAMING_SNAKE_CASE : Dict = bp_outa * self.vji.T - self.thre_bpa
SCREAMING_SNAKE_CASE : Any = self.sig(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = bp_outa * self.wkj.T - self.thre_bpa
SCREAMING_SNAKE_CASE : Tuple = self.sig(UpperCAmelCase_ )
produce_out.extend(bp_outa.getA().tolist() )
SCREAMING_SNAKE_CASE : List[Any] = [list(map(self.do_round , UpperCAmelCase_ ) ) for each in produce_out]
return np.asarray(UpperCAmelCase_ )
def _A ( self : Optional[int] , UpperCAmelCase_ : int ):
# return the data of image after convoluting process so we can check it out
SCREAMING_SNAKE_CASE : str = np.asmatrix(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = self.convolute(
UpperCAmelCase_ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
SCREAMING_SNAKE_CASE : Optional[Any] = self.pooling(UpperCAmelCase_ , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 319
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""MLukeTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
assert (
isinstance(lowercase , lowercase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = 1, 1
for _ in range(number_of_steps - 1 ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
|
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase__ ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 319
| 1
|
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : Union[str, Any] = set_counts
SCREAMING_SNAKE_CASE : Any = max(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = [1] * num_sets
SCREAMING_SNAKE_CASE : List[str] = list(range(UpperCAmelCase_ ) )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[Any] = self.get_parent(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self.get_parent(UpperCAmelCase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
SCREAMING_SNAKE_CASE : List[str] = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Tuple = src_parent
SCREAMING_SNAKE_CASE : Optional[int] = self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Optional[Any] = max(self.max_set , UpperCAmelCase_ )
return True
def _A ( self : Tuple , UpperCAmelCase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
SCREAMING_SNAKE_CASE : Tuple = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 319
|
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : Union[str, Any] = set_counts
SCREAMING_SNAKE_CASE : Any = max(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = [1] * num_sets
SCREAMING_SNAKE_CASE : List[str] = list(range(UpperCAmelCase_ ) )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[Any] = self.get_parent(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self.get_parent(UpperCAmelCase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
SCREAMING_SNAKE_CASE : List[str] = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Tuple = src_parent
SCREAMING_SNAKE_CASE : Optional[int] = self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Optional[Any] = max(self.max_set , UpperCAmelCase_ )
return True
def _A ( self : Tuple , UpperCAmelCase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
SCREAMING_SNAKE_CASE : Tuple = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 319
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""UperNetForSemanticSegmentation""",
"""UperNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''timm_backbone'''
def __init__( self : List[Any] , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : List[str]=3 , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : Union[str, Any]=None , **UpperCAmelCase_ : Optional[Any] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = backbone
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = features_only
SCREAMING_SNAKE_CASE : Dict = use_pretrained_backbone
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : List[Any] = out_indices if out_indices is not None else (-1,)
| 319
| 1
|
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = ['''torch''']
def __init__( self : Tuple , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = ['''torch''']
def __init__( self : Tuple , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = ['''torch''']
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[int] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = ['''torch''']
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ['''torch''']
def __init__( self : int , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = ['''torch''']
def __init__( self : Tuple , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : int , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''torch''']
def __init__( self : Dict , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = ['''torch''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : List[Any] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = ['''torch''']
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["torch"] )
def lowerCamelCase__ ( *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(lowercase , ["torch"] )
def lowerCamelCase__ ( *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(lowercase , ["torch"] )
def lowerCamelCase__ ( *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(lowercase , ["torch"] )
def lowerCamelCase__ ( *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(lowercase , ["torch"] )
def lowerCamelCase__ ( *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(lowercase , ["torch"] )
def lowerCamelCase__ ( *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(lowercase , ["torch"] )
def lowerCamelCase__ ( *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(lowercase , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ['''torch''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[Any] = ['''torch''']
def __init__( self : List[Any] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''torch''']
def __init__( self : Dict , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : List[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[int] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : Dict , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : str ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ['''torch''']
def __init__( self : Dict , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = ['''torch''']
def __init__( self : Optional[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : str = ['''torch''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : List[Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''torch''']
def __init__( self : List[str] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''torch''']
def __init__( self : Tuple , *UpperCAmelCase_ : str , **UpperCAmelCase_ : List[str] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : Dict , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : str ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[Any] = ['''torch''']
def __init__( self : int , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : List[Any] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = ['''torch''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : str = ['''torch''']
def __init__( self : List[Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = ['''torch''']
def __init__( self : Dict , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[Any] = ['''torch''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : str ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''torch''']
def __init__( self : List[Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[str] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : str ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : int ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ['''torch''']
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[int] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : int = ['''torch''']
def __init__( self : str , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : List[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''torch''']
def __init__( self : Tuple , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = ['''torch''']
def __init__( self : Dict , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = ['''torch''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : str ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ['''torch''']
def __init__( self : Dict , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Optional[int] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : int = ['''torch''']
def __init__( self : List[Any] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[Any] = ['''torch''']
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = ['''torch''']
def __init__( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[Any] = ['''torch''']
def __init__( self : int , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : str ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : str = ['''torch''']
def __init__( self : Tuple , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : str , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = ['''torch''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[int] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''torch''']
def __init__( self : Tuple , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Any ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : str , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["torch"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''torch''']
def __init__( self : Any , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(self , ["torch"] )
@classmethod
def _A ( cls : List[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["torch"] )
@classmethod
def _A ( cls : List[Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["torch"] )
| 319
|
from math import sqrt
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for i in range(1 , int(sqrt(lowercase ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase ):
total += i + n // i
elif i == sqrt(lowercase ):
total += i
return total - n
def lowerCamelCase__ ( lowercase = 10000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = sum(
i
for i in range(1 , lowercase )
if sum_of_divisors(sum_of_divisors(lowercase ) ) == i and sum_of_divisors(lowercase ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 319
| 1
|
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(lowercase ):
for j in range(lowercase ):
if dist[i][j] != float("inf" ):
print(int(dist[i][j] ) , end="\t" )
else:
print("INF" , end="\t" )
print()
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = [[float("inf" ) for _ in range(lowercase )] for _ in range(lowercase )]
for i in range(lowercase ):
for j in range(lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(lowercase ):
# looping through rows of graph array
for i in range(lowercase ):
# looping through columns of graph array
for j in range(lowercase ):
if (
dist[i][k] != float("inf" )
and dist[k][j] != float("inf" )
and dist[i][k] + dist[k][j] < dist[i][j]
):
SCREAMING_SNAKE_CASE : Optional[int] = dist[i][k] + dist[k][j]
_print_dist(lowercase , lowercase )
return dist, v
if __name__ == "__main__":
snake_case = int(input("""Enter number of vertices: """))
snake_case = int(input("""Enter number of edges: """))
snake_case = [[float("""inf""") for i in range(v)] for j in range(v)]
for i in range(v):
snake_case = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("""\nEdge """, i + 1)
snake_case = int(input("""Enter source:"""))
snake_case = int(input("""Enter destination:"""))
snake_case = float(input("""Enter weight:"""))
snake_case = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 319
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EncodecModel""",
"""EncodecPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
snake_case = False
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _A ( self : List[str] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : Optional[int] = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = pipe.dual_guided(
prompt="first prompt" , image=UpperCAmelCase_ , text_to_image_strength=0.75 , generator=UpperCAmelCase_ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase_ , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = generator.manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = pipe.dual_guided(
prompt="first prompt" , image=UpperCAmelCase_ , text_to_image_strength=0.75 , generator=UpperCAmelCase_ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def _A ( self : int ):
SCREAMING_SNAKE_CASE : Union[str, Any] = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = "cyberpunk 2077"
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
SCREAMING_SNAKE_CASE : Optional[int] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe.dual_guided(
prompt=UpperCAmelCase_ , image=UpperCAmelCase_ , text_to_image_strength=0.75 , generator=UpperCAmelCase_ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" , ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : List[Any] = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
SCREAMING_SNAKE_CASE : Dict = "A painting of a squirrel eating a burger "
SCREAMING_SNAKE_CASE : int = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[str] = pipe.text_to_image(
prompt=UpperCAmelCase_ , generator=UpperCAmelCase_ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : Optional[Any] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
SCREAMING_SNAKE_CASE : Dict = pipe.image_variation(UpperCAmelCase_ , generator=UpperCAmelCase_ , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : Tuple = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 319
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
snake_case = None
snake_case = logging.get_logger(__name__)
snake_case = """▁"""
snake_case = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
snake_case = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
snake_case = {
"""google/pegasus-xsum""": 512,
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES
UpperCamelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : int = PegasusTokenizer
UpperCamelCase_ : str = ['''input_ids''', '''attention_mask''']
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Optional[int]="<pad>" , UpperCAmelCase_ : int="</s>" , UpperCAmelCase_ : str="<unk>" , UpperCAmelCase_ : str="<mask_2>" , UpperCAmelCase_ : Optional[int]="<mask_1>" , UpperCAmelCase_ : int=None , UpperCAmelCase_ : str=103 , **UpperCAmelCase_ : Optional[int] , ):
SCREAMING_SNAKE_CASE : Optional[Any] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError(
f'''additional_special_tokens should be of type {type(UpperCAmelCase_ )}, but is'''
f''' {type(UpperCAmelCase_ )}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(UpperCAmelCase_ ) , self.offset - 1 )
]
if len(set(UpperCAmelCase_ ) ) != len(UpperCAmelCase_ ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
SCREAMING_SNAKE_CASE : int = additional_special_tokens_extended
else:
SCREAMING_SNAKE_CASE : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
super().__init__(
UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , mask_token_sent=UpperCAmelCase_ , offset=UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : str = vocab_file
SCREAMING_SNAKE_CASE : str = False if not self.vocab_file else True
def _A ( self : Optional[Any] , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Optional[int] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
f''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : int , UpperCAmelCase_ : List , UpperCAmelCase_ : Optional[List] = None , UpperCAmelCase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(UpperCAmelCase_ )
elif token_ids_a is None:
return self._special_token_mask(UpperCAmelCase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Any=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : str , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
UpperCAmelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase_ ):
copyfile(self.vocab_file , UpperCAmelCase_ )
return (out_vocab_file,)
| 319
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|
import datasets
snake_case = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin\",
title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing\",
year = \"2018\",
publisher = \"Association for Computational Linguistics\",
location = \"Brussels, Belgium\",
}
"""
snake_case = """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""
snake_case = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
Examples:
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> xnli_metric = datasets.load_metric(\"xnli\")
>>> results = xnli_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
"""
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def _A ( self : Optional[int] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32" ),
"references": datasets.Value("int64" if self.config_name != "sts-b" else "float32" ),
} ) , codebase_urls=[] , reference_urls=[] , format="numpy" , )
def _A ( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[Any] ):
return {"accuracy": simple_accuracy(UpperCAmelCase_ , UpperCAmelCase_ )}
| 319
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""SpeechEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""FlaxSpeechEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@require_torch
def _A ( self : int ):
SCREAMING_SNAKE_CASE : Union[str, Any] = pipeline(
task="zero-shot-audio-classification" , model="hf-internal-testing/tiny-clap-htsat-unfused" )
SCREAMING_SNAKE_CASE : Dict = load_dataset("ashraq/esc50" )
SCREAMING_SNAKE_CASE : Any = dataset["train"]["audio"][-1]["array"]
SCREAMING_SNAKE_CASE : Optional[int] = audio_classifier(UpperCAmelCase_ , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [{"score": 0.501, "label": "Sound of a dog"}, {"score": 0.499, "label": "Sound of vaccum cleaner"}] , )
@unittest.skip("No models are available in TF" )
def _A ( self : int ):
pass
@slow
@require_torch
def _A ( self : str ):
SCREAMING_SNAKE_CASE : str = pipeline(
task="zero-shot-audio-classification" , model="laion/clap-htsat-unfused" , )
# This is an audio of a dog
SCREAMING_SNAKE_CASE : List[str] = load_dataset("ashraq/esc50" )
SCREAMING_SNAKE_CASE : Tuple = dataset["train"]["audio"][-1]["array"]
SCREAMING_SNAKE_CASE : Dict = audio_classifier(UpperCAmelCase_ , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
] , )
SCREAMING_SNAKE_CASE : Union[str, Any] = audio_classifier([audio] * 5 , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5 , )
SCREAMING_SNAKE_CASE : Union[str, Any] = audio_classifier(
[audio] * 5 , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] , batch_size=5 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , [
[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5 , )
@unittest.skip("No models are available in TF" )
def _A ( self : Union[str, Any] ):
pass
| 319
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
snake_case = 16
snake_case = 32
def lowerCamelCase__ ( lowercase , lowercase = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained("bert-base-cased" )
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset("glue" , "mrpc" )
def tokenize_function(lowercase ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=lowercase , max_length=lowercase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE : List[Any] = datasets.map(
lowercase , batched=lowercase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE : Tuple = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE : str = 16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE : Optional[Any] = 8
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = None
return tokenizer.pad(
lowercase , padding="longest" , max_length=lowercase , pad_to_multiple_of=lowercase , return_tensors="pt" , )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE : Optional[int] = DataLoader(
tokenized_datasets["train"] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
SCREAMING_SNAKE_CASE : Dict = DataLoader(
tokenized_datasets["validation"] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
snake_case = mocked_dataloaders # noqa: F811
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , lowercase ) == "1":
SCREAMING_SNAKE_CASE : int = 2
# New Code #
SCREAMING_SNAKE_CASE : Union[str, Any] = int(args.gradient_accumulation_steps )
# Initialize accelerator
SCREAMING_SNAKE_CASE : Tuple = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE : Any = config["lr"]
SCREAMING_SNAKE_CASE : Optional[Any] = int(config["num_epochs"] )
SCREAMING_SNAKE_CASE : List[Any] = int(config["seed"] )
SCREAMING_SNAKE_CASE : Union[str, Any] = int(config["batch_size"] )
SCREAMING_SNAKE_CASE : Optional[Any] = evaluate.load("glue" , "mrpc" )
set_seed(lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = get_dataloaders(lowercase , lowercase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE : List[Any] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=lowercase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE : Any = model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE : Any = AdamW(params=model.parameters() , lr=lowercase )
# Instantiate scheduler
SCREAMING_SNAKE_CASE : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=lowercase , num_warmup_steps=100 , num_training_steps=(len(lowercase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = accelerator.prepare(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Now we train the model
for epoch in range(lowercase ):
model.train()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase ):
SCREAMING_SNAKE_CASE : Any = model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = output.loss
accelerator.backward(lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[Any] = model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=lowercase , references=lowercase , )
SCREAMING_SNAKE_CASE : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowercase )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=lowercase , default=lowercase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=lowercase , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
SCREAMING_SNAKE_CASE : Dict = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(lowercase , lowercase )
if __name__ == "__main__":
main()
| 319
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""VisionEncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""TFVisionEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""FlaxVisionEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
|
import functools
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ) or not all(isinstance(lowercase , lowercase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(lowercase ) != 3 or not all(isinstance(lowercase , lowercase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(lowercase ) == 0:
return 0
if min(lowercase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(lowercase ) >= 366:
raise ValueError("All days elements should be less than 366" )
SCREAMING_SNAKE_CASE : Dict = set(lowercase )
@functools.cache
def dynamic_programming(lowercase ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
| 1
|
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
snake_case = """<<<<<<< This should probably be modified because it mentions: """
snake_case = """=======
>>>>>>>
"""
snake_case = [
"""TextEncoderConfig""",
"""ByteTextEncoder""",
"""SubwordTextEncoder""",
"""encoder_config""",
"""maybe_build_from_corpus""",
"""manual_dir""",
]
snake_case = [
# (pattern, replacement)
# Order is important here for some replacements
(r"""tfds\.core""", r"""datasets"""),
(r"""tf\.io\.gfile\.GFile""", r"""open"""),
(r"""tf\.([\w\d]+)""", r"""datasets.Value('\1')"""),
(r"""tfds\.features\.Text\(\)""", r"""datasets.Value('string')"""),
(r"""tfds\.features\.Text\(""", r"""datasets.Value('string'),"""),
(r"""features\s*=\s*tfds.features.FeaturesDict\(""", r"""features=datasets.Features("""),
(r"""tfds\.features\.FeaturesDict\(""", r"""dict("""),
(r"""The TensorFlow Datasets Authors""", r"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
(r"""tfds\.""", r"""datasets."""),
(r"""dl_manager\.manual_dir""", r"""self.config.data_dir"""),
(r"""self\.builder_config""", r"""self.config"""),
]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return ConvertCommand(args.tfds_path , args.datasets_directory )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@staticmethod
def _A ( UpperCAmelCase_ : ArgumentParser ):
SCREAMING_SNAKE_CASE : Tuple = parser.add_parser(
"convert" , help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset." , )
train_parser.add_argument(
"--tfds_path" , type=UpperCAmelCase_ , required=UpperCAmelCase_ , help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert." , )
train_parser.add_argument(
"--datasets_directory" , type=UpperCAmelCase_ , required=UpperCAmelCase_ , help="Path to the HuggingFace Datasets folder." )
train_parser.set_defaults(func=UpperCAmelCase_ )
def __init__( self : Optional[int] , UpperCAmelCase_ : str , UpperCAmelCase_ : str , *UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE : Optional[Any] = get_logger("datasets-cli/converting" )
SCREAMING_SNAKE_CASE : Any = tfds_path
SCREAMING_SNAKE_CASE : Union[str, Any] = datasets_directory
def _A ( self : List[Any] ):
if os.path.isdir(self._tfds_path ):
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
SCREAMING_SNAKE_CASE : Tuple = os.path.dirname(self._tfds_path )
else:
raise ValueError("--tfds_path is neither a directory nor a file. Please check path." )
SCREAMING_SNAKE_CASE : List[Any] = os.path.abspath(self._datasets_directory )
self._logger.info(f'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : Optional[Any] = {}
if os.path.isdir(self._tfds_path ):
SCREAMING_SNAKE_CASE : List[Any] = os.listdir(UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : Dict = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(f'''Looking at file {f_name}''' )
SCREAMING_SNAKE_CASE : Dict = os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )
if not os.path.isfile(UpperCAmelCase_ ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("Skipping file" )
continue
with open(UpperCAmelCase_ , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE : Dict = f.readlines()
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : Optional[Any] = []
for line in lines:
SCREAMING_SNAKE_CASE : Any = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
SCREAMING_SNAKE_CASE : List[str] = "import datasets\n"
elif "import tensorflow" in out_line:
# order is important here
SCREAMING_SNAKE_CASE : List[str] = ""
continue
elif "from absl import logging" in out_line:
SCREAMING_SNAKE_CASE : int = "from datasets import logging\n"
elif "getLogger" in out_line:
SCREAMING_SNAKE_CASE : List[Any] = out_line.replace("getLogger" , "get_logger" )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : str = list(filter(lambda UpperCAmelCase_ : e in out_line , UpperCAmelCase_ ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCAmelCase_ ) + "\n" )
out_lines.append(UpperCAmelCase_ )
out_lines.append(UpperCAmelCase_ )
continue
else:
for pattern, replacement in TO_CONVERT:
SCREAMING_SNAKE_CASE : Union[str, Any] = re.sub(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
SCREAMING_SNAKE_CASE : Optional[Any] = re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)" , UpperCAmelCase_ )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split("," ) )
SCREAMING_SNAKE_CASE : Optional[Any] = "from . import " + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(f'''Error converting {out_line.strip()}''' )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
SCREAMING_SNAKE_CASE : List[Any] = True
out_lines.append(UpperCAmelCase_ )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
SCREAMING_SNAKE_CASE : str = f_name.replace(".py" , "" )
SCREAMING_SNAKE_CASE : int = os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
self._logger.info(f'''Adding directory {output_dir}''' )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(UpperCAmelCase_ )
if needs_manual_update:
with_manual_update.append(UpperCAmelCase_ )
with open(UpperCAmelCase_ , "w" , encoding="utf-8" ) as f:
f.writelines(UpperCAmelCase_ )
self._logger.info(f'''Converted in {output_file}''' )
for utils_file in utils_files:
try:
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.basename(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = imports_to_builder_map[f_name.replace(".py" , "" )]
self._logger.info(f'''Moving {dest_folder} to {utils_file}''' )
shutil.copy(UpperCAmelCase_ , UpperCAmelCase_ )
except KeyError:
self._logger.error(f'''Cannot find destination folder for {utils_file}. Please copy manually.''' )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
f'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.''' )
| 319
|
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 319
| 1
|
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
snake_case = logging.getLogger(__name__)
require_version("""pytorch_lightning>=1.0.4""")
snake_case = {
"""base""": AutoModel,
"""sequence-classification""": AutoModelForSequenceClassification,
"""question-answering""": AutoModelForQuestionAnswering,
"""pretraining""": AutoModelForPreTraining,
"""token-classification""": AutoModelForTokenClassification,
"""language-modeling""": AutoModelWithLMHead,
"""summarization""": AutoModelForSeqaSeqLM,
"""translation""": AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
snake_case = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
snake_case = sorted(arg_to_scheduler.keys())
snake_case = """{""" + """, """.join(arg_to_scheduler_choices) + """}"""
class SCREAMING_SNAKE_CASE ( pl.LightningModule ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : argparse.Namespace , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : str="base" , UpperCAmelCase_ : Union[str, Any]=None , UpperCAmelCase_ : int=None , UpperCAmelCase_ : str=None , **UpperCAmelCase_ : Any , ):
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : List[str] = Path(self.hparams.output_dir )
SCREAMING_SNAKE_CASE : Tuple = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
SCREAMING_SNAKE_CASE : int = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({"num_labels": num_labels} if num_labels is not None else {}) , cache_dir=UpperCAmelCase_ , **UpperCAmelCase_ , )
else:
SCREAMING_SNAKE_CASE : PretrainedConfig = config
SCREAMING_SNAKE_CASE : Optional[Any] = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(self.hparams , UpperCAmelCase_ , UpperCAmelCase_ ):
assert hasattr(self.config , UpperCAmelCase_ ), f'''model config doesn\'t have a `{p}` attribute'''
setattr(self.config , UpperCAmelCase_ , getattr(self.hparams , UpperCAmelCase_ ) )
if tokenizer is None:
SCREAMING_SNAKE_CASE : List[str] = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=UpperCAmelCase_ , )
else:
SCREAMING_SNAKE_CASE : PreTrainedTokenizer = tokenizer
SCREAMING_SNAKE_CASE : Any = MODEL_MODES[mode]
if model is None:
SCREAMING_SNAKE_CASE : List[Any] = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool(".ckpt" in self.hparams.model_name_or_path ) , config=self.config , cache_dir=UpperCAmelCase_ , )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = model
def _A ( self : Optional[int] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : int = self.model_type.from_pretrained(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : List[str] = arg_to_scheduler[self.hparams.lr_scheduler]
SCREAMING_SNAKE_CASE : Any = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
SCREAMING_SNAKE_CASE : Tuple = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return scheduler
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : int = self.model
SCREAMING_SNAKE_CASE : Union[str, Any] = ["bias", "LayerNorm.weight"]
SCREAMING_SNAKE_CASE : List[Any] = [
{
"params": [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
"weight_decay": self.hparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
if self.hparams.adafactor:
SCREAMING_SNAKE_CASE : Optional[Any] = Adafactor(
UpperCAmelCase_ , lr=self.hparams.learning_rate , scale_parameter=UpperCAmelCase_ , relative_step=UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : Optional[int] = AdamW(
UpperCAmelCase_ , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
SCREAMING_SNAKE_CASE : Union[str, Any] = optimizer
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_lr_scheduler()
return [optimizer], [scheduler]
def _A ( self : Union[str, Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Union[str, Any] ):
return self.validation_step(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : int , UpperCAmelCase_ : Any ):
return self.validation_end(UpperCAmelCase_ )
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : Optional[Any] = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
SCREAMING_SNAKE_CASE : Tuple = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def _A ( self : Dict , UpperCAmelCase_ : str ):
if stage == "test":
SCREAMING_SNAKE_CASE : int = len(self.test_dataloader().dataset )
else:
SCREAMING_SNAKE_CASE : Any = self.get_dataloader("train" , self.hparams.train_batch_size , shuffle=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = len(self.train_dataloader().dataset )
def _A ( self : Any , UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : bool = False ):
raise NotImplementedError("You must implement this for your task" )
def _A ( self : Optional[Any] ):
return self.train_loader
def _A ( self : Optional[Any] ):
return self.get_dataloader("dev" , self.hparams.eval_batch_size , shuffle=UpperCAmelCase_ )
def _A ( self : Optional[int] ):
return self.get_dataloader("test" , self.hparams.eval_batch_size , shuffle=UpperCAmelCase_ )
def _A ( self : int , UpperCAmelCase_ : Union[str, Any] ):
return os.path.join(
self.hparams.data_dir , "cached_{}_{}_{}".format(
UpperCAmelCase_ , list(filter(UpperCAmelCase_ , self.hparams.model_name_or_path.split("/" ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def _A ( self : str , UpperCAmelCase_ : Dict[str, Any] ):
SCREAMING_SNAKE_CASE : Optional[int] = self.output_dir.joinpath("best_tfmr" )
SCREAMING_SNAKE_CASE : Tuple = self.step_count
self.model.save_pretrained(UpperCAmelCase_ )
self.tokenizer.save_pretrained(UpperCAmelCase_ )
@staticmethod
def _A ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any] ):
parser.add_argument(
"--model_name_or_path" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , required=UpperCAmelCase_ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--config_name" , default="" , type=UpperCAmelCase_ , help="Pretrained config name or path if not the same as model_name" )
parser.add_argument(
"--tokenizer_name" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , help="Pretrained tokenizer name or path if not the same as model_name" , )
parser.add_argument(
"--cache_dir" , default=str(Path(UpperCAmelCase_ ).parent / "test_run" / "cache" ) , type=UpperCAmelCase_ , help="Where do you want to store the pre-trained models downloaded from huggingface.co" , )
parser.add_argument(
"--encoder_layerdrop" , type=UpperCAmelCase_ , help="Encoder layer dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--decoder_layerdrop" , type=UpperCAmelCase_ , help="Decoder layer dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--dropout" , type=UpperCAmelCase_ , help="Dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--attention_dropout" , type=UpperCAmelCase_ , help="Attention dropout probability (Optional). Goes into model.config" , )
parser.add_argument("--learning_rate" , default=5E-5 , type=UpperCAmelCase_ , help="The initial learning rate for Adam." )
parser.add_argument(
"--lr_scheduler" , default="linear" , choices=UpperCAmelCase_ , metavar=UpperCAmelCase_ , type=UpperCAmelCase_ , help="Learning rate scheduler" , )
parser.add_argument("--weight_decay" , default=0.0 , type=UpperCAmelCase_ , help="Weight decay if we apply some." )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=UpperCAmelCase_ , help="Epsilon for Adam optimizer." )
parser.add_argument("--warmup_steps" , default=0 , type=UpperCAmelCase_ , help="Linear warmup over warmup_steps." )
parser.add_argument("--num_workers" , default=4 , type=UpperCAmelCase_ , help="kwarg passed to DataLoader" )
parser.add_argument("--num_train_epochs" , dest="max_epochs" , default=3 , type=UpperCAmelCase_ )
parser.add_argument("--train_batch_size" , default=32 , type=UpperCAmelCase_ )
parser.add_argument("--eval_batch_size" , default=32 , type=UpperCAmelCase_ )
parser.add_argument("--adafactor" , action="store_true" )
class SCREAMING_SNAKE_CASE ( pl.Callback ):
'''simple docstring'''
def _A ( self : Dict , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str ):
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class SCREAMING_SNAKE_CASE ( pl.Callback ):
'''simple docstring'''
def _A ( self : Optional[int] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Tuple ):
# print(pl_module.model.rag)
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(UpperCAmelCase_ )
class SCREAMING_SNAKE_CASE ( pl.Callback ):
'''simple docstring'''
def _A ( self : Optional[int] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Optional[Any] = trainer.lr_schedulers[0]["scheduler"]
SCREAMING_SNAKE_CASE : int = {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(UpperCAmelCase_ )
def _A ( self : Optional[Any] , UpperCAmelCase_ : pl.Trainer , UpperCAmelCase_ : pl.LightningModule ):
rank_zero_info("***** Validation results *****" )
SCREAMING_SNAKE_CASE : Optional[Any] = trainer.callback_metrics
# Log results
for key in sorted(UpperCAmelCase_ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(UpperCAmelCase_ , str(metrics[key] ) ) )
def _A ( self : Any , UpperCAmelCase_ : pl.Trainer , UpperCAmelCase_ : pl.LightningModule ):
rank_zero_info("***** Test results *****" )
SCREAMING_SNAKE_CASE : Optional[int] = trainer.callback_metrics
# Log and save results to file
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(pl_module.hparams.output_dir , "test_results.txt" )
with open(UpperCAmelCase_ , "w" ) as writer:
for key in sorted(UpperCAmelCase_ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(UpperCAmelCase_ , str(metrics[key] ) ) )
writer.write("{} = {}\n".format(UpperCAmelCase_ , str(metrics[key] ) ) )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
parser.add_argument(
"--output_dir" , default=str(Path(lowercase ).parent / "test_run" / "model_checkpoints" ) , type=lowercase , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=lowercase , default="O2" , help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_tpu_cores" , dest="tpu_cores" , type=lowercase )
parser.add_argument("--max_grad_norm" , dest="gradient_clip_val" , default=1.0 , type=lowercase , help="Max gradient norm" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_predict" , action="store_true" , help="Whether to run predictions on the test set." )
parser.add_argument(
"--gradient_accumulation_steps" , dest="accumulate_grad_batches" , type=lowercase , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--seed" , type=lowercase , default=42 , help="random seed for initialization" )
parser.add_argument(
"--data_dir" , default=str(Path(lowercase ).parent / "test_run" / "dummy-train-data" ) , type=lowercase , help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=None , lowercase=True , lowercase=[] , lowercase=None , lowercase=None , **lowercase , ):
"""simple docstring"""
pl.seed_everything(args.seed )
# init model
SCREAMING_SNAKE_CASE : Tuple = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=lowercase )
# add custom checkpoints
if checkpoint_callback is None:
SCREAMING_SNAKE_CASE : Union[str, Any] = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix="checkpoint" , monitor="val_loss" , mode="min" , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(lowercase )
if logging_callback is None:
SCREAMING_SNAKE_CASE : Any = LoggingCallback()
SCREAMING_SNAKE_CASE : Any = {}
if args.fpaa:
SCREAMING_SNAKE_CASE : Any = 16
if args.gpus > 1:
SCREAMING_SNAKE_CASE : Any = "auto"
SCREAMING_SNAKE_CASE : str = "ddp"
SCREAMING_SNAKE_CASE : Optional[Any] = args.accumulate_grad_batches
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Tuple = "auto"
SCREAMING_SNAKE_CASE : Dict = pl.Trainer.from_argparse_args(
lowercase , weights_summary=lowercase , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=lowercase , val_check_interval=1 , num_sanity_val_steps=2 , **lowercase , )
if args.do_train:
trainer.fit(lowercase )
else:
print("RAG modeling tests with new set functions successfuly executed!" )
return trainer
| 319
|
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
snake_case = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = torch.load(lowercase , map_location="cpu" )
return sd
def lowerCamelCase__ ( lowercase , lowercase , lowercase=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = OrderedDict()
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE : Optional[Any] = key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE : Tuple = new_key.replace(name_pair[0] , name_pair[1] )
SCREAMING_SNAKE_CASE : Union[str, Any] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE : Union[str, Any] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE : str = "pretraining"
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE : str = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE : Optional[int] = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.''' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE : Optional[Any] = {"visual_embedding_dim": 512}
SCREAMING_SNAKE_CASE : Union[str, Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE : int = {"visual_embedding_dim": 2048}
SCREAMING_SNAKE_CASE : Any = "vqa_advanced"
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE : Any = {"visual_embedding_dim": 2048, "num_labels": 3129}
SCREAMING_SNAKE_CASE : Tuple = "vqa"
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE : int = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
SCREAMING_SNAKE_CASE : Union[str, Any] = "nlvr"
SCREAMING_SNAKE_CASE : List[Any] = VisualBertConfig(**lowercase )
# Load State Dict
SCREAMING_SNAKE_CASE : Union[str, Any] = load_state_dict(lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = get_new_dict(lowercase , lowercase )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE : Union[str, Any] = VisualBertForPreTraining(lowercase )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE : Optional[Any] = VisualBertForQuestionAnswering(lowercase )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE : Optional[Any] = VisualBertForVisualReasoning(lowercase )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE : List[Any] = VisualBertForMultipleChoice(lowercase )
model.load_state_dict(lowercase )
# Save Checkpoints
Path(lowercase ).mkdir(exist_ok=lowercase )
model.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
snake_case = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 319
| 1
|
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[int] , *,
UpperCAmelCase_ : int = 4 , UpperCAmelCase_ : int = 768 , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] , ):
super().__init__()
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(UpperCAmelCase_ ) )
# parameters for additional clip time embeddings
SCREAMING_SNAKE_CASE : Any = nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ )
# parameters for encoder hidden states
SCREAMING_SNAKE_CASE : Optional[Any] = clip_extra_context_tokens
SCREAMING_SNAKE_CASE : str = nn.Linear(
UpperCAmelCase_ , self.clip_extra_context_tokens * cross_attention_dim )
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = nn.LayerNorm(UpperCAmelCase_ )
def _A ( self : Dict , *, UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[int] ):
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
SCREAMING_SNAKE_CASE : str = image_embeddings.shape[0]
SCREAMING_SNAKE_CASE : Any = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = classifier_free_guidance_embeddings.expand(
UpperCAmelCase_ , -1 )
SCREAMING_SNAKE_CASE : int = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
SCREAMING_SNAKE_CASE : Optional[Any] = self.embedding_proj(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.clip_image_embeddings_project_to_time_embeddings(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
SCREAMING_SNAKE_CASE : Union[str, Any] = self.clip_extra_context_tokens_proj(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = clip_extra_context_tokens.reshape(UpperCAmelCase_ , -1 , self.clip_extra_context_tokens )
SCREAMING_SNAKE_CASE : List[str] = clip_extra_context_tokens.permute(0 , 2 , 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.encoder_hidden_states_proj(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = self.text_encoder_hidden_states_norm(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 319
|
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''ClapFeatureExtractor'''
UpperCamelCase_ : Any = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : List[str]=None , **UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("sampling_rate" , UpperCAmelCase_ )
if text is None and audios is None:
raise ValueError("You have to specify either text or audios. Both cannot be none." )
if text is not None:
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if audios is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.feature_extractor(
UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None and audios is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase_ ) , tensor_type=UpperCAmelCase_ )
def _A ( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : str ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : List[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Any ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@property
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Any = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE : List[Any] = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 319
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EncodecModel""",
"""EncodecPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
|
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Any = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : Optional[int] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Any = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(lowercase , cache_dir=lowercase , split=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = parquet_path
elif issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [parquet_path]
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Tuple = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=("train",) ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
for split in splits:
SCREAMING_SNAKE_CASE : Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Dict = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(
{"train": parquet_path} , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[int] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Dict = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : str = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : Optional[Any] = ParquetDatasetReader({"train": parquet_path} , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if split:
SCREAMING_SNAKE_CASE : Any = {split: parquet_path}
else:
SCREAMING_SNAKE_CASE : Tuple = "train"
SCREAMING_SNAKE_CASE : int = {"train": parquet_path, "test": parquet_path}
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : str = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : int = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : Tuple = pq.ParquetFile(tmp_path / "foo.parquet" )
SCREAMING_SNAKE_CASE : List[Any] = pf.read()
assert dataset.data.table == output_table
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = str(shared_datadir / "test_image_rgb.jpg" )
SCREAMING_SNAKE_CASE : Union[str, Any] = {"image": [image_path]}
SCREAMING_SNAKE_CASE : Union[str, Any] = Features({"image": Image()} )
SCREAMING_SNAKE_CASE : int = Dataset.from_dict(lowercase , features=lowercase )
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : str = Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
assert dataset.features == reloaded_dataset.features
SCREAMING_SNAKE_CASE : Any = ParquetDatasetReader(str(tmp_path / "foo.parquet" ) , streaming=lowercase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32" )} ), None),
(Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert get_writer_batch_size(lowercase ) == expected
| 319
| 1
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
def lowerCamelCase__ ( lowercase , lowercase=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
SCREAMING_SNAKE_CASE : List[Any] = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def lowerCamelCase__ ( lowercase , lowercase , lowercase=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
SCREAMING_SNAKE_CASE : List[str] = ""
else:
SCREAMING_SNAKE_CASE : Dict = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE : Any = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE : Dict = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE : Optional[Any] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE : Dict = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE : Tuple = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE : Union[str, Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE : str = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE : str = in_proj_bias[-config.hidden_size :]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = dct.pop(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = val
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE : Union[str, Any] = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase , lowercase=True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ViTConfig()
# patch_size
if model_name[-1] == "8":
SCREAMING_SNAKE_CASE : List[str] = 8
# set labels if required
if not base_model:
SCREAMING_SNAKE_CASE : Tuple = 1000
SCREAMING_SNAKE_CASE : Any = "huggingface/label-files"
SCREAMING_SNAKE_CASE : List[str] = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE : Any = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE : int = {int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : int = idalabel
SCREAMING_SNAKE_CASE : int = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
SCREAMING_SNAKE_CASE : List[Any] = 384
SCREAMING_SNAKE_CASE : Union[str, Any] = 1536
SCREAMING_SNAKE_CASE : int = 12
SCREAMING_SNAKE_CASE : List[str] = 6
# load original model from torch hub
SCREAMING_SNAKE_CASE : Tuple = torch.hub.load("facebookresearch/dino:main" , lowercase )
original_model.eval()
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE : Optional[int] = original_model.state_dict()
if base_model:
remove_classification_head_(lowercase )
SCREAMING_SNAKE_CASE : str = create_rename_keys(lowercase , base_model=lowercase )
for src, dest in rename_keys:
rename_key(lowercase , lowercase , lowercase )
read_in_q_k_v(lowercase , lowercase , lowercase )
# load HuggingFace model
if base_model:
SCREAMING_SNAKE_CASE : List[str] = ViTModel(lowercase , add_pooling_layer=lowercase ).eval()
else:
SCREAMING_SNAKE_CASE : List[Any] = ViTForImageClassification(lowercase ).eval()
model.load_state_dict(lowercase )
# Check outputs on an image, prepared by ViTImageProcessor
SCREAMING_SNAKE_CASE : Optional[Any] = ViTImageProcessor()
SCREAMING_SNAKE_CASE : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" )
SCREAMING_SNAKE_CASE : List[str] = encoding["pixel_values"]
SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowercase )
if base_model:
SCREAMING_SNAKE_CASE : int = original_model(lowercase )
assert torch.allclose(lowercase , outputs.last_hidden_state[:, 0, :] , atol=1E-1 )
else:
SCREAMING_SNAKE_CASE : Tuple = original_model(lowercase )
assert logits.shape == outputs.logits.shape
assert torch.allclose(lowercase , outputs.logits , atol=1E-3 )
Path(lowercase ).mkdir(exist_ok=lowercase )
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""dino_vitb16""",
type=str,
help="""Name of the model trained with DINO you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--base_model""",
action="""store_true""",
help="""Whether to only convert the base model (no projection head weights).""",
)
parser.set_defaults(base_model=True)
snake_case = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 319
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
snake_case = 3
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
print("Generating primitive root of p" )
while True:
SCREAMING_SNAKE_CASE : Union[str, Any] = random.randrange(3 , lowercase )
if pow(lowercase , 2 , lowercase ) == 1:
continue
if pow(lowercase , lowercase , lowercase ) == 1:
continue
return g
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
print("Generating prime p..." )
SCREAMING_SNAKE_CASE : Tuple = rabin_miller.generate_large_prime(lowercase ) # select large prime number.
SCREAMING_SNAKE_CASE : List[Any] = primitive_root(lowercase ) # one primitive root on modulo p.
SCREAMING_SNAKE_CASE : Tuple = random.randrange(3 , lowercase ) # private_key -> have to be greater than 2 for safety.
SCREAMING_SNAKE_CASE : Dict = cryptomath.find_mod_inverse(pow(lowercase , lowercase , lowercase ) , lowercase )
SCREAMING_SNAKE_CASE : Tuple = (key_size, e_a, e_a, p)
SCREAMING_SNAKE_CASE : Union[str, Any] = (key_size, d)
return public_key, private_key
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if os.path.exists(F'''{name}_pubkey.txt''' ) or os.path.exists(F'''{name}_privkey.txt''' ):
print("\nWARNING:" )
print(
F'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
"Use a different name or delete these files and re-run this program." )
sys.exit()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = generate_key(lowercase )
print(F'''\nWriting public key to file {name}_pubkey.txt...''' )
with open(F'''{name}_pubkey.txt''' , "w" ) as fo:
fo.write(F'''{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}''' )
print(F'''Writing private key to file {name}_privkey.txt...''' )
with open(F'''{name}_privkey.txt''' , "w" ) as fo:
fo.write(F'''{private_key[0]},{private_key[1]}''' )
def lowerCamelCase__ ( ):
"""simple docstring"""
print("Making key files..." )
make_key_files("elgamal" , 2048 )
print("Key files generation successful" )
if __name__ == "__main__":
main()
| 319
|
def lowerCamelCase__ ( lowercase , lowercase = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = length or len(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = list_data[i + 1], list_data[i]
SCREAMING_SNAKE_CASE : str = True
return list_data if not swapped else bubble_sort(lowercase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
| 1
|
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __get__( self : str , UpperCAmelCase_ : Any , UpperCAmelCase_ : Dict=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute" )
SCREAMING_SNAKE_CASE : Union[str, Any] = "__cached_" + self.fget.__name__
SCREAMING_SNAKE_CASE : Any = getattr(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
if cached is None:
SCREAMING_SNAKE_CASE : List[str] = self.fget(UpperCAmelCase_ )
setattr(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return cached
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F'''invalid truth value {val!r}''' )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if is_torch_fx_proxy(lowercase ):
return True
if is_torch_available():
import torch
if isinstance(lowercase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(lowercase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(lowercase , (jnp.ndarray, Tracer) ):
return True
return isinstance(lowercase , np.ndarray )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return isinstance(lowercase , np.ndarray )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return _is_numpy(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
import torch
return isinstance(lowercase , torch.Tensor )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return False if not is_torch_available() else _is_torch(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
import torch
return isinstance(lowercase , torch.device )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return False if not is_torch_available() else _is_torch_device(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
import torch
if isinstance(lowercase , lowercase ):
if hasattr(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = getattr(lowercase , lowercase )
else:
return False
return isinstance(lowercase , torch.dtype )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return False if not is_torch_available() else _is_torch_dtype(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
import tensorflow as tf
return isinstance(lowercase , tf.Tensor )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return False if not is_tf_available() else _is_tensorflow(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(lowercase , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(lowercase )
return type(lowercase ) == tf.Tensor
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return False if not is_tf_available() else _is_tf_symbolic_tensor(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
import jax.numpy as jnp # noqa: F811
return isinstance(lowercase , jnp.ndarray )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return False if not is_flax_available() else _is_jax(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if isinstance(lowercase , (dict, UserDict) ):
return {k: to_py_obj(lowercase ) for k, v in obj.items()}
elif isinstance(lowercase , (list, tuple) ):
return [to_py_obj(lowercase ) for o in obj]
elif is_tf_tensor(lowercase ):
return obj.numpy().tolist()
elif is_torch_tensor(lowercase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(lowercase ):
return np.asarray(lowercase ).tolist()
elif isinstance(lowercase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if isinstance(lowercase , (dict, UserDict) ):
return {k: to_numpy(lowercase ) for k, v in obj.items()}
elif isinstance(lowercase , (list, tuple) ):
return np.array(lowercase )
elif is_tf_tensor(lowercase ):
return obj.numpy()
elif is_torch_tensor(lowercase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(lowercase ):
return np.asarray(lowercase )
else:
return obj
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def _A ( self : List[str] ):
SCREAMING_SNAKE_CASE : Optional[int] = fields(self )
# Safety and consistency checks
if not len(UpperCAmelCase_ ):
raise ValueError(f'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(f'''{self.__class__.__name__} should not have more than one required field.''' )
SCREAMING_SNAKE_CASE : List[str] = getattr(self , class_fields[0].name )
SCREAMING_SNAKE_CASE : Tuple = all(getattr(self , field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(UpperCAmelCase_ ):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Any = first_field.items()
SCREAMING_SNAKE_CASE : List[Any] = True
else:
try:
SCREAMING_SNAKE_CASE : str = iter(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = True
except TypeError:
SCREAMING_SNAKE_CASE : str = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(UpperCAmelCase_ ):
if (
not isinstance(UpperCAmelCase_ , (list, tuple) )
or not len(UpperCAmelCase_ ) == 2
or not isinstance(element[0] , UpperCAmelCase_ )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
SCREAMING_SNAKE_CASE : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self , element[0] , element[1] )
if element[1] is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = element[1]
elif first_field is not None:
SCREAMING_SNAKE_CASE : int = first_field
else:
for field in class_fields:
SCREAMING_SNAKE_CASE : Any = getattr(self , field.name )
if v is not None:
SCREAMING_SNAKE_CASE : Tuple = v
def __delitem__( self : Tuple , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : List[Any] ):
raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def _A ( self : List[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[Any] ):
raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def _A ( self : List[Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ):
raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def _A ( self : str , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[Any] ):
raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : Tuple , UpperCAmelCase_ : Tuple ):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Any = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : int , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Any ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(UpperCAmelCase_ , UpperCAmelCase_ )
super().__setattr__(UpperCAmelCase_ , UpperCAmelCase_ )
def __setitem__( self : List[Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int ):
# Will raise a KeyException if needed
super().__setitem__(UpperCAmelCase_ , UpperCAmelCase_ )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Union[str, Any] ):
return tuple(self[k] for k in self.keys() )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
@classmethod
def _A ( cls : List[str] , UpperCAmelCase_ : Tuple ):
raise ValueError(
f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : int = '''longest'''
UpperCamelCase_ : List[Any] = '''max_length'''
UpperCamelCase_ : Tuple = '''do_not_pad'''
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = '''pt'''
UpperCamelCase_ : Optional[Any] = '''tf'''
UpperCamelCase_ : Tuple = '''np'''
UpperCamelCase_ : List[Any] = '''jax'''
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : List[ContextManager] ):
SCREAMING_SNAKE_CASE : Optional[Any] = context_managers
SCREAMING_SNAKE_CASE : List[str] = ExitStack()
def __enter__( self : Union[str, Any] ):
for context_manager in self.context_managers:
self.stack.enter_context(UpperCAmelCase_ )
def __exit__( self : int , *UpperCAmelCase_ : int , **UpperCAmelCase_ : List[Any] ):
self.stack.__exit__(*UpperCAmelCase_ , **UpperCAmelCase_ )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = infer_framework(lowercase )
if framework == "tf":
SCREAMING_SNAKE_CASE : Union[str, Any] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
SCREAMING_SNAKE_CASE : Optional[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
SCREAMING_SNAKE_CASE : Any = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = model_class.__name__
SCREAMING_SNAKE_CASE : str = infer_framework(lowercase )
if framework == "tf":
SCREAMING_SNAKE_CASE : Optional[Any] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
SCREAMING_SNAKE_CASE : int = inspect.signature(model_class.forward ) # PyTorch models
else:
SCREAMING_SNAKE_CASE : Any = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def lowerCamelCase__ ( lowercase , lowercase = "" , lowercase = "." ):
"""simple docstring"""
def _flatten_dict(lowercase , lowercase="" , lowercase="." ):
for k, v in d.items():
SCREAMING_SNAKE_CASE : Any = str(lowercase ) + delimiter + str(lowercase ) if parent_key else k
if v and isinstance(lowercase , lowercase ):
yield from flatten_dict(lowercase , lowercase , delimiter=lowercase ).items()
else:
yield key, v
return dict(_flatten_dict(lowercase , lowercase , lowercase ) )
@contextmanager
def lowerCamelCase__ ( lowercase , lowercase = False ):
"""simple docstring"""
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def lowerCamelCase__ ( lowercase , lowercase=None ):
"""simple docstring"""
if is_numpy_array(lowercase ):
return np.transpose(lowercase , axes=lowercase )
elif is_torch_tensor(lowercase ):
return array.T if axes is None else array.permute(*lowercase )
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.transpose(lowercase , perm=lowercase )
elif is_jax_tensor(lowercase ):
return jnp.transpose(lowercase , axes=lowercase )
else:
raise ValueError(F'''Type not supported for transpose: {type(lowercase )}.''' )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if is_numpy_array(lowercase ):
return np.reshape(lowercase , lowercase )
elif is_torch_tensor(lowercase ):
return array.reshape(*lowercase )
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.reshape(lowercase , lowercase )
elif is_jax_tensor(lowercase ):
return jnp.reshape(lowercase , lowercase )
else:
raise ValueError(F'''Type not supported for reshape: {type(lowercase )}.''' )
def lowerCamelCase__ ( lowercase , lowercase=None ):
"""simple docstring"""
if is_numpy_array(lowercase ):
return np.squeeze(lowercase , axis=lowercase )
elif is_torch_tensor(lowercase ):
return array.squeeze() if axis is None else array.squeeze(dim=lowercase )
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.squeeze(lowercase , axis=lowercase )
elif is_jax_tensor(lowercase ):
return jnp.squeeze(lowercase , axis=lowercase )
else:
raise ValueError(F'''Type not supported for squeeze: {type(lowercase )}.''' )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if is_numpy_array(lowercase ):
return np.expand_dims(lowercase , lowercase )
elif is_torch_tensor(lowercase ):
return array.unsqueeze(dim=lowercase )
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.expand_dims(lowercase , axis=lowercase )
elif is_jax_tensor(lowercase ):
return jnp.expand_dims(lowercase , axis=lowercase )
else:
raise ValueError(F'''Type not supported for expand_dims: {type(lowercase )}.''' )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if is_numpy_array(lowercase ):
return np.size(lowercase )
elif is_torch_tensor(lowercase ):
return array.numel()
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.size(lowercase )
elif is_jax_tensor(lowercase ):
return array.size
else:
raise ValueError(F'''Type not supported for expand_dims: {type(lowercase )}.''' )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
for key, value in auto_map.items():
if isinstance(lowercase , (tuple, list) ):
SCREAMING_SNAKE_CASE : str = [F'''{repo_id}--{v}''' if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
SCREAMING_SNAKE_CASE : str = F'''{repo_id}--{value}'''
return auto_map
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
for base_class in inspect.getmro(lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = base_class.__module__
SCREAMING_SNAKE_CASE : int = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F'''Could not infer framework from class {model_class}.''' )
| 319
|
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case = get_logger(__name__)
snake_case = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : str , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[int] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ):
for processor in self:
SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(processor.__call__ ).parameters
if len(UpperCAmelCase_ ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
f'''Make sure that all the required parameters: {list(function_args.keys() )} for '''
f'''{processor.__class__} are passed to the logits processor.''' )
SCREAMING_SNAKE_CASE : int = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , **UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : Dict = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : float ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not (temperature > 0):
raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}''' )
SCREAMING_SNAKE_CASE : Optional[int] = temperature
def __call__( self : List[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = scores / self.temperature
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : float , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (top_p < 0 or top_p > 1.0):
raise ValueError(f'''`top_p` has to be a float > 0 and < 1, but is {top_p}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (min_tokens_to_keep < 1):
raise ValueError(f'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}''' )
SCREAMING_SNAKE_CASE : Optional[int] = top_p
SCREAMING_SNAKE_CASE : str = filter_value
SCREAMING_SNAKE_CASE : List[str] = min_tokens_to_keep
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = lax.top_k(UpperCAmelCase_ , scores.shape[-1] )
SCREAMING_SNAKE_CASE : str = jnp.full_like(UpperCAmelCase_ , self.filter_value )
SCREAMING_SNAKE_CASE : Optional[int] = jax.nn.softmax(UpperCAmelCase_ , axis=-1 ).cumsum(axis=-1 )
SCREAMING_SNAKE_CASE : Tuple = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
SCREAMING_SNAKE_CASE : Optional[int] = jnp.roll(UpperCAmelCase_ , 1 )
score_mask |= score_mask.at[:, 0].set(UpperCAmelCase_ )
# min tokens to keep
SCREAMING_SNAKE_CASE : Union[str, Any] = score_mask.at[:, : self.min_tokens_to_keep].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = jnp.where(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jax.lax.sort_key_val(UpperCAmelCase_ , UpperCAmelCase_ )[-1]
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or top_k <= 0:
raise ValueError(f'''`top_k` has to be a strictly positive integer, but is {top_k}''' )
SCREAMING_SNAKE_CASE : List[str] = max(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = filter_value
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = scores.shape
SCREAMING_SNAKE_CASE : List[str] = jnp.full(batch_size * vocab_size , self.filter_value )
SCREAMING_SNAKE_CASE : List[str] = min(self.top_k , scores.shape[-1] ) # Safety check
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = lax.top_k(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = jnp.broadcast_to((jnp.arange(UpperCAmelCase_ ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
SCREAMING_SNAKE_CASE : List[str] = topk_scores.flatten()
SCREAMING_SNAKE_CASE : List[Any] = topk_indices.flatten() + shift
SCREAMING_SNAKE_CASE : Dict = next_scores_flat.at[topk_indices_flat].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = next_scores_flat.reshape(UpperCAmelCase_ , UpperCAmelCase_ )
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = bos_token_id
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.bool_(cur_len - 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.bos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = max_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : List[str] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : str = 1 - jnp.bool_(cur_len - self.max_length + 1 )
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.eos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or min_length < 0:
raise ValueError(f'''`min_length` has to be a positive integer, but is {min_length}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or eos_token_id < 0:
raise ValueError(f'''`eos_token_id` has to be a positive integer, but is {eos_token_id}''' )
SCREAMING_SNAKE_CASE : List[str] = min_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
# create boolean flag to decide if min length penalty should be applied
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
SCREAMING_SNAKE_CASE : Optional[int] = jnp.where(UpperCAmelCase_ , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = list(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = begin_index
def __call__( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Union[str, Any] = 1 - jnp.bool_(cur_len - self.begin_index )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(UpperCAmelCase_ , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : List[Any] = list(UpperCAmelCase_ )
def __call__( self : Any , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Tuple = scores.at[..., self.suppress_tokens].set(-float("inf" ) )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : List[Any] = dict(UpperCAmelCase_ )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
SCREAMING_SNAKE_CASE : Any = force_token_array.at[index].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = jnp.intaa(UpperCAmelCase_ )
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
def _force_token(UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : List[str] = scores.shape[0]
SCREAMING_SNAKE_CASE : Optional[int] = self.force_token_array[generation_idx]
SCREAMING_SNAKE_CASE : Tuple = jnp.ones_like(UpperCAmelCase_ , dtype=scores.dtype ) * -float("inf" )
SCREAMING_SNAKE_CASE : Dict = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
SCREAMING_SNAKE_CASE : Optional[Any] = lax.dynamic_update_slice(UpperCAmelCase_ , UpperCAmelCase_ , (0, current_token) )
return new_scores
SCREAMING_SNAKE_CASE : Any = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(UpperCAmelCase_ ) , lambda: scores , ) , )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Union[str, Any] = generate_config.eos_token_id
SCREAMING_SNAKE_CASE : Tuple = generate_config.no_timestamps_token_id
SCREAMING_SNAKE_CASE : List[Any] = generate_config.no_timestamps_token_id + 1
SCREAMING_SNAKE_CASE : Dict = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(UpperCAmelCase_ , "max_initial_timestamp_index" ):
SCREAMING_SNAKE_CASE : List[Any] = generate_config.max_initial_timestamp_index
else:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
if self.max_initial_timestamp_index is None:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
def __call__( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
# suppress <|notimestamps|> which is handled by without_timestamps
SCREAMING_SNAKE_CASE : int = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) )
def handle_pairs(UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) >= 1 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) < 2 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , UpperCAmelCase_ , UpperCAmelCase_ , )
return jnp.where(
UpperCAmelCase_ , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Optional[Any] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(cur_len == self.begin_index , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = self.timestamp_begin + self.max_initial_timestamp_index
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(
UpperCAmelCase_ , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , UpperCAmelCase_ , )
# if sum of probability over timestamps is above any other token, sample timestamp
SCREAMING_SNAKE_CASE : List[Any] = jax.nn.log_softmax(UpperCAmelCase_ , axis=-1 )
def handle_cumulative_probs(UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
return scores
| 319
| 1
|
import os
from collections.abc import Iterator
def lowerCamelCase__ ( lowercase = "." ):
"""simple docstring"""
for dir_path, dir_names, filenames in os.walk(lowercase ):
SCREAMING_SNAKE_CASE : Tuple = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(lowercase )[1] in (".py", ".ipynb"):
yield os.path.join(lowercase , lowercase ).lstrip("./" )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return F'''{i * ' '}*''' if i else "\n##"
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(lowercase ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(lowercase )} {new_part.replace('_' , ' ' ).title()}''' )
return new_path
def lowerCamelCase__ ( lowercase = "." ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ""
for filepath in sorted(good_file_paths(lowercase ) ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = os.path.split(lowercase )
if filepath != old_path:
SCREAMING_SNAKE_CASE : str = print_path(lowercase , lowercase )
SCREAMING_SNAKE_CASE : Tuple = (filepath.count(os.sep ) + 1) if filepath else 0
SCREAMING_SNAKE_CASE : Tuple = F'''{filepath}/{filename}'''.replace(" " , "%20" )
SCREAMING_SNAKE_CASE : List[Any] = os.path.splitext(filename.replace("_" , " " ).title() )[0]
print(F'''{md_prefix(lowercase )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(""".""")
| 319
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
snake_case = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
print("""Torch version:""", None)
try:
import transformers
print("""transformers version:""", transformers.__version__)
except ImportError:
print("""transformers version:""", None)
| 319
| 1
|
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _A ( self : List[Any] , UpperCAmelCase_ : Dict ):
SCREAMING_SNAKE_CASE : Optional[int] = 3
SCREAMING_SNAKE_CASE : Optional[int] = 250
SCREAMING_SNAKE_CASE : int = ids_tensor((batch_size, length) , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = torch.ones((batch_size, length) , device=UpperCAmelCase_ , dtype=torch.float ) / length
return input_ids, scores
def _A ( self : Any ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = self._get_tensors(5 )
SCREAMING_SNAKE_CASE : str = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self._get_tensors(9 )
self.assertFalse(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = self._get_tensors(10 )
self.assertTrue(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
def _A ( self : int ):
SCREAMING_SNAKE_CASE : Tuple = MaxLengthCriteria(max_length=10 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = self._get_tensors(5 )
self.assertFalse(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = self._get_tensors(9 )
self.assertFalse(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = self._get_tensors(10 )
self.assertTrue(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : int = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self._get_tensors(5 )
self.assertFalse(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = self._get_tensors(9 )
self.assertFalse(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self._get_tensors(10 )
self.assertTrue(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : List[Any] = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 10 )
def _A ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = self._get_tensors(5 )
SCREAMING_SNAKE_CASE : List[str] = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : List[str] = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) )
def _A ( self : Union[str, Any] ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 10 )
with self.assertWarns(UpperCAmelCase_ ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 11 )
SCREAMING_SNAKE_CASE : Dict = validate_stopping_criteria(StoppingCriteriaList() , 11 )
self.assertEqual(len(UpperCAmelCase_ ) , 1 )
| 319
|
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 319
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMSNModel""",
"""ViTMSNForImageClassification""",
"""ViTMSNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
|
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
snake_case = {
"""b0""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1_408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1_536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1_792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2_304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2_560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = EfficientNetConfig()
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["hidden_dim"]
SCREAMING_SNAKE_CASE : Tuple = CONFIG_MAP[model_name]["width_coef"]
SCREAMING_SNAKE_CASE : Optional[int] = CONFIG_MAP[model_name]["depth_coef"]
SCREAMING_SNAKE_CASE : Union[str, Any] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = CONFIG_MAP[model_name]["dropout_rate"]
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["dw_padding"]
SCREAMING_SNAKE_CASE : str = "huggingface/label-files"
SCREAMING_SNAKE_CASE : str = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE : str = 1000
SCREAMING_SNAKE_CASE : List[Any] = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE : Tuple = {int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Union[str, Any] = idalabel
SCREAMING_SNAKE_CASE : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE : List[Any] = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : int = EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=lowercase , )
return preprocessor
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
SCREAMING_SNAKE_CASE : List[str] = sorted(set(lowercase ) )
SCREAMING_SNAKE_CASE : List[str] = len(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = {b: str(lowercase ) for b, i in zip(lowercase , range(lowercase ) )}
SCREAMING_SNAKE_CASE : Dict = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
SCREAMING_SNAKE_CASE : Tuple = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
SCREAMING_SNAKE_CASE : int = {}
for item in rename_keys:
if item[0] in original_param_names:
SCREAMING_SNAKE_CASE : Any = "efficientnet." + item[1]
SCREAMING_SNAKE_CASE : Optional[Any] = "classifier.weight"
SCREAMING_SNAKE_CASE : List[str] = "classifier.bias"
return key_mapping
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
SCREAMING_SNAKE_CASE : str = key_mapping[key]
if "_conv" in key and "kernel" in key:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
SCREAMING_SNAKE_CASE : int = torch.from_numpy(lowercase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
SCREAMING_SNAKE_CASE : List[str] = torch.from_numpy(np.transpose(lowercase ) )
else:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(lowercase )
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = model_classes[model_name](
include_top=lowercase , weights="imagenet" , input_tensor=lowercase , input_shape=lowercase , pooling=lowercase , classes=1000 , classifier_activation="softmax" , )
SCREAMING_SNAKE_CASE : List[Any] = original_model.trainable_variables
SCREAMING_SNAKE_CASE : Dict = original_model.non_trainable_variables
SCREAMING_SNAKE_CASE : Dict = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
SCREAMING_SNAKE_CASE : Tuple = param.numpy()
SCREAMING_SNAKE_CASE : Tuple = list(tf_params.keys() )
# Load HuggingFace model
SCREAMING_SNAKE_CASE : Tuple = get_efficientnet_config(lowercase )
SCREAMING_SNAKE_CASE : str = EfficientNetForImageClassification(lowercase ).eval()
SCREAMING_SNAKE_CASE : Dict = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
SCREAMING_SNAKE_CASE : Dict = rename_keys(lowercase )
replace_params(lowercase , lowercase , lowercase )
# Initialize preprocessor and preprocess input image
SCREAMING_SNAKE_CASE : Optional[int] = convert_image_processor(lowercase )
SCREAMING_SNAKE_CASE : int = preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = hf_model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.logits.detach().numpy()
# Original model inference
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
SCREAMING_SNAKE_CASE : Tuple = image.img_to_array(lowercase )
SCREAMING_SNAKE_CASE : Tuple = np.expand_dims(lowercase , axis=0 )
SCREAMING_SNAKE_CASE : Any = original_model.predict(lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(lowercase , lowercase , atol=1E-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(lowercase ):
os.mkdir(lowercase )
# Save converted model and image processor
hf_model.save_pretrained(lowercase )
preprocessor.save_pretrained(lowercase )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(lowercase )
hf_model.push_to_hub(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
snake_case = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
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def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase__ ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
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|
def lowerCamelCase__ ( ):
"""simple docstring"""
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
snake_case = generate_large_matrix()
snake_case = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
assert all(row == sorted(lowercase , reverse=lowercase ) for row in grid )
assert all(list(lowercase ) == sorted(lowercase , reverse=lowercase ) for col in zip(*lowercase ) )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowercase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
SCREAMING_SNAKE_CASE : List[Any] = (left + right) // 2
SCREAMING_SNAKE_CASE : Optional[int] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
SCREAMING_SNAKE_CASE : List[Any] = mid + 1
else:
SCREAMING_SNAKE_CASE : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : List[str] = len(grid[0] )
for i in range(len(lowercase ) ):
SCREAMING_SNAKE_CASE : Any = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowercase ) * len(grid[0] )) - total
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return len([number for row in grid for number in row if number < 0] )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = 0
for row in grid:
for i, number in enumerate(lowercase ):
if number < 0:
total += len(lowercase ) - i
break
return total
def lowerCamelCase__ ( ):
"""simple docstring"""
from timeit import timeit
print("Running benchmarks" )
SCREAMING_SNAKE_CASE : List[str] = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
SCREAMING_SNAKE_CASE : Union[str, Any] = timeit(F'''{func}(grid=grid)''' , setup=lowercase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
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|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlenderbotConfig""",
"""BlenderbotOnnxConfig""",
],
"""tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""BlenderbotTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlenderbotForCausalLM""",
"""BlenderbotForConditionalGeneration""",
"""BlenderbotModel""",
"""BlenderbotPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""TFBlenderbotForConditionalGeneration""",
"""TFBlenderbotModel""",
"""TFBlenderbotPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FlaxBlenderbotForConditionalGeneration""",
"""FlaxBlenderbotModel""",
"""FlaxBlenderbotPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case = ["""small""", """medium""", """large"""]
snake_case = """lm_head.decoder.weight"""
snake_case = """lm_head.weight"""
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = torch.load(lowercase )
SCREAMING_SNAKE_CASE : Any = d.pop(lowercase )
os.makedirs(lowercase , exist_ok=lowercase )
torch.save(lowercase , os.path.join(lowercase , lowercase ) )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
snake_case = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
snake_case = os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
snake_case = F"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
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|
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''ClapFeatureExtractor'''
UpperCamelCase_ : Any = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : List[str]=None , **UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("sampling_rate" , UpperCAmelCase_ )
if text is None and audios is None:
raise ValueError("You have to specify either text or audios. Both cannot be none." )
if text is not None:
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if audios is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.feature_extractor(
UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None and audios is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase_ ) , tensor_type=UpperCAmelCase_ )
def _A ( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : str ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : List[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Any ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@property
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Any = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE : List[Any] = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
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|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""MLukeTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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| 1
|
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Optional[int] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : str = 8
# DPR tok
SCREAMING_SNAKE_CASE : Union[str, Any] = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
SCREAMING_SNAKE_CASE : List[str] = os.path.join(self.tmpdirname , "dpr_tokenizer" )
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(UpperCAmelCase_ , DPR_VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
# BART tok
SCREAMING_SNAKE_CASE : str = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
SCREAMING_SNAKE_CASE : Any = dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_ ) ) ) )
SCREAMING_SNAKE_CASE : List[str] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
SCREAMING_SNAKE_CASE : Dict = {"unk_token": "<unk>"}
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(self.tmpdirname , "bart_tokenizer" )
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(UpperCAmelCase_ , BART_VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE : str = os.path.join(UpperCAmelCase_ , BART_VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCAmelCase_ ) )
def _A ( self : str ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def _A ( self : Optional[int] ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , "bart_tokenizer" ) )
def _A ( self : List[Any] ):
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : str = os.path.join(self.tmpdirname , "rag_tokenizer" )
SCREAMING_SNAKE_CASE : str = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
SCREAMING_SNAKE_CASE : Optional[int] = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(UpperCAmelCase_ )
rag_tokenizer.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = RagTokenizer.from_pretrained(UpperCAmelCase_ , config=UpperCAmelCase_ )
self.assertIsInstance(new_rag_tokenizer.question_encoder , UpperCAmelCase_ )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , UpperCAmelCase_ )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Union[str, Any] = RagTokenizer.from_pretrained("facebook/rag-token-nq" )
SCREAMING_SNAKE_CASE : Union[str, Any] = [
"who got the first nobel prize in physics",
"when is the next deadpool movie being released",
"which mode is used for short wave broadcast service",
"who is the owner of reading football club",
"when is the next scandal episode coming out",
"when is the last time the philadelphia won the superbowl",
"what is the most current adobe flash player version",
"how many episodes are there in dragon ball z",
"what is the first step in the evolution of the eye",
"where is gall bladder situated in human body",
"what is the main mineral in lithium batteries",
"who is the president of usa right now",
"where do the greasers live in the outsiders",
"panda is a national animal of which country",
"what is the name of manchester united stadium",
]
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
@slow
def _A ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE : Optional[Any] = RagTokenizer.from_pretrained("facebook/rag-sequence-nq" )
SCREAMING_SNAKE_CASE : List[str] = [
"who got the first nobel prize in physics",
"when is the next deadpool movie being released",
"which mode is used for short wave broadcast service",
"who is the owner of reading football club",
"when is the next scandal episode coming out",
"when is the last time the philadelphia won the superbowl",
"what is the most current adobe flash player version",
"how many episodes are there in dragon ball z",
"what is the first step in the evolution of the eye",
"where is gall bladder situated in human body",
"what is the main mineral in lithium batteries",
"who is the president of usa right now",
"where do the greasers live in the outsiders",
"panda is a national animal of which country",
"what is the name of manchester united stadium",
]
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
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|
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase__ ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 319
| 1
|
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : str = field(default='''text-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
UpperCamelCase_ : ClassVar[Features] = Features({'''text''': Value('''string''' )} )
UpperCamelCase_ : ClassVar[Features] = Features({'''labels''': ClassLabel} )
UpperCamelCase_ : str = "text"
UpperCamelCase_ : str = "labels"
def _A ( self : Any , UpperCAmelCase_ : Dict ):
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] , UpperCAmelCase_ ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
SCREAMING_SNAKE_CASE : Any = copy.deepcopy(self )
SCREAMING_SNAKE_CASE : List[Any] = self.label_schema.copy()
SCREAMING_SNAKE_CASE : Union[str, Any] = features[self.label_column]
SCREAMING_SNAKE_CASE : str = label_schema
return task_template
@property
def _A ( self : List[Any] ):
return {
self.text_column: "text",
self.label_column: "labels",
}
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|
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : Union[str, Any] = set_counts
SCREAMING_SNAKE_CASE : Any = max(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = [1] * num_sets
SCREAMING_SNAKE_CASE : List[str] = list(range(UpperCAmelCase_ ) )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[Any] = self.get_parent(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self.get_parent(UpperCAmelCase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
SCREAMING_SNAKE_CASE : List[str] = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Tuple = src_parent
SCREAMING_SNAKE_CASE : Optional[int] = self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Optional[Any] = max(self.max_set , UpperCAmelCase_ )
return True
def _A ( self : Tuple , UpperCAmelCase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
SCREAMING_SNAKE_CASE : Tuple = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 319
| 1
|
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : bool = False ):
SCREAMING_SNAKE_CASE : Any = scheduler
SCREAMING_SNAKE_CASE : Union[str, Any] = optimizers if isinstance(UpperCAmelCase_ , (list, tuple) ) else [optimizers]
SCREAMING_SNAKE_CASE : List[Any] = split_batches
SCREAMING_SNAKE_CASE : str = step_with_optimizer
SCREAMING_SNAKE_CASE : str = GradientState()
def _A ( self : str , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Union[str, Any] ):
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*UpperCAmelCase_ , **UpperCAmelCase_ )
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(*UpperCAmelCase_ , **UpperCAmelCase_ )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
SCREAMING_SNAKE_CASE : List[str] = AcceleratorState().num_processes
for _ in range(UpperCAmelCase_ ):
# 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(*UpperCAmelCase_ , **UpperCAmelCase_ )
else:
self.scheduler.step(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : Dict ):
return self.scheduler.get_last_lr()
def _A ( self : Optional[int] ):
return self.scheduler.state_dict()
def _A ( self : Any , UpperCAmelCase_ : int ):
self.scheduler.load_state_dict(UpperCAmelCase_ )
def _A ( self : int ):
return self.scheduler.get_lr()
def _A ( self : str , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[Any] ):
return self.scheduler.print_lr(*UpperCAmelCase_ , **UpperCAmelCase_ )
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|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''timm_backbone'''
def __init__( self : List[Any] , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : List[str]=3 , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : Union[str, Any]=None , **UpperCAmelCase_ : Optional[Any] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = backbone
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = features_only
SCREAMING_SNAKE_CASE : Dict = use_pretrained_backbone
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : List[Any] = out_indices if out_indices is not None else (-1,)
| 319
| 1
|
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
raise Exception("""requires fairseq >= 1.0.0a""")
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = """Hello world! cécé herlolip"""
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = FairseqRobertaModel.from_pretrained(lowercase )
roberta.eval() # disable dropout
SCREAMING_SNAKE_CASE : Any = roberta.model.encoder.sentence_encoder
SCREAMING_SNAKE_CASE : Optional[int] = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1E-5 , )
if classification_head:
SCREAMING_SNAKE_CASE : List[Any] = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our RoBERTa config:" , lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = XLMRobertaXLForSequenceClassification(lowercase ) if classification_head else XLMRobertaXLForMaskedLM(lowercase )
model.eval()
# Now let's copy all the weights.
# Embeddings
SCREAMING_SNAKE_CASE : Optional[Any] = roberta_sent_encoder.embed_tokens.weight
SCREAMING_SNAKE_CASE : int = roberta_sent_encoder.embed_positions.weight
SCREAMING_SNAKE_CASE : List[str] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
SCREAMING_SNAKE_CASE : str = roberta_sent_encoder.layer_norm.weight
SCREAMING_SNAKE_CASE : Any = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
SCREAMING_SNAKE_CASE : BertLayer = model.roberta.encoder.layer[i]
SCREAMING_SNAKE_CASE : TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
SCREAMING_SNAKE_CASE : RobertaAttention = layer.attention
SCREAMING_SNAKE_CASE : str = roberta_layer.self_attn_layer_norm.weight
SCREAMING_SNAKE_CASE : Dict = roberta_layer.self_attn_layer_norm.bias
# self attention
SCREAMING_SNAKE_CASE : BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
SCREAMING_SNAKE_CASE : Optional[int] = roberta_layer.self_attn.q_proj.weight
SCREAMING_SNAKE_CASE : List[str] = roberta_layer.self_attn.q_proj.bias
SCREAMING_SNAKE_CASE : Any = roberta_layer.self_attn.k_proj.weight
SCREAMING_SNAKE_CASE : List[Any] = roberta_layer.self_attn.k_proj.bias
SCREAMING_SNAKE_CASE : Union[str, Any] = roberta_layer.self_attn.v_proj.weight
SCREAMING_SNAKE_CASE : Union[str, Any] = roberta_layer.self_attn.v_proj.bias
# self-attention output
SCREAMING_SNAKE_CASE : BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
SCREAMING_SNAKE_CASE : Optional[Any] = roberta_layer.self_attn.out_proj.weight
SCREAMING_SNAKE_CASE : Optional[Any] = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
SCREAMING_SNAKE_CASE : List[str] = roberta_layer.final_layer_norm.weight
SCREAMING_SNAKE_CASE : Optional[int] = roberta_layer.final_layer_norm.bias
# intermediate
SCREAMING_SNAKE_CASE : BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE : Dict = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE : Dict = roberta_layer.fca.bias
# output
SCREAMING_SNAKE_CASE : BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE : List[str] = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE : int = roberta_layer.fca.bias
# end of layer
if classification_head:
SCREAMING_SNAKE_CASE : Union[str, Any] = roberta.model.classification_heads["mnli"].dense.weight
SCREAMING_SNAKE_CASE : int = roberta.model.classification_heads["mnli"].dense.bias
SCREAMING_SNAKE_CASE : List[Any] = roberta.model.classification_heads["mnli"].out_proj.weight
SCREAMING_SNAKE_CASE : List[Any] = roberta.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
SCREAMING_SNAKE_CASE : Tuple = roberta.model.encoder.lm_head.dense.weight
SCREAMING_SNAKE_CASE : Dict = roberta.model.encoder.lm_head.dense.bias
SCREAMING_SNAKE_CASE : Union[str, Any] = roberta.model.encoder.lm_head.layer_norm.weight
SCREAMING_SNAKE_CASE : Tuple = roberta.model.encoder.lm_head.layer_norm.bias
SCREAMING_SNAKE_CASE : List[str] = roberta.model.encoder.lm_head.weight
SCREAMING_SNAKE_CASE : Tuple = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
SCREAMING_SNAKE_CASE : torch.Tensor = roberta.encode(lowercase ).unsqueeze(0 ) # batch of size 1
SCREAMING_SNAKE_CASE : List[Any] = model(lowercase )[0]
if classification_head:
SCREAMING_SNAKE_CASE : Any = roberta.model.classification_heads["mnli"](roberta.extract_features(lowercase ) )
else:
SCREAMING_SNAKE_CASE : Optional[int] = roberta.model(lowercase )[0]
print(our_output.shape , their_output.shape )
SCREAMING_SNAKE_CASE : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
SCREAMING_SNAKE_CASE : Tuple = torch.allclose(lowercase , lowercase , atol=1E-3 )
print("Do both models output the same tensors?" , "🔥" if success else "💩" )
if not success:
raise Exception("Something went wRoNg" )
pathlib.Path(lowercase ).mkdir(parents=lowercase , exist_ok=lowercase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--roberta_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."""
)
snake_case = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 319
|
from math import sqrt
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for i in range(1 , int(sqrt(lowercase ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase ):
total += i + n // i
elif i == sqrt(lowercase ):
total += i
return total - n
def lowerCamelCase__ ( lowercase = 10000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = sum(
i
for i in range(1 , lowercase )
if sum_of_divisors(sum_of_divisors(lowercase ) ) == i and sum_of_divisors(lowercase ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 319
| 1
|
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = coefficient_matrix.shape
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = constant_matrix.shape
if rowsa != colsa:
SCREAMING_SNAKE_CASE : List[Any] = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
raise ValueError(lowercase )
if colsa != 1:
SCREAMING_SNAKE_CASE : Optional[Any] = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
raise ValueError(lowercase )
if rowsa != rowsa:
SCREAMING_SNAKE_CASE : int = (
"Coefficient and constant matrices dimensions must be nxn and nx1 but "
F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
)
raise ValueError(lowercase )
if len(lowercase ) != rowsa:
SCREAMING_SNAKE_CASE : Optional[Any] = (
"Number of initial values must be equal to number of rows in coefficient "
F'''matrix but received {len(lowercase )} and {rowsa}'''
)
raise ValueError(lowercase )
if iterations <= 0:
raise ValueError("Iterations must be at least 1" )
SCREAMING_SNAKE_CASE : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = table.shape
strictly_diagonally_dominant(lowercase )
# Iterates the whole matrix for given number of times
for _ in range(lowercase ):
SCREAMING_SNAKE_CASE : Dict = []
for row in range(lowercase ):
SCREAMING_SNAKE_CASE : List[str] = 0
for col in range(lowercase ):
if col == row:
SCREAMING_SNAKE_CASE : Dict = table[row][col]
elif col == cols - 1:
SCREAMING_SNAKE_CASE : Tuple = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
SCREAMING_SNAKE_CASE : List[Any] = (temp + val) / denom
new_val.append(lowercase )
SCREAMING_SNAKE_CASE : List[Any] = new_val
return [float(lowercase ) for i in new_val]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = table.shape
SCREAMING_SNAKE_CASE : str = True
for i in range(0 , lowercase ):
SCREAMING_SNAKE_CASE : int = 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()
| 319
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EncodecModel""",
"""EncodecPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = tmp_path / "file.csv"
SCREAMING_SNAKE_CASE : Optional[Any] = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20\n " )
with open(lowercase , "w" ) as f:
f.write(lowercase )
return str(lowercase )
@pytest.fixture
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = tmp_path / "malformed_file.csv"
SCREAMING_SNAKE_CASE : Dict = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20,\n " )
with open(lowercase , "w" ) as f:
f.write(lowercase )
return str(lowercase )
@pytest.fixture
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "csv_with_image.csv"
SCREAMING_SNAKE_CASE : Tuple = textwrap.dedent(
F'''\
image
{image_file}
''' )
with open(lowercase , "w" ) as f:
f.write(lowercase )
return str(lowercase )
@pytest.fixture
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "csv_with_label.csv"
SCREAMING_SNAKE_CASE : Dict = textwrap.dedent(
"\\n label\n good\n bad\n good\n " )
with open(lowercase , "w" ) as f:
f.write(lowercase )
return str(lowercase )
@pytest.fixture
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "csv_with_int_list.csv"
SCREAMING_SNAKE_CASE : Optional[int] = textwrap.dedent(
"\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n " )
with open(lowercase , "w" ) as f:
f.write(lowercase )
return str(lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = Csv()
SCREAMING_SNAKE_CASE : Optional[Any] = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(lowercase , match="Error tokenizing data" ):
for _ in generator:
pass
assert any(
record.levelname == "ERROR"
and "Failed to read file" in record.message
and os.path.basename(lowercase ) in record.message
for record in caplog.records )
@require_pil
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
with open(lowercase , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE : int = f.read().splitlines()[1]
SCREAMING_SNAKE_CASE : Optional[Any] = Csv(encoding="utf-8" , features=Features({"image": Image()} ) )
SCREAMING_SNAKE_CASE : int = csv._generate_tables([[csv_file_with_image]] )
SCREAMING_SNAKE_CASE : List[str] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("image" ).type == Image()()
SCREAMING_SNAKE_CASE : Dict = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
with open(lowercase , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE : Dict = f.read().splitlines()[1:]
SCREAMING_SNAKE_CASE : Union[str, Any] = Csv(encoding="utf-8" , features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
SCREAMING_SNAKE_CASE : Dict = csv._generate_tables([[csv_file_with_label]] )
SCREAMING_SNAKE_CASE : Optional[int] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
SCREAMING_SNAKE_CASE : Tuple = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(lowercase ) for label in labels]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = Csv(encoding="utf-8" , sep="," , converters={"int_list": lambda lowercase : [int(lowercase ) for i in x.split()]} )
SCREAMING_SNAKE_CASE : Optional[Any] = csv._generate_tables([[csv_file_with_int_list]] )
SCREAMING_SNAKE_CASE : Optional[Any] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
SCREAMING_SNAKE_CASE : Union[str, Any] = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 319
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
snake_case = None
snake_case = logging.get_logger(__name__)
snake_case = """▁"""
snake_case = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
snake_case = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
snake_case = {
"""google/pegasus-xsum""": 512,
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES
UpperCamelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : int = PegasusTokenizer
UpperCamelCase_ : str = ['''input_ids''', '''attention_mask''']
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Optional[int]="<pad>" , UpperCAmelCase_ : int="</s>" , UpperCAmelCase_ : str="<unk>" , UpperCAmelCase_ : str="<mask_2>" , UpperCAmelCase_ : Optional[int]="<mask_1>" , UpperCAmelCase_ : int=None , UpperCAmelCase_ : str=103 , **UpperCAmelCase_ : Optional[int] , ):
SCREAMING_SNAKE_CASE : Optional[Any] = offset
if additional_special_tokens is not None:
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError(
f'''additional_special_tokens should be of type {type(UpperCAmelCase_ )}, but is'''
f''' {type(UpperCAmelCase_ )}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(UpperCAmelCase_ ) , self.offset - 1 )
]
if len(set(UpperCAmelCase_ ) ) != len(UpperCAmelCase_ ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
SCREAMING_SNAKE_CASE : int = additional_special_tokens_extended
else:
SCREAMING_SNAKE_CASE : Tuple = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
super().__init__(
UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , mask_token_sent=UpperCAmelCase_ , offset=UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : str = vocab_file
SCREAMING_SNAKE_CASE : str = False if not self.vocab_file else True
def _A ( self : Optional[Any] , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Optional[int] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
f''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : int , UpperCAmelCase_ : List , UpperCAmelCase_ : Optional[List] = None , UpperCAmelCase_ : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(UpperCAmelCase_ )
elif token_ids_a is None:
return self._special_token_mask(UpperCAmelCase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Any=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : str , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
UpperCAmelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase_ ):
copyfile(self.vocab_file , UpperCAmelCase_ )
return (out_vocab_file,)
| 319
| 1
|
import math
import unittest
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
assert isinstance(lowercase , lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowercase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _A ( self : List[str] ):
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def _A ( self : int ):
with self.assertRaises(UpperCAmelCase_ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) , "Zero doesn't have any positive factors, primes must have exactly two." , )
self.assertFalse(
is_prime(1 ) , "One only has 1 positive factor, primes must have exactly two." , )
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 319
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""SpeechEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""FlaxSpeechEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
snake_case = """
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"red cat, 4k photo\"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")
>>> pipe.to(\"cuda\")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save(\"cat.png\")
```
"""
def lowerCamelCase__ ( lowercase , lowercase , lowercase=8 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
SCREAMING_SNAKE_CASE : List[Any] = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : MultilingualCLIP , UpperCAmelCase_ : XLMRobertaTokenizer , UpperCAmelCase_ : UNetaDConditionModel , UpperCAmelCase_ : Union[DDIMScheduler, DDPMScheduler] , UpperCAmelCase_ : VQModel , ):
super().__init__()
self.register_modules(
text_encoder=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ , unet=UpperCAmelCase_ , scheduler=UpperCAmelCase_ , movq=UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def _A ( self : Optional[int] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : str , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict ):
if latents is None:
SCREAMING_SNAKE_CASE : str = randn_tensor(UpperCAmelCase_ , generator=UpperCAmelCase_ , device=UpperCAmelCase_ , dtype=UpperCAmelCase_ )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
SCREAMING_SNAKE_CASE : Tuple = latents.to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = latents * scheduler.init_noise_sigma
return latents
def _A ( self : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any=None , ):
SCREAMING_SNAKE_CASE : Union[str, Any] = len(UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else 1
# get prompt text embeddings
SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer(
UpperCAmelCase_ , padding="max_length" , truncation=UpperCAmelCase_ , max_length=77 , return_attention_mask=UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ , return_tensors="pt" , )
SCREAMING_SNAKE_CASE : Optional[int] = text_inputs.input_ids
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(UpperCAmelCase_ , padding="longest" , return_tensors="pt" ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Dict = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
SCREAMING_SNAKE_CASE : Optional[Any] = text_input_ids.to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = text_inputs.attention_mask.to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = self.text_encoder(
input_ids=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = prompt_embeds.repeat_interleave(UpperCAmelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Optional[int] = text_encoder_hidden_states.repeat_interleave(UpperCAmelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : int = text_mask.repeat_interleave(UpperCAmelCase_ , dim=0 )
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : List[str]
if negative_prompt is None:
SCREAMING_SNAKE_CASE : str = [""] * batch_size
elif type(UpperCAmelCase_ ) is not type(UpperCAmelCase_ ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase_ )} !='''
f''' {type(UpperCAmelCase_ )}.''' )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : int = [negative_prompt]
elif batch_size != len(UpperCAmelCase_ ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase_ )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
" the batch size of `prompt`." )
else:
SCREAMING_SNAKE_CASE : int = negative_prompt
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer(
UpperCAmelCase_ , padding="max_length" , max_length=77 , truncation=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ , return_tensors="pt" , )
SCREAMING_SNAKE_CASE : List[Any] = uncond_input.input_ids.to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = uncond_input.attention_mask.to(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = self.text_encoder(
input_ids=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
SCREAMING_SNAKE_CASE : Tuple = negative_prompt_embeds.shape[1]
SCREAMING_SNAKE_CASE : Union[str, Any] = negative_prompt_embeds.repeat(1 , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = negative_prompt_embeds.view(batch_size * num_images_per_prompt , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = uncond_text_encoder_hidden_states.shape[1]
SCREAMING_SNAKE_CASE : Tuple = uncond_text_encoder_hidden_states.repeat(1 , UpperCAmelCase_ , 1 )
SCREAMING_SNAKE_CASE : int = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , UpperCAmelCase_ , -1 )
SCREAMING_SNAKE_CASE : str = uncond_text_mask.repeat_interleave(UpperCAmelCase_ , dim=0 )
# done duplicates
# 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
SCREAMING_SNAKE_CASE : str = torch.cat([negative_prompt_embeds, prompt_embeds] )
SCREAMING_SNAKE_CASE : List[Any] = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def _A ( self : List[str] , UpperCAmelCase_ : int=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
SCREAMING_SNAKE_CASE : str = torch.device(f'''cuda:{gpu_id}''' )
SCREAMING_SNAKE_CASE : List[str] = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Tuple , UpperCAmelCase_ : Optional[int]=0 ):
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
SCREAMING_SNAKE_CASE : Tuple = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=UpperCAmelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
SCREAMING_SNAKE_CASE : Optional[Any] = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = cpu_offload_with_hook(UpperCAmelCase_ , UpperCAmelCase_ , prev_module_hook=UpperCAmelCase_ )
if self.safety_checker is not None:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = cpu_offload_with_hook(self.safety_checker , UpperCAmelCase_ , prev_module_hook=UpperCAmelCase_ )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE : int = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _A ( self : str ):
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(UpperCAmelCase_ , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(UpperCAmelCase_ )
def __call__( self : Any , UpperCAmelCase_ : Union[str, List[str]] , UpperCAmelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , UpperCAmelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , UpperCAmelCase_ : Optional[Union[str, List[str]]] = None , UpperCAmelCase_ : int = 512 , UpperCAmelCase_ : int = 512 , UpperCAmelCase_ : int = 100 , UpperCAmelCase_ : float = 4.0 , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCAmelCase_ : Optional[torch.FloatTensor] = None , UpperCAmelCase_ : Optional[str] = "pil" , UpperCAmelCase_ : bool = True , ):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Any = 1
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = len(UpperCAmelCase_ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase_ )}''' )
SCREAMING_SNAKE_CASE : str = self._execution_device
SCREAMING_SNAKE_CASE : Tuple = batch_size * num_images_per_prompt
SCREAMING_SNAKE_CASE : Dict = guidance_scale > 1.0
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = self._encode_prompt(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Dict = torch.cat(UpperCAmelCase_ , dim=0 )
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : List[str] = torch.cat(UpperCAmelCase_ , dim=0 )
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : Tuple = image_embeds.repeat_interleave(UpperCAmelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : int = negative_image_embeds.repeat_interleave(UpperCAmelCase_ , dim=0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=UpperCAmelCase_ )
self.scheduler.set_timesteps(UpperCAmelCase_ , device=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = self.scheduler.timesteps
SCREAMING_SNAKE_CASE : Tuple = self.unet.config.in_channels
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = get_new_h_w(UpperCAmelCase_ , UpperCAmelCase_ , self.movq_scale_factor )
# create initial latent
SCREAMING_SNAKE_CASE : List[str] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(UpperCAmelCase_ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE : Optional[int] = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
SCREAMING_SNAKE_CASE : List[str] = self.unet(
sample=UpperCAmelCase_ , timestep=UpperCAmelCase_ , encoder_hidden_states=UpperCAmelCase_ , added_cond_kwargs=UpperCAmelCase_ , return_dict=UpperCAmelCase_ , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE : int = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE : Tuple = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE : Dict = self.scheduler.step(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ , ).prev_sample
# post-processing
SCREAMING_SNAKE_CASE : Any = self.movq.decode(UpperCAmelCase_ , force_not_quantize=UpperCAmelCase_ )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
SCREAMING_SNAKE_CASE : str = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : Tuple = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Any = self.numpy_to_pil(UpperCAmelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCAmelCase_ )
| 319
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
snake_case = 16
snake_case = 32
def lowerCamelCase__ ( lowercase , lowercase = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained("bert-base-cased" )
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset("glue" , "mrpc" )
def tokenize_function(lowercase ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=lowercase , max_length=lowercase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE : List[Any] = datasets.map(
lowercase , batched=lowercase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE : Tuple = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE : str = 16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE : Optional[Any] = 8
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = None
return tokenizer.pad(
lowercase , padding="longest" , max_length=lowercase , pad_to_multiple_of=lowercase , return_tensors="pt" , )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE : Optional[int] = DataLoader(
tokenized_datasets["train"] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
SCREAMING_SNAKE_CASE : Dict = DataLoader(
tokenized_datasets["validation"] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
snake_case = mocked_dataloaders # noqa: F811
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" , lowercase ) == "1":
SCREAMING_SNAKE_CASE : int = 2
# New Code #
SCREAMING_SNAKE_CASE : Union[str, Any] = int(args.gradient_accumulation_steps )
# Initialize accelerator
SCREAMING_SNAKE_CASE : Tuple = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE : Any = config["lr"]
SCREAMING_SNAKE_CASE : Optional[Any] = int(config["num_epochs"] )
SCREAMING_SNAKE_CASE : List[Any] = int(config["seed"] )
SCREAMING_SNAKE_CASE : Union[str, Any] = int(config["batch_size"] )
SCREAMING_SNAKE_CASE : Optional[Any] = evaluate.load("glue" , "mrpc" )
set_seed(lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = get_dataloaders(lowercase , lowercase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE : List[Any] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=lowercase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE : Any = model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE : Any = AdamW(params=model.parameters() , lr=lowercase )
# Instantiate scheduler
SCREAMING_SNAKE_CASE : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=lowercase , num_warmup_steps=100 , num_training_steps=(len(lowercase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = accelerator.prepare(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Now we train the model
for epoch in range(lowercase ):
model.train()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase ):
SCREAMING_SNAKE_CASE : Any = model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = output.loss
accelerator.backward(lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[Any] = model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=lowercase , references=lowercase , )
SCREAMING_SNAKE_CASE : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowercase )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=lowercase , default=lowercase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=lowercase , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
SCREAMING_SNAKE_CASE : Dict = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(lowercase , lowercase )
if __name__ == "__main__":
main()
| 319
| 1
|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
snake_case = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""
class SCREAMING_SNAKE_CASE ( unittest.TestCase , lowerCAmelCase ):
'''simple docstring'''
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : Optional[int] = load_tool("text-question-answering" )
self.tool.setup()
SCREAMING_SNAKE_CASE : Dict = load_tool("text-question-answering" , remote=UpperCAmelCase_ )
def _A ( self : List[Any] ):
SCREAMING_SNAKE_CASE : List[str] = self.tool(UpperCAmelCase_ , "What did Hugging Face do in April 2021?" )
self.assertEqual(UpperCAmelCase_ , "launched the BigScience Research Workshop" )
def _A ( self : List[str] ):
SCREAMING_SNAKE_CASE : List[Any] = self.remote_tool(UpperCAmelCase_ , "What did Hugging Face do in April 2021?" )
self.assertEqual(UpperCAmelCase_ , "launched the BigScience Research Workshop" )
def _A ( self : List[Any] ):
SCREAMING_SNAKE_CASE : Dict = self.tool(text=UpperCAmelCase_ , question="What did Hugging Face do in April 2021?" )
self.assertEqual(UpperCAmelCase_ , "launched the BigScience Research Workshop" )
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : List[Any] = self.remote_tool(text=UpperCAmelCase_ , question="What did Hugging Face do in April 2021?" )
self.assertEqual(UpperCAmelCase_ , "launched the BigScience Research Workshop" )
| 319
|
import functools
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ) or not all(isinstance(lowercase , lowercase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(lowercase ) != 3 or not all(isinstance(lowercase , lowercase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(lowercase ) == 0:
return 0
if min(lowercase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(lowercase ) >= 366:
raise ValueError("All days elements should be less than 366" )
SCREAMING_SNAKE_CASE : Dict = set(lowercase )
@functools.cache
def dynamic_programming(lowercase ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
| 1
|
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
SCREAMING_SNAKE_CASE : str = ""
SCREAMING_SNAKE_CASE : List[str] = ""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(lowercase ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = 0, 0
# length[i] shows the length of palindromic substring with center i
SCREAMING_SNAKE_CASE : Union[str, Any] = [1 for i in range(len(lowercase ) )]
# for each character in new_string find corresponding palindromic string
SCREAMING_SNAKE_CASE : int = 0
for j in range(len(lowercase ) ):
SCREAMING_SNAKE_CASE : List[Any] = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(lowercase )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
SCREAMING_SNAKE_CASE : List[Any] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
SCREAMING_SNAKE_CASE : Optional[Any] = j - k + 1 # noqa: E741
SCREAMING_SNAKE_CASE : Optional[Any] = j + k - 1
# update max_length and start position
if max_length < length[j]:
SCREAMING_SNAKE_CASE : Dict = length[j]
SCREAMING_SNAKE_CASE : str = j
# create that string
SCREAMING_SNAKE_CASE : int = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
|
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 319
| 1
|
from itertools import permutations
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
SCREAMING_SNAKE_CASE : Any = [7, 11, 13, 17]
for i, test in enumerate(lowercase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def lowerCamelCase__ ( lowercase = 10 ):
"""simple docstring"""
return sum(
int("".join(map(lowercase , lowercase ) ) )
for num in permutations(range(lowercase ) )
if is_substring_divisible(lowercase ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 319
|
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
snake_case = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = torch.load(lowercase , map_location="cpu" )
return sd
def lowerCamelCase__ ( lowercase , lowercase , lowercase=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = OrderedDict()
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE : Optional[Any] = key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE : Tuple = new_key.replace(name_pair[0] , name_pair[1] )
SCREAMING_SNAKE_CASE : Union[str, Any] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE : Union[str, Any] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE : str = "pretraining"
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE : str = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE : Optional[int] = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.''' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE : Optional[Any] = {"visual_embedding_dim": 512}
SCREAMING_SNAKE_CASE : Union[str, Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE : int = {"visual_embedding_dim": 2048}
SCREAMING_SNAKE_CASE : Any = "vqa_advanced"
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE : Any = {"visual_embedding_dim": 2048, "num_labels": 3129}
SCREAMING_SNAKE_CASE : Tuple = "vqa"
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE : int = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
SCREAMING_SNAKE_CASE : Union[str, Any] = "nlvr"
SCREAMING_SNAKE_CASE : List[Any] = VisualBertConfig(**lowercase )
# Load State Dict
SCREAMING_SNAKE_CASE : Union[str, Any] = load_state_dict(lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = get_new_dict(lowercase , lowercase )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE : Union[str, Any] = VisualBertForPreTraining(lowercase )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE : Optional[Any] = VisualBertForQuestionAnswering(lowercase )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE : Optional[Any] = VisualBertForVisualReasoning(lowercase )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE : List[Any] = VisualBertForMultipleChoice(lowercase )
model.load_state_dict(lowercase )
# Save Checkpoints
Path(lowercase ).mkdir(exist_ok=lowercase )
model.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
snake_case = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 319
| 1
|
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
snake_case = """sshleifer/mar_enro_6_3_student"""
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def _A ( self : Dict ):
super().setUp()
SCREAMING_SNAKE_CASE : Any = cached_path(
"https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz" , extract_compressed_file=UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Optional[int] = f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def _A ( self : int ):
MarianMTModel.from_pretrained(UpperCAmelCase_ )
@slow
@require_torch_gpu
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : Optional[Any] = {
"$MAX_LEN": 64,
"$BS": 64,
"$GAS": 1,
"$ENRO_DIR": self.data_dir,
"facebook/mbart-large-cc25": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"--learning_rate=3e-5": "--learning_rate 3e-4",
"--num_train_epochs 6": "--num_train_epochs 1",
}
# Clean up bash script
SCREAMING_SNAKE_CASE : Optional[Any] = (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py" )[1].strip()
SCREAMING_SNAKE_CASE : List[str] = bash_script.replace("\\\n" , "" ).strip().replace("\"$@\"" , "" )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE : int = bash_script.replace(UpperCAmelCase_ , str(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Dict = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
SCREAMING_SNAKE_CASE : Union[str, Any] = f'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
SCREAMING_SNAKE_CASE : Dict = ["finetune.py"] + bash_script.split() + args
with patch.object(UpperCAmelCase_ , "argv" , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : List[Any] = argparse.ArgumentParser()
SCREAMING_SNAKE_CASE : List[str] = pl.Trainer.add_argparse_args(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = SummarizationModule.add_model_specific_args(UpperCAmelCase_ , os.getcwd() )
SCREAMING_SNAKE_CASE : int = parser.parse_args()
SCREAMING_SNAKE_CASE : List[Any] = main(UpperCAmelCase_ )
# Check metrics
SCREAMING_SNAKE_CASE : List[str] = load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE : str = metrics["val"][0]
SCREAMING_SNAKE_CASE : Union[str, Any] = metrics["val"][-1]
self.assertEqual(len(metrics["val"] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}'''] , UpperCAmelCase_ )
self.assertGreater(last_step_stats["val_avg_gen_time"] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["val_avg_gen_time"] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["val_avg_bleu"] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE : Tuple = os.listdir(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = [x for x in contents if x.endswith(".ckpt" )][0]
SCREAMING_SNAKE_CASE : int = os.path.join(args.output_dir , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = torch.load(UpperCAmelCase_ , map_location="cpu" )
SCREAMING_SNAKE_CASE : Union[str, Any] = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE : Union[str, Any] = {os.path.basename(UpperCAmelCase_ ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["test"] ) == 1
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def _A ( self : List[str] ):
SCREAMING_SNAKE_CASE : Optional[int] = f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
SCREAMING_SNAKE_CASE : str = {
"--fp16_opt_level=O1": "",
"$MAX_LEN": 128,
"$BS": 16,
"$GAS": 1,
"$ENRO_DIR": data_dir,
"$m": "sshleifer/student_marian_en_ro_6_1",
"val_check_interval=0.25": "val_check_interval=1.0",
}
# Clean up bash script
SCREAMING_SNAKE_CASE : Any = (
(self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py" )[1].strip()
)
SCREAMING_SNAKE_CASE : Any = bash_script.replace("\\\n" , "" ).strip().replace("\"$@\"" , "" )
SCREAMING_SNAKE_CASE : Dict = bash_script.replace("--fp16 " , " " )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE : Union[str, Any] = bash_script.replace(UpperCAmelCase_ , str(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Tuple = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE : str = bash_script.replace("--fp16" , "" )
SCREAMING_SNAKE_CASE : Optional[int] = 6
SCREAMING_SNAKE_CASE : str = (
["distillation.py"]
+ bash_script.split()
+ [
f'''--output_dir={output_dir}''',
"--gpus=1",
"--learning_rate=1e-3",
f'''--num_train_epochs={epochs}''',
"--warmup_steps=10",
"--val_check_interval=1.0",
"--do_predict",
]
)
with patch.object(UpperCAmelCase_ , "argv" , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
SCREAMING_SNAKE_CASE : Optional[Any] = pl.Trainer.add_argparse_args(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = SummarizationDistiller.add_model_specific_args(UpperCAmelCase_ , os.getcwd() )
SCREAMING_SNAKE_CASE : Tuple = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
SCREAMING_SNAKE_CASE : List[Any] = distill_main(UpperCAmelCase_ )
# Check metrics
SCREAMING_SNAKE_CASE : Optional[Any] = load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE : str = metrics["val"][0]
SCREAMING_SNAKE_CASE : Dict = metrics["val"][-1]
assert len(metrics["val"] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}'''] , UpperCAmelCase_ )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE : int = os.listdir(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = [x for x in contents if x.endswith(".ckpt" )][0]
SCREAMING_SNAKE_CASE : Any = os.path.join(args.output_dir , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = torch.load(UpperCAmelCase_ , map_location="cpu" )
SCREAMING_SNAKE_CASE : Tuple = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE : Any = {os.path.basename(UpperCAmelCase_ ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["test"] ) == 1
| 319
|
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''ClapFeatureExtractor'''
UpperCamelCase_ : Any = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : List[str]=None , **UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("sampling_rate" , UpperCAmelCase_ )
if text is None and audios is None:
raise ValueError("You have to specify either text or audios. Both cannot be none." )
if text is not None:
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if audios is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.feature_extractor(
UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None and audios is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase_ ) , tensor_type=UpperCAmelCase_ )
def _A ( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : str ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : List[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Any ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@property
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Any = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE : List[Any] = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 319
| 1
|
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = RoCBertTokenizer
UpperCamelCase_ : Dict = None
UpperCamelCase_ : Optional[int] = False
UpperCamelCase_ : List[Any] = True
UpperCamelCase_ : List[Any] = filter_non_english
def _A ( self : Tuple ):
super().setUp()
SCREAMING_SNAKE_CASE : List[str] = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
SCREAMING_SNAKE_CASE : Optional[Any] = {}
SCREAMING_SNAKE_CASE : Optional[int] = {}
for i, value in enumerate(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = i
SCREAMING_SNAKE_CASE : List[Any] = i
SCREAMING_SNAKE_CASE : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_shape_file"] )
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_pronunciation_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
with open(self.word_shape_file , "w" , encoding="utf-8" ) as word_shape_writer:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ , ensure_ascii=UpperCAmelCase_ )
with open(self.word_pronunciation_file , "w" , encoding="utf-8" ) as word_pronunciation_writer:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ , ensure_ascii=UpperCAmelCase_ )
def _A ( self : str ):
SCREAMING_SNAKE_CASE : str = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
SCREAMING_SNAKE_CASE : int = tokenizer.tokenize("你好[SEP]你是谁" )
self.assertListEqual(UpperCAmelCase_ , ["你", "好", "[SEP]", "你", "是", "谁"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(UpperCAmelCase_ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(UpperCAmelCase_ ) , [5, 6, 2, 5, 7, 8] )
def _A ( self : List[Any] ):
SCREAMING_SNAKE_CASE : Dict = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : Any = RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _A ( self : int ):
SCREAMING_SNAKE_CASE : int = RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , strip_accents=UpperCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : int = RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , strip_accents=UpperCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _A ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE : Optional[int] = RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _A ( self : str ):
SCREAMING_SNAKE_CASE : Optional[Any] = RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def _A ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE : Tuple = RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , strip_accents=UpperCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : str = RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , strip_accents=UpperCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : Tuple = RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : int = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
for i, token in enumerate(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Dict = i
SCREAMING_SNAKE_CASE : Any = RoCBertWordpieceTokenizer(vocab=UpperCAmelCase_ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def _A ( self : int ):
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def _A ( self : Tuple ):
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def _A ( self : int ):
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def _A ( self : int ):
SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(UpperCAmelCase_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
if self.test_rust_tokenizer:
SCREAMING_SNAKE_CASE : List[Any] = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(UpperCAmelCase_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
def _A ( self : int ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE : Tuple = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.encode_plus(
UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , return_token_type_ids=UpperCAmelCase_ , return_offsets_mapping=UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = tokenizer_r.do_lower_case if hasattr(UpperCAmelCase_ , "do_lower_case" ) else False
SCREAMING_SNAKE_CASE : List[Any] = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def _A ( self : str ):
SCREAMING_SNAKE_CASE : List[str] = ["的", "人", "有"]
SCREAMING_SNAKE_CASE : Optional[int] = "".join(UpperCAmelCase_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_p.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = tokenizer_r.convert_ids_to_tokens(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = tokenizer_p.convert_ids_to_tokens(UpperCAmelCase_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : List[Any] = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = self.tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = tokenizer_r.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer_p.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.convert_ids_to_tokens(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer_p.convert_ids_to_tokens(UpperCAmelCase_ )
# it is expected that only the first Chinese character is not preceded by "##".
SCREAMING_SNAKE_CASE : Optional[int] = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(UpperCAmelCase_ )
]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Dict ):
SCREAMING_SNAKE_CASE : int = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.encode("你好" , add_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = tokenizer.encode("你是谁" , add_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ , UpperCAmelCase_ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _A ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_tokenizers(do_lower_case=UpperCAmelCase_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE : List[str] = "你好，你是谁"
SCREAMING_SNAKE_CASE : int = tokenizer.tokenize(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = tokenizer.convert_tokens_to_shape_ids(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.convert_tokens_to_pronunciation_ids(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.prepare_for_model(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = tokenizer.encode_plus(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
| 319
|
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Any = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : Optional[int] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Any = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(lowercase , cache_dir=lowercase , split=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = parquet_path
elif issubclass(lowercase , lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = [parquet_path]
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Tuple = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_dataset(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase=("train",) ):
"""simple docstring"""
assert isinstance(lowercase , lowercase )
for split in splits:
SCREAMING_SNAKE_CASE : Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Dict = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE : str = ParquetDatasetReader(
{"train": parquet_path} , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = tmp_path / "cache"
SCREAMING_SNAKE_CASE : Optional[int] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : Dict = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE : str = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE : Optional[Any] = ParquetDatasetReader({"train": parquet_path} , features=lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if split:
SCREAMING_SNAKE_CASE : Any = {split: parquet_path}
else:
SCREAMING_SNAKE_CASE : Tuple = "train"
SCREAMING_SNAKE_CASE : int = {"train": parquet_path, "test": parquet_path}
SCREAMING_SNAKE_CASE : Dict = tmp_path / "cache"
SCREAMING_SNAKE_CASE : str = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
SCREAMING_SNAKE_CASE : int = ParquetDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_parquet_datasetdict(lowercase , lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : Tuple = pq.ParquetFile(tmp_path / "foo.parquet" )
SCREAMING_SNAKE_CASE : List[Any] = pf.read()
assert dataset.data.table == output_table
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = str(shared_datadir / "test_image_rgb.jpg" )
SCREAMING_SNAKE_CASE : Union[str, Any] = {"image": [image_path]}
SCREAMING_SNAKE_CASE : Union[str, Any] = Features({"image": Image()} )
SCREAMING_SNAKE_CASE : int = Dataset.from_dict(lowercase , features=lowercase )
SCREAMING_SNAKE_CASE : List[str] = ParquetDatasetWriter(lowercase , tmp_path / "foo.parquet" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE : str = Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
assert dataset.features == reloaded_dataset.features
SCREAMING_SNAKE_CASE : Any = ParquetDatasetReader(str(tmp_path / "foo.parquet" ) , streaming=lowercase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32" )} ), None),
(Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
assert get_writer_batch_size(lowercase ) == expected
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| 1
|
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
def lowerCamelCase__ ( lowercase , lowercase=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "deit.embeddings.cls_token"),
("dist_token", "deit.embeddings.distillation_token"),
("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "deit.embeddings.position_embeddings"),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "deit" from all keys that start with "deit"
SCREAMING_SNAKE_CASE : Dict = [(pair[0], pair[1][4:]) if pair[1].startswith("deit" ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
("norm.weight", "deit.layernorm.weight"),
("norm.bias", "deit.layernorm.bias"),
("head.weight", "cls_classifier.weight"),
("head.bias", "cls_classifier.bias"),
("head_dist.weight", "distillation_classifier.weight"),
("head_dist.bias", "distillation_classifier.bias"),
] )
return rename_keys
def lowerCamelCase__ ( lowercase , lowercase , lowercase=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
SCREAMING_SNAKE_CASE : Dict = ""
else:
SCREAMING_SNAKE_CASE : Tuple = "deit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE : int = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE : List[str] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE : Dict = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE : Tuple = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE : Dict = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE : Optional[Any] = in_proj_bias[-config.hidden_size :]
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = dct.pop(lowercase )
SCREAMING_SNAKE_CASE : List[Any] = val
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE : Tuple = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = DeiTConfig()
# all deit models have fine-tuned heads
SCREAMING_SNAKE_CASE : List[str] = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
SCREAMING_SNAKE_CASE : Tuple = 1000
SCREAMING_SNAKE_CASE : Dict = "huggingface/label-files"
SCREAMING_SNAKE_CASE : Tuple = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE : Union[str, Any] = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = {int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Optional[Any] = idalabel
SCREAMING_SNAKE_CASE : Dict = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Union[str, Any] = int(deit_name[-6:-4] )
SCREAMING_SNAKE_CASE : int = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith("tiny" ):
SCREAMING_SNAKE_CASE : List[Any] = 192
SCREAMING_SNAKE_CASE : Union[str, Any] = 768
SCREAMING_SNAKE_CASE : Optional[int] = 12
SCREAMING_SNAKE_CASE : Tuple = 3
elif deit_name[9:].startswith("small" ):
SCREAMING_SNAKE_CASE : str = 384
SCREAMING_SNAKE_CASE : Optional[Any] = 1536
SCREAMING_SNAKE_CASE : List[str] = 12
SCREAMING_SNAKE_CASE : int = 6
if deit_name[9:].startswith("base" ):
pass
elif deit_name[4:].startswith("large" ):
SCREAMING_SNAKE_CASE : int = 1024
SCREAMING_SNAKE_CASE : Any = 4096
SCREAMING_SNAKE_CASE : int = 24
SCREAMING_SNAKE_CASE : Union[str, Any] = 16
# load original model from timm
SCREAMING_SNAKE_CASE : Tuple = timm.create_model(lowercase , pretrained=lowercase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE : int = timm_model.state_dict()
SCREAMING_SNAKE_CASE : Union[str, Any] = create_rename_keys(lowercase , lowercase )
for src, dest in rename_keys:
rename_key(lowercase , lowercase , lowercase )
read_in_q_k_v(lowercase , lowercase , lowercase )
# load HuggingFace model
SCREAMING_SNAKE_CASE : Union[str, Any] = DeiTForImageClassificationWithTeacher(lowercase ).eval()
model.load_state_dict(lowercase )
# Check outputs on an image, prepared by DeiTImageProcessor
SCREAMING_SNAKE_CASE : Union[str, Any] = int(
(256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
SCREAMING_SNAKE_CASE : Dict = DeiTImageProcessor(size=lowercase , crop_size=config.image_size )
SCREAMING_SNAKE_CASE : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" )
SCREAMING_SNAKE_CASE : int = encoding["pixel_values"]
SCREAMING_SNAKE_CASE : Optional[Any] = model(lowercase )
SCREAMING_SNAKE_CASE : Dict = timm_model(lowercase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowercase , outputs.logits , atol=1E-3 )
Path(lowercase ).mkdir(exist_ok=lowercase )
print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--deit_name""",
default="""vit_deit_base_distilled_patch16_224""",
type=str,
help="""Name of the DeiT timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
snake_case = parser.parse_args()
convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
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|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
snake_case = logging.getLogger(__name__)
@dataclass(frozen=lowerCAmelCase )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
UpperCamelCase_ : str
UpperCamelCase_ : str
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
UpperCamelCase_ : Optional[str] = None
@dataclass(frozen=lowerCAmelCase )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
UpperCamelCase_ : List[int]
UpperCamelCase_ : Optional[List[int]] = None
UpperCamelCase_ : Optional[List[int]] = None
UpperCamelCase_ : Optional[Union[int, float]] = None
UpperCamelCase_ : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[InputFeatures]
def __init__( self : Optional[int] , UpperCAmelCase_ : str , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[int]=False , UpperCAmelCase_ : bool = False , ):
SCREAMING_SNAKE_CASE : Tuple = hans_processors[task]()
SCREAMING_SNAKE_CASE : Dict = os.path.join(
UpperCAmelCase_ , "cached_{}_{}_{}_{}".format(
"dev" if evaluate else "train" , tokenizer.__class__.__name__ , str(UpperCAmelCase_ ) , UpperCAmelCase_ , ) , )
SCREAMING_SNAKE_CASE : str = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = label_list[2], label_list[1]
SCREAMING_SNAKE_CASE : List[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
SCREAMING_SNAKE_CASE : Tuple = cached_features_file + ".lock"
with FileLock(UpperCAmelCase_ ):
if os.path.exists(UpperCAmelCase_ ) and not overwrite_cache:
logger.info(f'''Loading features from cached file {cached_features_file}''' )
SCREAMING_SNAKE_CASE : int = torch.load(UpperCAmelCase_ )
else:
logger.info(f'''Creating features from dataset file at {data_dir}''' )
SCREAMING_SNAKE_CASE : List[Any] = (
processor.get_dev_examples(UpperCAmelCase_ ) if evaluate else processor.get_train_examples(UpperCAmelCase_ )
)
logger.info("Training examples: %s" , len(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Any = hans_convert_examples_to_features(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
logger.info("Saving features into cached file %s" , UpperCAmelCase_ )
torch.save(self.features , UpperCAmelCase_ )
def __len__( self : Optional[Any] ):
return len(self.features )
def __getitem__( self : List[Any] , UpperCAmelCase_ : str ):
return self.features[i]
def _A ( self : List[str] ):
return self.label_list
if is_tf_available():
import tensorflow as tf
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
UpperCamelCase_ : List[InputFeatures]
def __init__( self : Any , UpperCAmelCase_ : str , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[int] = 128 , UpperCAmelCase_ : Optional[Any]=False , UpperCAmelCase_ : bool = False , ):
SCREAMING_SNAKE_CASE : Union[str, Any] = hans_processors[task]()
SCREAMING_SNAKE_CASE : Tuple = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = label_list[2], label_list[1]
SCREAMING_SNAKE_CASE : Dict = label_list
SCREAMING_SNAKE_CASE : Any = processor.get_dev_examples(UpperCAmelCase_ ) if evaluate else processor.get_train_examples(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = hans_convert_examples_to_features(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc="convert examples to features" ):
if ex_index % 1_0000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(UpperCAmelCase_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
SCREAMING_SNAKE_CASE : Optional[int] = tf.data.Dataset.from_generator(
UpperCAmelCase_ , (
{
"example_id": tf.intaa,
"input_ids": tf.intaa,
"attention_mask": tf.intaa,
"token_type_ids": tf.intaa,
},
tf.intaa,
) , (
{
"example_id": tf.TensorShape([] ),
"input_ids": tf.TensorShape([None, None] ),
"attention_mask": tf.TensorShape([None, None] ),
"token_type_ids": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _A ( self : Tuple ):
return self.dataset
def __len__( self : Optional[int] ):
return len(self.features )
def __getitem__( self : List[Any] , UpperCAmelCase_ : Union[str, Any] ):
return self.features[i]
def _A ( self : List[str] ):
return self.label_list
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def _A ( self : Any , UpperCAmelCase_ : Any ):
return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_ , "heuristics_train_set.txt" ) ) , "train" )
def _A ( self : str , UpperCAmelCase_ : Optional[int] ):
return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_ , "heuristics_evaluation_set.txt" ) ) , "dev" )
def _A ( self : Any ):
return ["contradiction", "entailment", "neutral"]
def _A ( self : Tuple , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Tuple = []
for i, line in enumerate(UpperCAmelCase_ ):
if i == 0:
continue
SCREAMING_SNAKE_CASE : Optional[int] = "%s-%s" % (set_type, line[0])
SCREAMING_SNAKE_CASE : Optional[int] = line[5]
SCREAMING_SNAKE_CASE : List[str] = line[6]
SCREAMING_SNAKE_CASE : Optional[int] = line[7][2:] if line[7].startswith("ex" ) else line[7]
SCREAMING_SNAKE_CASE : int = line[0]
examples.append(InputExample(guid=UpperCAmelCase_ , text_a=UpperCAmelCase_ , text_b=UpperCAmelCase_ , label=UpperCAmelCase_ , pairID=UpperCAmelCase_ ) )
return examples
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = {label: i for i, label in enumerate(lowercase )}
SCREAMING_SNAKE_CASE : Optional[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(lowercase ) , desc="convert examples to features" ):
if ex_index % 10000 == 0:
logger.info("Writing example %d" % (ex_index) )
SCREAMING_SNAKE_CASE : Tuple = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowercase , max_length=lowercase , padding="max_length" , truncation=lowercase , return_overflowing_tokens=lowercase , )
SCREAMING_SNAKE_CASE : Tuple = label_map[example.label] if example.label in label_map else 0
SCREAMING_SNAKE_CASE : Optional[int] = int(example.pairID )
features.append(InputFeatures(**lowercase , label=lowercase , pairID=lowercase ) )
for i, example in enumerate(examples[:5] ):
logger.info("*** Example ***" )
logger.info(F'''guid: {example}''' )
logger.info(F'''features: {features[i]}''' )
return features
snake_case = {
"""hans""": 3,
}
snake_case = {
"""hans""": HansProcessor,
}
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|
def lowerCamelCase__ ( lowercase , lowercase = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = length or len(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = list_data[i + 1], list_data[i]
SCREAMING_SNAKE_CASE : str = True
return list_data if not swapped else bubble_sort(lowercase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
def count_of_possible_combinations(lowercase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
lowercase , lowercase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
SCREAMING_SNAKE_CASE : Tuple = sum(
count_of_possible_combinations_with_dp_array(target - item , lowercase )
for item in array )
SCREAMING_SNAKE_CASE : Optional[int] = answer
return answer
SCREAMING_SNAKE_CASE : Tuple = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(lowercase , lowercase )
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [0] * (target + 1)
SCREAMING_SNAKE_CASE : Optional[Any] = 1
for i in range(1 , target + 1 ):
for j in range(lowercase ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case = 3
snake_case = 5
snake_case = [1, 2, 5]
print(combination_sum_iv(n, array, target))
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import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case = get_logger(__name__)
snake_case = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : str , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[int] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ):
for processor in self:
SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(processor.__call__ ).parameters
if len(UpperCAmelCase_ ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
f'''Make sure that all the required parameters: {list(function_args.keys() )} for '''
f'''{processor.__class__} are passed to the logits processor.''' )
SCREAMING_SNAKE_CASE : int = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , **UpperCAmelCase_ )
else:
SCREAMING_SNAKE_CASE : Dict = processor(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : float ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not (temperature > 0):
raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}''' )
SCREAMING_SNAKE_CASE : Optional[int] = temperature
def __call__( self : List[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = scores / self.temperature
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : float , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (top_p < 0 or top_p > 1.0):
raise ValueError(f'''`top_p` has to be a float > 0 and < 1, but is {top_p}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or (min_tokens_to_keep < 1):
raise ValueError(f'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}''' )
SCREAMING_SNAKE_CASE : Optional[int] = top_p
SCREAMING_SNAKE_CASE : str = filter_value
SCREAMING_SNAKE_CASE : List[str] = min_tokens_to_keep
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = lax.top_k(UpperCAmelCase_ , scores.shape[-1] )
SCREAMING_SNAKE_CASE : str = jnp.full_like(UpperCAmelCase_ , self.filter_value )
SCREAMING_SNAKE_CASE : Optional[int] = jax.nn.softmax(UpperCAmelCase_ , axis=-1 ).cumsum(axis=-1 )
SCREAMING_SNAKE_CASE : Tuple = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
SCREAMING_SNAKE_CASE : Optional[int] = jnp.roll(UpperCAmelCase_ , 1 )
score_mask |= score_mask.at[:, 0].set(UpperCAmelCase_ )
# min tokens to keep
SCREAMING_SNAKE_CASE : Union[str, Any] = score_mask.at[:, : self.min_tokens_to_keep].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = jnp.where(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jax.lax.sort_key_val(UpperCAmelCase_ , UpperCAmelCase_ )[-1]
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : float = -float("Inf" ) , UpperCAmelCase_ : int = 1 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or top_k <= 0:
raise ValueError(f'''`top_k` has to be a strictly positive integer, but is {top_k}''' )
SCREAMING_SNAKE_CASE : List[str] = max(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = filter_value
def __call__( self : Dict , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = scores.shape
SCREAMING_SNAKE_CASE : List[str] = jnp.full(batch_size * vocab_size , self.filter_value )
SCREAMING_SNAKE_CASE : List[str] = min(self.top_k , scores.shape[-1] ) # Safety check
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = lax.top_k(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = jnp.broadcast_to((jnp.arange(UpperCAmelCase_ ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
SCREAMING_SNAKE_CASE : List[str] = topk_scores.flatten()
SCREAMING_SNAKE_CASE : List[Any] = topk_indices.flatten() + shift
SCREAMING_SNAKE_CASE : Dict = next_scores_flat.at[topk_indices_flat].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = next_scores_flat.reshape(UpperCAmelCase_ , UpperCAmelCase_ )
return next_scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = bos_token_id
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Dict = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.bool_(cur_len - 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.bos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = max_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : List[str] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[str] = jnp.full(scores.shape , -float("inf" ) )
SCREAMING_SNAKE_CASE : str = 1 - jnp.bool_(cur_len - self.max_length + 1 )
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(UpperCAmelCase_ , new_scores.at[:, self.eos_token_id].set(0 ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or min_length < 0:
raise ValueError(f'''`min_length` has to be a positive integer, but is {min_length}''' )
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or eos_token_id < 0:
raise ValueError(f'''`eos_token_id` has to be a positive integer, but is {eos_token_id}''' )
SCREAMING_SNAKE_CASE : List[str] = min_length
SCREAMING_SNAKE_CASE : Tuple = eos_token_id
def __call__( self : Optional[Any] , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
# create boolean flag to decide if min length penalty should be applied
SCREAMING_SNAKE_CASE : Optional[int] = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
SCREAMING_SNAKE_CASE : Optional[int] = jnp.where(UpperCAmelCase_ , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Optional[Any] = list(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = begin_index
def __call__( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Union[str, Any] = 1 - jnp.bool_(cur_len - self.begin_index )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(UpperCAmelCase_ , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , UpperCAmelCase_ )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : List[Any] = list(UpperCAmelCase_ )
def __call__( self : Any , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : Tuple = scores.at[..., self.suppress_tokens].set(-float("inf" ) )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : List[Any] = dict(UpperCAmelCase_ )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
SCREAMING_SNAKE_CASE : Any = force_token_array.at[index].set(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = jnp.intaa(UpperCAmelCase_ )
def __call__( self : Tuple , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : jnp.ndarray , UpperCAmelCase_ : int ):
def _force_token(UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : List[str] = scores.shape[0]
SCREAMING_SNAKE_CASE : Optional[int] = self.force_token_array[generation_idx]
SCREAMING_SNAKE_CASE : Tuple = jnp.ones_like(UpperCAmelCase_ , dtype=scores.dtype ) * -float("inf" )
SCREAMING_SNAKE_CASE : Dict = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
SCREAMING_SNAKE_CASE : Optional[Any] = lax.dynamic_update_slice(UpperCAmelCase_ , UpperCAmelCase_ , (0, current_token) )
return new_scores
SCREAMING_SNAKE_CASE : Any = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(UpperCAmelCase_ ) , lambda: scores , ) , )
return scores
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : Union[str, Any] = generate_config.eos_token_id
SCREAMING_SNAKE_CASE : Tuple = generate_config.no_timestamps_token_id
SCREAMING_SNAKE_CASE : List[Any] = generate_config.no_timestamps_token_id + 1
SCREAMING_SNAKE_CASE : Dict = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(UpperCAmelCase_ , "max_initial_timestamp_index" ):
SCREAMING_SNAKE_CASE : List[Any] = generate_config.max_initial_timestamp_index
else:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
if self.max_initial_timestamp_index is None:
SCREAMING_SNAKE_CASE : List[str] = model_config.vocab_size
def __call__( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
# suppress <|notimestamps|> which is handled by without_timestamps
SCREAMING_SNAKE_CASE : int = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) )
def handle_pairs(UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) >= 1 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Tuple = jnp.where((cur_len - self.begin_index) < 2 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , UpperCAmelCase_ , UpperCAmelCase_ , )
return jnp.where(
UpperCAmelCase_ , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : Optional[Any] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.where(cur_len == self.begin_index , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = self.timestamp_begin + self.max_initial_timestamp_index
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.where(
UpperCAmelCase_ , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , UpperCAmelCase_ , )
# if sum of probability over timestamps is above any other token, sample timestamp
SCREAMING_SNAKE_CASE : List[Any] = jax.nn.log_softmax(UpperCAmelCase_ , axis=-1 )
def handle_cumulative_probs(UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = jax.vmap(UpperCAmelCase_ )(UpperCAmelCase_ , UpperCAmelCase_ )
return scores
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|
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
snake_case = logging.getLogger(__name__)
snake_case = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowerCAmelCase )} , )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase_ : bool = field(
default=lowerCAmelCase , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase_ : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase_ : bool = field(
default=lowerCAmelCase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def _A ( self : Optional[Any] ):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path" )
@dataclass
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
UpperCamelCase_ : Optional[str] = field(default=lowerCAmelCase , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''} , )
UpperCamelCase_ : Optional[str] = field(
default=lowerCAmelCase , metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''} , )
UpperCamelCase_ : bool = field(
default=lowerCAmelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCamelCase_ : Optional[int] = field(
default=5 , metadata={
'''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
} , )
UpperCamelCase_ : Optional[int] = field(
default=lowerCAmelCase , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated. Default to the max input length of the model.'''
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=lowerCAmelCase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCamelCase_ : float = field(
default=0.15 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
UpperCamelCase_ : bool = field(
default=lowerCAmelCase , 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.'''
)
} , )
def _A ( self : int ):
if self.train_file is not None:
SCREAMING_SNAKE_CASE : Dict = self.train_file.split("." )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
SCREAMING_SNAKE_CASE : Tuple = self.validation_file.split("." )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
with open(lowercase , "r" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE : Optional[int] = [json.loads(lowercase ) for line in f.read().splitlines() if (len(lowercase ) > 0 and not line.isspace())]
assert len(lowercase ) == len(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = {c: dataset[c] for c in dataset.column_names}
SCREAMING_SNAKE_CASE : Dict = refs
return Dataset.from_dict(lowercase )
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 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.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE : Optional[Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE : List[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:
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." )
# 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 )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}''' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , lowercase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
SCREAMING_SNAKE_CASE : Optional[int] = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''train[:{data_args.validation_split_percentage}%]''' , )
SCREAMING_SNAKE_CASE : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''train[{data_args.validation_split_percentage}%:]''' , )
else:
SCREAMING_SNAKE_CASE : Optional[Any] = {}
if data_args.train_file is not None:
SCREAMING_SNAKE_CASE : Tuple = data_args.train_file
if data_args.validation_file is not None:
SCREAMING_SNAKE_CASE : Optional[int] = data_args.validation_file
SCREAMING_SNAKE_CASE : Optional[Any] = data_args.train_file.split("." )[-1]
if extension == "txt":
SCREAMING_SNAKE_CASE : List[str] = "text"
SCREAMING_SNAKE_CASE : Optional[int] = load_dataset(lowercase , data_files=lowercase )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE : List[Any] = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(model_args.config_name , **lowercase )
elif model_args.model_name_or_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(model_args.model_name_or_path , **lowercase )
else:
SCREAMING_SNAKE_CASE : List[Any] = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
SCREAMING_SNAKE_CASE : Any = {
"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,
}
if model_args.tokenizer_name:
SCREAMING_SNAKE_CASE : List[str] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **lowercase )
elif model_args.model_name_or_path:
SCREAMING_SNAKE_CASE : List[str] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **lowercase )
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name." )
if model_args.model_name_or_path:
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("Training new model from scratch" )
SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForMaskedLM.from_config(lowercase )
model.resize_token_embeddings(len(lowercase ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
SCREAMING_SNAKE_CASE : str = datasets["train"].column_names
else:
SCREAMING_SNAKE_CASE : List[str] = datasets["validation"].column_names
SCREAMING_SNAKE_CASE : List[str] = "text" if "text" in column_names else column_names[0]
SCREAMING_SNAKE_CASE : List[Any] = "max_length" if data_args.pad_to_max_length else False
def tokenize_function(lowercase ):
# Remove empty lines
SCREAMING_SNAKE_CASE : Any = [line for line in examples["text"] if len(lowercase ) > 0 and not line.isspace()]
return tokenizer(examples["text"] , padding=lowercase , truncation=lowercase , max_length=data_args.max_seq_length )
SCREAMING_SNAKE_CASE : Tuple = datasets.map(
lowercase , batched=lowercase , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = add_chinese_references(tokenized_datasets["train"] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
SCREAMING_SNAKE_CASE : Optional[Any] = add_chinese_references(
tokenized_datasets["validation"] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
SCREAMING_SNAKE_CASE : Tuple = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
SCREAMING_SNAKE_CASE : List[str] = False
# Data collator
# This one will take care of randomly masking the tokens.
SCREAMING_SNAKE_CASE : List[Any] = DataCollatorForWholeWordMask(tokenizer=lowercase , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
SCREAMING_SNAKE_CASE : List[Any] = Trainer(
model=lowercase , args=lowercase , train_dataset=tokenized_datasets["train"] if training_args.do_train else None , eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None , tokenizer=lowercase , data_collator=lowercase , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
SCREAMING_SNAKE_CASE : Tuple = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
SCREAMING_SNAKE_CASE : Dict = model_args.model_name_or_path
else:
SCREAMING_SNAKE_CASE : List[str] = None
SCREAMING_SNAKE_CASE : Dict = trainer.train(resume_from_checkpoint=lowercase )
trainer.save_model() # Saves the tokenizer too for easy upload
SCREAMING_SNAKE_CASE : Dict = os.path.join(training_args.output_dir , "train_results.txt" )
if trainer.is_world_process_zero():
with open(lowercase , "w" ) as writer:
logger.info("***** Train results *****" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F''' {key} = {value}''' )
writer.write(F'''{key} = {value}\n''' )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# Evaluation
SCREAMING_SNAKE_CASE : Optional[int] = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
SCREAMING_SNAKE_CASE : str = trainer.evaluate()
SCREAMING_SNAKE_CASE : Dict = math.exp(eval_output["eval_loss"] )
SCREAMING_SNAKE_CASE : Optional[Any] = perplexity
SCREAMING_SNAKE_CASE : int = os.path.join(training_args.output_dir , "eval_results_mlm_wwm.txt" )
if trainer.is_world_process_zero():
with open(lowercase , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in sorted(results.items() ):
logger.info(F''' {key} = {value}''' )
writer.write(F'''{key} = {value}\n''' )
return results
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 319
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
snake_case = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
print("""Torch version:""", None)
try:
import transformers
print("""transformers version:""", transformers.__version__)
except ImportError:
print("""transformers version:""", None)
| 319
| 1
|
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = (KDPMaDiscreteScheduler,)
UpperCamelCase_ : Any = 1_0
def _A ( self : List[str] , **UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE : Any = {
"num_train_timesteps": 1100,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**UpperCAmelCase_ )
return config
def _A ( self : List[str] ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )
def _A ( self : Optional[Any] ):
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=UpperCAmelCase_ , beta_end=UpperCAmelCase_ )
def _A ( self : Tuple ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=UpperCAmelCase_ )
def _A ( self : str ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase_ )
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : List[str] = self.get_scheduler_config(prediction_type="v_prediction" )
SCREAMING_SNAKE_CASE : Optional[Any] = scheduler_class(**UpperCAmelCase_ )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE : Any = self.dummy_model()
SCREAMING_SNAKE_CASE : Tuple = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE : Tuple = sample.to(UpperCAmelCase_ )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE : Optional[int] = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = model(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = output.prev_sample
SCREAMING_SNAKE_CASE : str = torch.sum(torch.abs(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = torch.mean(torch.abs(UpperCAmelCase_ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6_934E-07 ) < 1E-2
assert abs(result_mean.item() - 6.1_112E-10 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 4.693_428_650_170_972E-07 ) < 1E-2
assert abs(result_mean.item() - 0.0_002 ) < 1E-3
def _A ( self : Any ):
if torch_device == "mps":
return
SCREAMING_SNAKE_CASE : List[str] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Optional[Any] = scheduler_class(**UpperCAmelCase_ )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE : int = self.dummy_model()
SCREAMING_SNAKE_CASE : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE : List[Any] = sample.to(UpperCAmelCase_ )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE : Tuple = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = model(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = output.prev_sample
SCREAMING_SNAKE_CASE : Optional[Any] = torch.sum(torch.abs(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : int = torch.mean(torch.abs(UpperCAmelCase_ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
def _A ( self : Dict ):
if torch_device == "mps":
return
SCREAMING_SNAKE_CASE : Dict = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Any = scheduler_class(**UpperCAmelCase_ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE : List[str] = self.dummy_sample_deter.to(UpperCAmelCase_ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE : Union[str, Any] = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = model(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = output.prev_sample
SCREAMING_SNAKE_CASE : Any = torch.sum(torch.abs(UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.mean(torch.abs(UpperCAmelCase_ ) )
if str(UpperCAmelCase_ ).startswith("cpu" ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
| 319
|
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 319
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""SpeechEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""FlaxSpeechEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
|
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
snake_case = {
"""b0""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 224,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_280,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 240,
"""dropout_rate""": 0.2,
"""dw_padding""": [16],
},
"""b2""": {
"""hidden_dim""": 1_408,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 260,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 16],
},
"""b3""": {
"""hidden_dim""": 1_536,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 300,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 18],
},
"""b4""": {
"""hidden_dim""": 1_792,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 380,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_048,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 456,
"""dropout_rate""": 0.4,
"""dw_padding""": [13, 27],
},
"""b6""": {
"""hidden_dim""": 2_304,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 528,
"""dropout_rate""": 0.5,
"""dw_padding""": [31],
},
"""b7""": {
"""hidden_dim""": 2_560,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 600,
"""dropout_rate""": 0.5,
"""dw_padding""": [18],
},
}
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = EfficientNetConfig()
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["hidden_dim"]
SCREAMING_SNAKE_CASE : Tuple = CONFIG_MAP[model_name]["width_coef"]
SCREAMING_SNAKE_CASE : Optional[int] = CONFIG_MAP[model_name]["depth_coef"]
SCREAMING_SNAKE_CASE : Union[str, Any] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = CONFIG_MAP[model_name]["dropout_rate"]
SCREAMING_SNAKE_CASE : str = CONFIG_MAP[model_name]["dw_padding"]
SCREAMING_SNAKE_CASE : str = "huggingface/label-files"
SCREAMING_SNAKE_CASE : str = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE : str = 1000
SCREAMING_SNAKE_CASE : List[Any] = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE : Tuple = {int(lowercase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Union[str, Any] = idalabel
SCREAMING_SNAKE_CASE : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE : List[Any] = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : int = EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=lowercase , )
return preprocessor
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
SCREAMING_SNAKE_CASE : List[str] = sorted(set(lowercase ) )
SCREAMING_SNAKE_CASE : List[str] = len(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = {b: str(lowercase ) for b, i in zip(lowercase , range(lowercase ) )}
SCREAMING_SNAKE_CASE : Dict = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
SCREAMING_SNAKE_CASE : Tuple = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
SCREAMING_SNAKE_CASE : int = {}
for item in rename_keys:
if item[0] in original_param_names:
SCREAMING_SNAKE_CASE : Any = "efficientnet." + item[1]
SCREAMING_SNAKE_CASE : Optional[Any] = "classifier.weight"
SCREAMING_SNAKE_CASE : List[str] = "classifier.bias"
return key_mapping
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
SCREAMING_SNAKE_CASE : str = key_mapping[key]
if "_conv" in key and "kernel" in key:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
SCREAMING_SNAKE_CASE : int = torch.from_numpy(lowercase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
SCREAMING_SNAKE_CASE : List[str] = torch.from_numpy(np.transpose(lowercase ) )
else:
SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(lowercase )
@torch.no_grad()
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = model_classes[model_name](
include_top=lowercase , weights="imagenet" , input_tensor=lowercase , input_shape=lowercase , pooling=lowercase , classes=1000 , classifier_activation="softmax" , )
SCREAMING_SNAKE_CASE : List[Any] = original_model.trainable_variables
SCREAMING_SNAKE_CASE : Dict = original_model.non_trainable_variables
SCREAMING_SNAKE_CASE : Dict = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
SCREAMING_SNAKE_CASE : Tuple = param.numpy()
SCREAMING_SNAKE_CASE : Tuple = list(tf_params.keys() )
# Load HuggingFace model
SCREAMING_SNAKE_CASE : Tuple = get_efficientnet_config(lowercase )
SCREAMING_SNAKE_CASE : str = EfficientNetForImageClassification(lowercase ).eval()
SCREAMING_SNAKE_CASE : Dict = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
SCREAMING_SNAKE_CASE : Dict = rename_keys(lowercase )
replace_params(lowercase , lowercase , lowercase )
# Initialize preprocessor and preprocess input image
SCREAMING_SNAKE_CASE : Optional[int] = convert_image_processor(lowercase )
SCREAMING_SNAKE_CASE : int = preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = hf_model(**lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.logits.detach().numpy()
# Original model inference
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAP[model_name]["image_size"]
SCREAMING_SNAKE_CASE : Any = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
SCREAMING_SNAKE_CASE : Tuple = image.img_to_array(lowercase )
SCREAMING_SNAKE_CASE : Tuple = np.expand_dims(lowercase , axis=0 )
SCREAMING_SNAKE_CASE : Any = original_model.predict(lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(lowercase , lowercase , atol=1E-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(lowercase ):
os.mkdir(lowercase )
# Save converted model and image processor
hf_model.save_pretrained(lowercase )
preprocessor.save_pretrained(lowercase )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(lowercase )
hf_model.push_to_hub(lowercase )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
snake_case = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
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import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
snake_case = {
"""vocab_file""": {
"""allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
"""allenai/longformer-large-4096""": (
"""https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
),
"""allenai/longformer-large-4096-finetuned-triviaqa""": (
"""https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
),
"""allenai/longformer-base-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
),
"""allenai/longformer-large-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
),
},
"""merges_file""": {
"""allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
"""allenai/longformer-large-4096""": (
"""https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
),
"""allenai/longformer-large-4096-finetuned-triviaqa""": (
"""https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
),
"""allenai/longformer-base-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
),
"""allenai/longformer-large-4096-extra.pos.embd.only""": (
"""https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
),
},
}
snake_case = {
"""allenai/longformer-base-4096""": 4_096,
"""allenai/longformer-large-4096""": 4_096,
"""allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
"""allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
"""allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCamelCase__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
SCREAMING_SNAKE_CASE : List[str] = bs[:]
SCREAMING_SNAKE_CASE : List[str] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE : int = [chr(lowercase ) for n in cs]
return dict(zip(lowercase , lowercase ) )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = set()
SCREAMING_SNAKE_CASE : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE : Dict = char
return pairs
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = VOCAB_FILES_NAMES
UpperCamelCase_ : Any = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self : Any , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int="replace" , UpperCAmelCase_ : str="<s>" , UpperCAmelCase_ : Tuple="</s>" , UpperCAmelCase_ : Dict="</s>" , UpperCAmelCase_ : List[str]="<s>" , UpperCAmelCase_ : Tuple="<unk>" , UpperCAmelCase_ : Optional[int]="<pad>" , UpperCAmelCase_ : Union[str, Any]="<mask>" , UpperCAmelCase_ : List[str]=False , **UpperCAmelCase_ : Any , ):
SCREAMING_SNAKE_CASE : Union[str, Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else bos_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else eos_token
SCREAMING_SNAKE_CASE : List[str] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else sep_token
SCREAMING_SNAKE_CASE : List[Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else cls_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else unk_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : Optional[int] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else mask_token
super().__init__(
errors=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , add_prefix_space=UpperCAmelCase_ , **UpperCAmelCase_ , )
with open(UpperCAmelCase_ , encoding="utf-8" ) as vocab_handle:
SCREAMING_SNAKE_CASE : str = json.load(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE : int = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE : Any = bytes_to_unicode()
SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.byte_encoder.items()}
with open(UpperCAmelCase_ , encoding="utf-8" ) as merges_handle:
SCREAMING_SNAKE_CASE : Tuple = merges_handle.read().split("\n" )[1:-1]
SCREAMING_SNAKE_CASE : Union[str, Any] = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE : Union[str, Any] = dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_ ) ) ) )
SCREAMING_SNAKE_CASE : Any = {}
SCREAMING_SNAKE_CASE : Optional[int] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE : Any = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def _A ( self : Optional[Any] ):
return len(self.encoder )
def _A ( self : Optional[int] ):
return dict(self.encoder , **self.added_tokens_encoder )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : Optional[int] ):
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE : Optional[Any] = tuple(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = get_pairs(UpperCAmelCase_ )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE : Tuple = min(UpperCAmelCase_ , key=lambda UpperCAmelCase_ : self.bpe_ranks.get(UpperCAmelCase_ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = bigram
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : List[Any] = 0
while i < len(UpperCAmelCase_ ):
try:
SCREAMING_SNAKE_CASE : Dict = word.index(UpperCAmelCase_ , UpperCAmelCase_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE : Optional[int] = j
if word[i] == first and i < len(UpperCAmelCase_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE : Union[str, Any] = tuple(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = new_word
if len(UpperCAmelCase_ ) == 1:
break
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = get_pairs(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = " ".join(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = word
return word
def _A ( self : Dict , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : List[Any] = []
for token in re.findall(self.pat , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Optional[int] = "".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(UpperCAmelCase_ ).split(" " ) )
return bpe_tokens
def _A ( self : str , UpperCAmelCase_ : List[str] ):
return self.encoder.get(UpperCAmelCase_ , self.encoder.get(self.unk_token ) )
def _A ( self : Optional[int] , UpperCAmelCase_ : List[str] ):
return self.decoder.get(UpperCAmelCase_ )
def _A ( self : Any , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : Tuple = "".join(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _A ( self : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : Dict = os.path.join(
UpperCAmelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(
UpperCAmelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(UpperCAmelCase_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCAmelCase_ , ensure_ascii=UpperCAmelCase_ ) + "\n" )
SCREAMING_SNAKE_CASE : Any = 0
with open(UpperCAmelCase_ , "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 UpperCAmelCase_ : 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!" )
SCREAMING_SNAKE_CASE : Dict = token_index
writer.write(" ".join(UpperCAmelCase_ ) + "\n" )
index += 1
return vocab_file, merge_file
def _A ( self : Optional[Any] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE : Tuple = [self.cls_token_id]
SCREAMING_SNAKE_CASE : Optional[int] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _A ( self : Union[str, Any] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None , UpperCAmelCase_ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase_ , token_ids_a=UpperCAmelCase_ , already_has_special_tokens=UpperCAmelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(UpperCAmelCase_ )) + [1]
return [1] + ([0] * len(UpperCAmelCase_ )) + [1, 1] + ([0] * len(UpperCAmelCase_ )) + [1]
def _A ( self : Optional[Any] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE : Dict = [self.sep_token_id]
SCREAMING_SNAKE_CASE : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _A ( self : Dict , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any]=False , **UpperCAmelCase_ : List[str] ):
SCREAMING_SNAKE_CASE : Any = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase_ ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE : int = " " + text
return (text, kwargs)
| 319
|
def lowerCamelCase__ ( ):
"""simple docstring"""
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
snake_case = generate_large_matrix()
snake_case = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
assert all(row == sorted(lowercase , reverse=lowercase ) for row in grid )
assert all(list(lowercase ) == sorted(lowercase , reverse=lowercase ) for col in zip(*lowercase ) )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowercase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
SCREAMING_SNAKE_CASE : List[Any] = (left + right) // 2
SCREAMING_SNAKE_CASE : Optional[int] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
SCREAMING_SNAKE_CASE : List[Any] = mid + 1
else:
SCREAMING_SNAKE_CASE : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowercase )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : List[str] = len(grid[0] )
for i in range(len(lowercase ) ):
SCREAMING_SNAKE_CASE : Any = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowercase ) * len(grid[0] )) - total
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
return len([number for row in grid for number in row if number < 0] )
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = 0
for row in grid:
for i, number in enumerate(lowercase ):
if number < 0:
total += len(lowercase ) - i
break
return total
def lowerCamelCase__ ( ):
"""simple docstring"""
from timeit import timeit
print("Running benchmarks" )
SCREAMING_SNAKE_CASE : List[str] = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
SCREAMING_SNAKE_CASE : Union[str, Any] = timeit(F'''{func}(grid=grid)''' , setup=lowercase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 319
| 1
|
from collections import Counter
from timeit import timeit
def lowerCamelCase__ ( lowercase = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def lowerCamelCase__ ( lowercase = "" ):
"""simple docstring"""
if len(lowercase ) == 0:
return True
SCREAMING_SNAKE_CASE : List[Any] = input_str.replace(" " , "" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
SCREAMING_SNAKE_CASE : dict[str, int] = {}
for character in lower_case_input_str:
SCREAMING_SNAKE_CASE : List[Any] = character_freq_dict.get(lowercase , 0 ) + 1
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def lowerCamelCase__ ( lowercase = "" ):
"""simple docstring"""
print("\nFor string = " , lowercase , ":" )
print(
"> can_string_be_rearranged_as_palindrome_counter()" , "\tans =" , can_string_be_rearranged_as_palindrome_counter(lowercase ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome_counter(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
print(
"> can_string_be_rearranged_as_palindrome()" , "\tans =" , can_string_be_rearranged_as_palindrome(lowercase ) , "\ttime =" , timeit(
"z.can_string_be_rearranged_as_palindrome(z.check_str)" , setup="import __main__ as z" , ) , "seconds" , )
if __name__ == "__main__":
snake_case = input(
"""Enter string to determine if it can be rearranged as a palindrome or not: """
).strip()
benchmark(check_str)
snake_case = can_string_be_rearranged_as_palindrome_counter(check_str)
print(F"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")
| 319
|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case = ["""small""", """medium""", """large"""]
snake_case = """lm_head.decoder.weight"""
snake_case = """lm_head.weight"""
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = torch.load(lowercase )
SCREAMING_SNAKE_CASE : Any = d.pop(lowercase )
os.makedirs(lowercase , exist_ok=lowercase )
torch.save(lowercase , os.path.join(lowercase , lowercase ) )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
snake_case = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
snake_case = os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
snake_case = F"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 319
| 1
|
from copy import deepcopy
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : list[int] | None = None , UpperCAmelCase_ : int | None = None ):
if arr is None and size is not None:
SCREAMING_SNAKE_CASE : Any = size
SCREAMING_SNAKE_CASE : List[str] = [0] * size
elif arr is not None:
self.init(UpperCAmelCase_ )
else:
raise ValueError("Either arr or size must be specified" )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : list[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = deepcopy(UpperCAmelCase_ )
for i in range(1 , self.size ):
SCREAMING_SNAKE_CASE : List[Any] = self.next_(UpperCAmelCase_ )
if j < self.size:
self.tree[j] += self.tree[i]
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE : Optional[int] = self.next_(UpperCAmelCase_ )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def _A ( UpperCAmelCase_ : int ):
return index + (index & (-index))
@staticmethod
def _A ( UpperCAmelCase_ : int ):
return index - (index & (-index))
def _A ( self : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
SCREAMING_SNAKE_CASE : Any = self.next_(UpperCAmelCase_ )
def _A ( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
self.add(UpperCAmelCase_ , value - self.get(UpperCAmelCase_ ) )
def _A ( self : Dict , UpperCAmelCase_ : int ):
if right == 0:
return 0
SCREAMING_SNAKE_CASE : List[str] = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
SCREAMING_SNAKE_CASE : List[str] = self.prev(UpperCAmelCase_ )
return result
def _A ( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
return self.prefix(UpperCAmelCase_ ) - self.prefix(UpperCAmelCase_ )
def _A ( self : Optional[int] , UpperCAmelCase_ : int ):
return self.query(UpperCAmelCase_ , index + 1 )
def _A ( self : List[Any] , UpperCAmelCase_ : int ):
value -= self.tree[0]
if value < 0:
return -1
SCREAMING_SNAKE_CASE : int = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
SCREAMING_SNAKE_CASE : List[str] = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""MLukeTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 1
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
@staticmethod
@abstractmethod
def _A ( UpperCAmelCase_ : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
def _A ( self : List[str] ):
raise NotImplementedError()
| 319
|
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase__ ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 319
| 1
|
import re
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if len(re.findall("[ATCG]" , lowercase ) ) != len(lowercase ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319
|
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase_ : list ):
SCREAMING_SNAKE_CASE : Union[str, Any] = set_counts
SCREAMING_SNAKE_CASE : Any = max(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = [1] * num_sets
SCREAMING_SNAKE_CASE : List[str] = list(range(UpperCAmelCase_ ) )
def _A ( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE : List[Any] = self.get_parent(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self.get_parent(UpperCAmelCase_ )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
SCREAMING_SNAKE_CASE : List[str] = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Tuple = src_parent
SCREAMING_SNAKE_CASE : Optional[int] = self.set_counts[src_parent]
SCREAMING_SNAKE_CASE : Optional[Any] = max(self.max_set , UpperCAmelCase_ )
return True
def _A ( self : Tuple , UpperCAmelCase_ : int ):
if self.parents[disj_set] == disj_set:
return disj_set
SCREAMING_SNAKE_CASE : Tuple = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 319
| 1
|
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