code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_: int ={
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: Optional[Any] =[
'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'AdaptiveEmbedding',
'TransfoXLForSequenceClassification',
'TransfoXLLMHeadModel',
'TransfoXLModel',
'TransfoXLPreTrainedModel',
'load_tf_weights_in_transfo_xl',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: Union[str, Any] =[
'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAdaptiveEmbedding',
'TFTransfoXLForSequenceClassification',
'TFTransfoXLLMHeadModel',
'TFTransfoXLMainLayer',
'TFTransfoXLModel',
'TFTransfoXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_: Tuple =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 1 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCAmelCase ( lowerCAmelCase_ )-> Union[str, Any]:
return EnvironmentCommand()
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def lowercase_ ( __lowercase ) -> List[Any]:
lowerCAmelCase_ : List[str] = parser.add_parser('''env''' )
download_parser.set_defaults(func=__lowercase )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Optional[Any] = huggingface_hub.__version__
lowerCAmelCase_ : str = '''not installed'''
lowerCAmelCase_ : str = '''NA'''
if is_torch_available():
import torch
lowerCAmelCase_ : Any = torch.__version__
lowerCAmelCase_ : str = torch.cuda.is_available()
lowerCAmelCase_ : List[str] = '''not installed'''
if is_transformers_available():
import transformers
lowerCAmelCase_ : Any = transformers.__version__
lowerCAmelCase_ : Optional[Any] = '''not installed'''
if is_accelerate_available():
import accelerate
lowerCAmelCase_ : List[Any] = accelerate.__version__
lowerCAmelCase_ : List[str] = '''not installed'''
if is_xformers_available():
import xformers
lowerCAmelCase_ : Optional[Any] = xformers.__version__
lowerCAmelCase_ : int = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(__lowercase ) )
return info
@staticmethod
def lowercase_ ( __lowercase ) -> str:
return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n" | 262 | 0 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def _SCREAMING_SNAKE_CASE (A ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def _SCREAMING_SNAKE_CASE () -> List[Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def _SCREAMING_SNAKE_CASE () -> Optional[Any]:
"""simple docstring"""
lowercase__ = '''mock-s3-bucket'''
lowercase__ = f"s3://{mock_bucket}"
lowercase__ = extract_path_from_uri(A )
assert dataset_path.startswith('''s3://''' ) is False
lowercase__ = '''./local/path'''
lowercase__ = extract_path_from_uri(A )
assert dataset_path == new_dataset_path
def _SCREAMING_SNAKE_CASE (A ) -> Dict:
"""simple docstring"""
lowercase__ = is_remote_filesystem(A )
assert is_remote is True
lowercase__ = fsspec.filesystem('''file''' )
lowercase__ = is_remote_filesystem(A )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , A )
def _SCREAMING_SNAKE_CASE (A , A , A , A , A , A , A ) -> List[str]:
"""simple docstring"""
lowercase__ = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
lowercase__ = input_paths[compression_fs_class.protocol]
if input_path is None:
lowercase__ = f"for '{compression_fs_class.protocol}' compression protocol, "
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(A )
lowercase__ = fsspec.filesystem(compression_fs_class.protocol , fo=A )
assert isinstance(A , A )
lowercase__ = os.path.basename(A )
lowercase__ = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(A , '''r''' , encoding='''utf-8''' ) as f, open(A , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Optional[int]:
"""simple docstring"""
lowercase__ = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
lowercase__ = compressed_file_paths[protocol]
lowercase__ = '''dataset.jsonl'''
lowercase__ = f"{protocol}://{member_file_path}::{compressed_file_path}"
lowercase__ ,*lowercase__ = fsspec.get_fs_token_paths(A )
assert fs.isfile(A )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE (A , A , A , A ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = hf_api.dataset_info(A , token=A )
lowercase__ = HfFileSystem(repo_info=A , token=A )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(A ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def _SCREAMING_SNAKE_CASE () -> Optional[int]:
"""simple docstring"""
lowercase__ = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(A , A , clobber=A )
with pytest.warns(A ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(A ) == 1
assert (
str(warning_info[0].message )
== f"A filesystem protocol was already set for {protocol} and will be overwritten."
)
| 2 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = JukeboxTokenizer
SCREAMING_SNAKE_CASE__ : int = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
import torch
lowerCAmelCase_ : Union[str, Any] = JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
lowerCAmelCase_ : Any = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : List[str] = [
torch.tensor([[
0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
7_6, 7_6]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def lowercase_ ( self ) -> List[Any]:
import torch
lowerCAmelCase_ : Any = JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
lowerCAmelCase_ : str = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : Tuple = [
torch.tensor([[
0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 7_7, 7_7]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) ) | 262 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase : int = logging.get_logger(__name__)
lowercase : Any = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
lowercase : str = {
'tokenizer_file': {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
},
}
lowercase : Optional[Any] = {
'gpt-neox-20b': 20_48,
}
class A ( __snake_case ):
__magic_name__ = VOCAB_FILES_NAMES
__magic_name__ = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ = ['''input_ids''', '''attention_mask''']
def __init__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE="<|endoftext|>" , SCREAMING_SNAKE_CASE="<|endoftext|>" , SCREAMING_SNAKE_CASE="<|endoftext|>" , SCREAMING_SNAKE_CASE=False , **SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , tokenizer_file=SCREAMING_SNAKE_CASE , unk_token=SCREAMING_SNAKE_CASE , bos_token=SCREAMING_SNAKE_CASE , eos_token=SCREAMING_SNAKE_CASE , add_prefix_space=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
A : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE ) != add_prefix_space:
A : Optional[int] = getattr(SCREAMING_SNAKE_CASE , pre_tok_state.pop('''type''' ) )
A : Dict = add_prefix_space
A : Dict = pre_tok_class(**SCREAMING_SNAKE_CASE )
A : List[str] = add_prefix_space
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
A : Tuple = self._tokenizer.model.save(SCREAMING_SNAKE_CASE , name=SCREAMING_SNAKE_CASE )
return tuple(SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[int]:
"""simple docstring"""
A : Any = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE ) + [self.eos_token_id] )
if len(SCREAMING_SNAKE_CASE ) > self.model_max_length:
A : List[str] = input_ids[-self.model_max_length :]
return input_ids
| 3 |
from __future__ import annotations
import requests
def lowerCAmelCase ( lowerCAmelCase_ )-> dict:
lowerCAmelCase_ : List[Any] = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(lowerCAmelCase_ ).json()
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> list[dict]:
lowerCAmelCase_ : List[Any] = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
lowerCAmelCase_ : Tuple = requests.get(lowerCAmelCase_ ).json()[:max_stories]
return [get_hackernews_story(lowerCAmelCase_ ) for story_id in story_ids]
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> str:
lowerCAmelCase_ : Optional[Any] = hackernews_top_stories(lowerCAmelCase_ )
return "\n".join('''* [{title}]({url})'''.format(**lowerCAmelCase_ ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown()) | 262 | 0 |
'''simple docstring'''
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 UpperCAmelCase_ ( nn.Module ):
def __init__( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int=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 , ) -> List[Any]:
super().__init__()
lowerCAmelCase = only_cross_attention
lowerCAmelCase = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
lowerCAmelCase = (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:
lowerCAmelCase = AdaLayerNorm(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.use_ada_layer_norm_zero:
lowerCAmelCase = AdaLayerNormZero(UpperCAmelCase__ , UpperCAmelCase__ )
else:
lowerCAmelCase = nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ )
lowerCAmelCase = 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.
lowerCAmelCase = (
AdaLayerNorm(UpperCAmelCase__ , UpperCAmelCase__ )
if self.use_ada_layer_norm
else nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ )
)
lowerCAmelCase = 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:
lowerCAmelCase = None
lowerCAmelCase = None
# 3. Feed-forward
lowerCAmelCase = nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ )
lowerCAmelCase = FeedForward(UpperCAmelCase__ , dropout=UpperCAmelCase__ , activation_fn=UpperCAmelCase__ , final_dropout=UpperCAmelCase__ )
# let chunk size default to None
lowerCAmelCase = None
lowerCAmelCase = 0
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ) -> Optional[int]:
# Sets chunk feed-forward
lowerCAmelCase = chunk_size
lowerCAmelCase = dim
def __UpperCAmelCase ( self : Any , 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 , ) -> Any:
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
lowerCAmelCase = self.norma(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.use_ada_layer_norm_zero:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = self.norma(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , hidden_dtype=hidden_states.dtype )
else:
lowerCAmelCase = self.norma(UpperCAmelCase__ )
lowerCAmelCase = cross_attention_kwargs if cross_attention_kwargs is not None else {}
lowerCAmelCase = 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:
lowerCAmelCase = gate_msa.unsqueeze(1 ) * attn_output
lowerCAmelCase = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
lowerCAmelCase = (
self.norma(UpperCAmelCase__ , UpperCAmelCase__ ) if self.use_ada_layer_norm else self.norma(UpperCAmelCase__ )
)
lowerCAmelCase = self.attna(
UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , **UpperCAmelCase__ , )
lowerCAmelCase = attn_output + hidden_states
# 3. Feed-forward
lowerCAmelCase = self.norma(UpperCAmelCase__ )
if self.use_ada_layer_norm_zero:
lowerCAmelCase = 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`.''' )
lowerCAmelCase = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
lowerCAmelCase = torch.cat(
[self.ff(UpperCAmelCase__ ) for hid_slice in norm_hidden_states.chunk(UpperCAmelCase__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
lowerCAmelCase = self.ff(UpperCAmelCase__ )
if self.use_ada_layer_norm_zero:
lowerCAmelCase = gate_mlp.unsqueeze(1 ) * ff_output
lowerCAmelCase = ff_output + hidden_states
return hidden_states
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : int = 4 , UpperCAmelCase__ : float = 0.0 , UpperCAmelCase__ : str = "geglu" , UpperCAmelCase__ : bool = False , ) -> str:
super().__init__()
lowerCAmelCase = int(dim * mult )
lowerCAmelCase = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
lowerCAmelCase = GELU(UpperCAmelCase__ , UpperCAmelCase__ )
if activation_fn == "gelu-approximate":
lowerCAmelCase = GELU(UpperCAmelCase__ , UpperCAmelCase__ , approximate='tanh' )
elif activation_fn == "geglu":
lowerCAmelCase = GEGLU(UpperCAmelCase__ , UpperCAmelCase__ )
elif activation_fn == "geglu-approximate":
lowerCAmelCase = ApproximateGELU(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = 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 __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Tuple ) -> str:
for module in self.net:
lowerCAmelCase = module(UpperCAmelCase__ )
return hidden_states
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "none" ) -> List[Any]:
super().__init__()
lowerCAmelCase = nn.Linear(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = approximate
def __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : Optional[int] ) -> 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 __UpperCAmelCase ( self : Tuple , UpperCAmelCase__ : Tuple ) -> List[str]:
lowerCAmelCase = self.proj(UpperCAmelCase__ )
lowerCAmelCase = self.gelu(UpperCAmelCase__ )
return hidden_states
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
super().__init__()
lowerCAmelCase = nn.Linear(UpperCAmelCase__ , dim_out * 2 )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any] ) -> 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 __UpperCAmelCase ( self : List[Any] , UpperCAmelCase__ : List[Any] ) -> Optional[int]:
lowerCAmelCase , lowerCAmelCase = self.proj(UpperCAmelCase__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(UpperCAmelCase__ )
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Optional[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> Optional[Any]:
super().__init__()
lowerCAmelCase = nn.Linear(UpperCAmelCase__ , UpperCAmelCase__ )
def __UpperCAmelCase ( self : int , UpperCAmelCase__ : List[str] ) -> List[Any]:
lowerCAmelCase = self.proj(UpperCAmelCase__ )
return x * torch.sigmoid(1.702 * x )
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Union[str, Any] ) -> Optional[int]:
super().__init__()
lowerCAmelCase = nn.Embedding(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = nn.SiLU()
lowerCAmelCase = nn.Linear(UpperCAmelCase__ , embedding_dim * 2 )
lowerCAmelCase = nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ )
def __UpperCAmelCase ( self : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] ) -> Dict:
lowerCAmelCase = self.linear(self.silu(self.emb(UpperCAmelCase__ ) ) )
lowerCAmelCase , lowerCAmelCase = torch.chunk(UpperCAmelCase__ , 2 )
lowerCAmelCase = self.norm(UpperCAmelCase__ ) * (1 + scale) + shift
return x
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : int ) -> Dict:
super().__init__()
lowerCAmelCase = CombinedTimestepLabelEmbeddings(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = nn.SiLU()
lowerCAmelCase = nn.Linear(UpperCAmelCase__ , 6 * embedding_dim , bias=UpperCAmelCase__ )
lowerCAmelCase = nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ , eps=1E-6 )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int]=None ) -> List[Any]:
lowerCAmelCase = self.linear(self.silu(self.emb(UpperCAmelCase__ , UpperCAmelCase__ , hidden_dtype=UpperCAmelCase__ ) ) )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = emb.chunk(6 , dim=1 )
lowerCAmelCase = self.norm(UpperCAmelCase__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[str] = None , UpperCAmelCase__ : float = 1E-5 ) -> Optional[Any]:
super().__init__()
lowerCAmelCase = num_groups
lowerCAmelCase = eps
if act_fn is None:
lowerCAmelCase = None
else:
lowerCAmelCase = get_activation(UpperCAmelCase__ )
lowerCAmelCase = nn.Linear(UpperCAmelCase__ , out_dim * 2 )
def __UpperCAmelCase ( self : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Any ) -> Tuple:
if self.act:
lowerCAmelCase = self.act(UpperCAmelCase__ )
lowerCAmelCase = self.linear(UpperCAmelCase__ )
lowerCAmelCase = emb[:, :, None, None]
lowerCAmelCase , lowerCAmelCase = emb.chunk(2 , dim=1 )
lowerCAmelCase = F.group_norm(UpperCAmelCase__ , self.num_groups , eps=self.eps )
lowerCAmelCase = x * (1 + scale) + shift
return x
| 4 |
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
_UpperCAmelCase : List[str] =get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_UpperCAmelCase : Optional[int] =25_0004
_UpperCAmelCase : Tuple =25_0020
@require_sentencepiece
@require_tokenizers
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = MBartTokenizer
SCREAMING_SNAKE_CASE__ : Dict = MBartTokenizerFast
SCREAMING_SNAKE_CASE__ : Tuple = True
SCREAMING_SNAKE_CASE__ : List[str] = True
def lowercase_ ( self ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase_ : str = MBartTokenizer(__lowercase , keep_accents=__lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[int] = MBartTokenizer(__lowercase , keep_accents=__lowercase )
lowerCAmelCase_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowerCAmelCase_ : Optional[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [
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''',
'''.''',
] , )
lowerCAmelCase_ : Dict = tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(
__lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCAmelCase_ : Union[str, Any] = tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [
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 lowercase_ ( 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
lowerCAmelCase_ : int = (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})""" ):
lowerCAmelCase_ : Optional[int] = self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : int = self.tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = tempfile.mkdtemp()
lowerCAmelCase_ : Union[str, Any] = tokenizer_r.save_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.save_pretrained(__lowercase )
# 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 ) )
lowerCAmelCase_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Tuple = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=True
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : int = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Tuple = tokenizer_p.save_pretrained(__lowercase )
# Checks it save with the same files
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Optional[int] = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=False
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Optional[int] = tokenizer_p.save_pretrained(__lowercase )
# 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
lowerCAmelCase_ : Dict = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = """facebook/mbart-large-en-ro"""
SCREAMING_SNAKE_CASE__ : int = [
""" 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.""",
]
SCREAMING_SNAKE_CASE__ : Optional[int] = [
"""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.""",
]
SCREAMING_SNAKE_CASE__ : str = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def lowercase_ ( cls ) -> Optional[int]:
lowerCAmelCase_ : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
lowerCAmelCase_ : Optional[Any] = 1
return cls
def lowercase_ ( self ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 2_5_0_0_2_0 )
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
def lowercase_ ( self ) -> Any:
self.assertIn(__lowercase , self.tokenizer.all_special_ids )
lowerCAmelCase_ : Union[str, Any] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
lowerCAmelCase_ : Tuple = self.tokenizer.decode(__lowercase , skip_special_tokens=__lowercase )
lowerCAmelCase_ : List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertNotIn(self.tokenizer.eos_token , __lowercase )
def lowercase_ ( self ) -> Any:
lowerCAmelCase_ : Union[str, Any] = ['''this is gunna be a long sentence ''' * 2_0]
assert isinstance(src_text[0] , __lowercase )
lowerCAmelCase_ : str = 1_0
lowerCAmelCase_ : Tuple = self.tokenizer(__lowercase , max_length=__lowercase , truncation=__lowercase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __lowercase )
self.assertEqual(len(__lowercase ) , __lowercase )
def lowercase_ ( self ) -> int:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = tempfile.mkdtemp()
lowerCAmelCase_ : int = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = MBartTokenizer.from_pretrained(__lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __lowercase )
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Tuple = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowercase , return_tensors='''pt''' )
lowerCAmelCase_ : Tuple = 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 lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
lowerCAmelCase_ : int = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
lowerCAmelCase_ : str = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
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 lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = self.tokenizer(self.src_text , padding=__lowercase , truncation=__lowercase , max_length=3 , return_tensors='''pt''' )
lowerCAmelCase_ : Any = self.tokenizer(
text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=1_0 , return_tensors='''pt''' )
lowerCAmelCase_ : int = targets['''input_ids''']
lowerCAmelCase_ : Optional[Any] = shift_tokens_right(__lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(__lowercase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 2_5_0_0_0_1,
} , ) | 262 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def UpperCAmelCase_ ( __snake_case ) -> typing.Counter[int]:
"""simple docstring"""
_lowercase =Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(__snake_case , max_perimeter + 1 ):
_lowercase =(base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(__snake_case ):
_lowercase =int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def UpperCAmelCase_ ( __snake_case = 1000 ) -> int:
"""simple docstring"""
_lowercase =pythagorean_triple(__snake_case )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f'''Perimeter {solution()} has maximum solutions''')
| 5 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = "cpu" , lowerCAmelCase_ = None )-> None:
lowerCAmelCase_ : str = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase_ , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowerCAmelCase_ : int = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Tuple = src_path
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
fire.Fire(convert) | 262 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a , __a = FlaxControlNetModel.from_pretrained(
'''lllyasviel/sd-controlnet-canny''' , from_pt=_snake_case , dtype=jnp.bfloataa )
__a , __a = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , controlnet=_snake_case , from_pt=_snake_case , dtype=jnp.bfloataa )
__a = controlnet_params
__a = '''bird'''
__a = jax.device_count()
__a = pipe.prepare_text_inputs([prompts] * num_samples )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''' )
__a = pipe.prepare_image_inputs([canny_image] * num_samples )
__a = jax.random.PRNGKey(0 )
__a = jax.random.split(_snake_case , jax.device_count() )
__a = replicate(_snake_case )
__a = shard(_snake_case )
__a = shard(_snake_case )
__a = pipe(
prompt_ids=_snake_case , image=_snake_case , params=_snake_case , prng_seed=_snake_case , num_inference_steps=50 , jit=_snake_case , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
__a = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__a = images[0, 253:256, 253:256, -1]
__a = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__a = jnp.array(
[0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078] )
print(F"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a , __a = FlaxControlNetModel.from_pretrained(
'''lllyasviel/sd-controlnet-openpose''' , from_pt=_snake_case , dtype=jnp.bfloataa )
__a , __a = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , controlnet=_snake_case , from_pt=_snake_case , dtype=jnp.bfloataa )
__a = controlnet_params
__a = '''Chef in the kitchen'''
__a = jax.device_count()
__a = pipe.prepare_text_inputs([prompts] * num_samples )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png''' )
__a = pipe.prepare_image_inputs([pose_image] * num_samples )
__a = jax.random.PRNGKey(0 )
__a = jax.random.split(_snake_case , jax.device_count() )
__a = replicate(_snake_case )
__a = shard(_snake_case )
__a = shard(_snake_case )
__a = pipe(
prompt_ids=_snake_case , image=_snake_case , params=_snake_case , prng_seed=_snake_case , num_inference_steps=50 , jit=_snake_case , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
__a = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__a = images[0, 253:256, 253:256, -1]
__a = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__a = jnp.array(
[[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]] )
print(F"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2 | 6 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel | 262 | 0 |
from math import factorial, pi
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or accuracy <= 0:
raise ValueError('maclaurin_sin() requires a positive int for accuracy' )
A__ = float(SCREAMING_SNAKE_CASE__ )
A__ = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(SCREAMING_SNAKE_CASE__ ) )
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise ValueError('maclaurin_cos() requires either an int or float for theta' )
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or accuracy <= 0:
raise ValueError('maclaurin_cos() requires a positive int for accuracy' )
A__ = float(SCREAMING_SNAKE_CASE__ )
A__ = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(SCREAMING_SNAKE_CASE__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15))
| 7 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : str ={
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = """vit_mae"""
def __init__( self , __lowercase=7_6_8 , __lowercase=1_2 , __lowercase=1_2 , __lowercase=3_0_7_2 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1e-12 , __lowercase=2_2_4 , __lowercase=1_6 , __lowercase=3 , __lowercase=True , __lowercase=1_6 , __lowercase=5_1_2 , __lowercase=8 , __lowercase=2_0_4_8 , __lowercase=0.75 , __lowercase=False , **__lowercase , ) -> str:
super().__init__(**__lowercase )
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Any = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : int = intermediate_size
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : int = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : Union[str, Any] = image_size
lowerCAmelCase_ : Optional[int] = patch_size
lowerCAmelCase_ : Tuple = num_channels
lowerCAmelCase_ : List[str] = qkv_bias
lowerCAmelCase_ : List[Any] = decoder_num_attention_heads
lowerCAmelCase_ : int = decoder_hidden_size
lowerCAmelCase_ : Optional[int] = decoder_num_hidden_layers
lowerCAmelCase_ : Tuple = decoder_intermediate_size
lowerCAmelCase_ : Tuple = mask_ratio
lowerCAmelCase_ : Any = norm_pix_loss | 262 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
lowerCAmelCase_ = get_tests_dir('''fixtures''')
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Union[str, Any] ) ->Dict:
# A mock response for an HTTP head request to emulate server down
snake_case_ = mock.Mock()
snake_case_ = 5_0_0
snake_case_ = {}
snake_case_ = HTTPError
snake_case_ = {}
# Download this model to make sure it's in the cache.
snake_case_ = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=_UpperCamelCase ) as mock_head:
snake_case_ = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def snake_case__( self : List[str] ) ->Optional[int]:
# This test is for deprecated behavior and can be removed in v5
snake_case_ = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def snake_case__( self : Any ) ->List[str]:
with self.assertRaises(_UpperCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
snake_case_ = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
snake_case_ = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(_UpperCamelCase )
@is_staging_test
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def snake_case__( cls : List[Any] ) ->Dict:
snake_case_ = TOKEN
HfFolder.save_token(_UpperCamelCase )
@classmethod
def snake_case__( cls : List[Any] ) ->Optional[int]:
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def snake_case__( self : Any ) ->List[Any]:
snake_case_ = ViTImageProcessor.from_pretrained(_UpperCamelCase )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
snake_case_ = ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_UpperCamelCase , repo_id='''test-image-processor''' , push_to_hub=_UpperCamelCase , use_auth_token=self._token )
snake_case_ = ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
def snake_case__( self : str ) ->List[str]:
snake_case_ = ViTImageProcessor.from_pretrained(_UpperCamelCase )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
snake_case_ = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_UpperCamelCase , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=_UpperCamelCase , use_auth_token=self._token )
snake_case_ = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
def snake_case__( self : List[str] ) ->Tuple:
CustomImageProcessor.register_for_auto_class()
snake_case_ = CustomImageProcessor.from_pretrained(_UpperCamelCase )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
snake_case_ = AutoImageProcessor.from_pretrained(
f'''{USER}/test-dynamic-image-processor''' , trust_remote_code=_UpperCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' ) | 8 |
def lowerCAmelCase ( lowerCAmelCase_ = 10**9 )-> int:
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[int] = 2
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : str = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
lowerCAmelCase_ : Any = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""") | 262 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def _UpperCamelCase ( lowercase__ ):
if num <= 0:
raise ValueError('''math domain error''' )
return quad(lowercase__ , 0 , lowercase__ , args=(lowercase__) )[0]
def _UpperCamelCase ( lowercase__ , lowercase__ ):
return math.pow(lowercase__ , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 9 |
import inspect
import unittest
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def lowercase_ ( self ) -> List[str]:
import diffusers
from diffusers.dependency_versions_table import deps
lowerCAmelCase_ : Any = inspect.getmembers(__lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowerCAmelCase_ : Optional[int] = '''k-diffusion'''
elif backend == "invisible_watermark":
lowerCAmelCase_ : Dict = '''invisible-watermark'''
assert backend in deps, f"""{backend} is not in the deps table!""" | 262 | 0 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__A = "."
if __name__ == "__main__":
__A = os.path.join(REPO_PATH, "utils/documentation_tests.txt")
__A = []
__A = []
with open(doctest_file_path) as fp:
for line in fp:
__A = line.strip()
__A = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
__A = "\n".join(non_existent_paths)
raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
if all_paths != sorted(all_paths):
raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")
| 10 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_UpperCAmelCase : Any =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__lowercase , **__lowercase ) -> None:
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __lowercase , )
super().__init__(*__lowercase , **__lowercase ) | 262 | 0 |
from __future__ import annotations
def _UpperCAmelCase (UpperCamelCase__ : list ):
if not nums:
raise ValueError("List is empty" )
return sum(UpperCamelCase__ ) / len(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : Optional[int] = set()
# edges = list of graph's edges
lowerCAmelCase_ : List[str] = get_edges(lowerCAmelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = edges.pop()
chosen_vertices.add(lowerCAmelCase_ )
chosen_vertices.add(lowerCAmelCase_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(lowerCAmelCase_ )
return chosen_vertices
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}") | 262 | 0 |
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__ ( A__ : Dict ):
'''simple docstring'''
__lowerCamelCase = tmp_path / """file.csv"""
__lowerCamelCase = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(A__ , """w""" ) as f:
f.write(A__ )
return str(A__ )
@pytest.fixture
def lowerCamelCase__ ( A__ : str ):
'''simple docstring'''
__lowerCamelCase = tmp_path / """malformed_file.csv"""
__lowerCamelCase = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(A__ , """w""" ) as f:
f.write(A__ )
return str(A__ )
@pytest.fixture
def lowerCamelCase__ ( A__ : List[str] , A__ : List[str] ):
'''simple docstring'''
__lowerCamelCase = tmp_path / """csv_with_image.csv"""
__lowerCamelCase = textwrap.dedent(
f'\\n image\n {image_file}\n ' )
with open(A__ , """w""" ) as f:
f.write(A__ )
return str(A__ )
@pytest.fixture
def lowerCamelCase__ ( A__ : List[str] ):
'''simple docstring'''
__lowerCamelCase = tmp_path / """csv_with_label.csv"""
__lowerCamelCase = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(A__ , """w""" ) as f:
f.write(A__ )
return str(A__ )
@pytest.fixture
def lowerCamelCase__ ( A__ : Any ):
'''simple docstring'''
__lowerCamelCase = tmp_path / """csv_with_int_list.csv"""
__lowerCamelCase = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(A__ , """w""" ) as f:
f.write(A__ )
return str(A__ )
def lowerCamelCase__ ( A__ : Any , A__ : str , A__ : Any ):
'''simple docstring'''
__lowerCamelCase = Csv()
__lowerCamelCase = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(A__ , 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(A__ ) in record.message
for record in caplog.records )
@require_pil
def lowerCamelCase__ ( A__ : int ):
'''simple docstring'''
with open(A__ , encoding="""utf-8""" ) as f:
__lowerCamelCase = f.read().splitlines()[1]
__lowerCamelCase = Csv(encoding="""utf-8""" , features=Features({"""image""": Image()} ) )
__lowerCamelCase = csv._generate_tables([[csv_file_with_image]] )
__lowerCamelCase = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
__lowerCamelCase = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def lowerCamelCase__ ( A__ : str ):
'''simple docstring'''
with open(A__ , encoding="""utf-8""" ) as f:
__lowerCamelCase = f.read().splitlines()[1:]
__lowerCamelCase = Csv(encoding="""utf-8""" , features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
__lowerCamelCase = csv._generate_tables([[csv_file_with_label]] )
__lowerCamelCase = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
__lowerCamelCase = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(A__ ) for label in labels]
def lowerCamelCase__ ( A__ : Any ):
'''simple docstring'''
__lowerCamelCase = Csv(encoding="""utf-8""" , sep=""",""" , converters={"""int_list""": lambda A__ : [int(A__ ) for i in x.split()]} )
__lowerCamelCase = csv._generate_tables([[csv_file_with_int_list]] )
__lowerCamelCase = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
__lowerCamelCase = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 12 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
lowerCAmelCase_ : Optional[int] = False
for divisor in range(2 , int(round(sqrt(lowerCAmelCase_ ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
lowerCAmelCase_ : Tuple = False
break
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'status' must been from type bool"
return status
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
lowerCAmelCase_ : Tuple = list(range(2 , n + 1 ) )
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(lowerCAmelCase_ ) ):
for j in range(i + 1 , len(lowerCAmelCase_ ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
lowerCAmelCase_ : str = 0
# filters actual prime numbers.
lowerCAmelCase_ : Optional[int] = [x for x in begin_list if x != 0]
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
lowerCAmelCase_ : List[Any] = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(lowerCAmelCase_ ):
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and number >= 0, "'number' must been an int and >= 0"
lowerCAmelCase_ : int = [] # this list will be returns of the function.
# potential prime number factors.
lowerCAmelCase_ : List[Any] = 2
lowerCAmelCase_ : Optional[int] = number
if number == 0 or number == 1:
ans.append(lowerCAmelCase_ )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(lowerCAmelCase_ ):
while quotient != 1:
if is_prime(lowerCAmelCase_ ) and (quotient % factor == 0):
ans.append(lowerCAmelCase_ )
quotient /= factor
else:
factor += 1
else:
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : Dict = 0
# prime factorization of 'number'
lowerCAmelCase_ : Any = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = max(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : List[Any] = 0
# prime factorization of 'number'
lowerCAmelCase_ : Dict = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = min(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 == 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 == 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 != 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 != 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[str]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (number > 2) and is_even(lowerCAmelCase_ )
), "'number' must been an int, even and > 2"
lowerCAmelCase_ : str = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
lowerCAmelCase_ : int = get_prime_numbers(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = len(lowerCAmelCase_ )
# run variable for while-loops.
lowerCAmelCase_ : Union[str, Any] = 0
lowerCAmelCase_ : Tuple = None
# exit variable. for break up the loops
lowerCAmelCase_ : int = True
while i < len_pn and loop:
lowerCAmelCase_ : int = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
lowerCAmelCase_ : Tuple = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (len(lowerCAmelCase_ ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : int = 0
while numbera != 0:
lowerCAmelCase_ : str = numbera % numbera
lowerCAmelCase_ : List[Any] = numbera
lowerCAmelCase_ : Any = rest
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : List[Any] = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
elif numbera == 1 or numbera == 1:
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : Union[str, Any] = max(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Dict = 0
lowerCAmelCase_ : Union[str, Any] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(max(lowerCAmelCase_ , lowerCAmelCase_ ) ):
ans *= n
else:
lowerCAmelCase_ : List[str] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'number' must been a positive int"
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : Optional[int] = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(lowerCAmelCase_ ):
ans += 1
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and is_prime(
lowerCAmelCase_ ), "'ans' must been a prime number and from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
assert (
is_prime(lowerCAmelCase_ ) and is_prime(lowerCAmelCase_ ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
lowerCAmelCase_ : Union[str, Any] = p_number_a + 1 # jump to the next number
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
while number < p_number_a:
ans.append(lowerCAmelCase_ )
number += 1
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and ans[0] != p_number_a
and ans[len(lowerCAmelCase_ ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 1), "'n' must been int and >= 1"
lowerCAmelCase_ : List[Any] = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(lowerCAmelCase_ )
# precondition
assert ans[0] == 1 and ans[len(lowerCAmelCase_ ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number > 1
), "'number' must been an int and >= 1"
lowerCAmelCase_ : Union[str, Any] = get_divisors(lowerCAmelCase_ )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (divisors[0] == 1)
and (divisors[len(lowerCAmelCase_ ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
lowerCAmelCase_ : Optional[Any] = gcd(abs(lowerCAmelCase_ ) , abs(lowerCAmelCase_ ) )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been a int and >= 0"
lowerCAmelCase_ : Any = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been an int and >= 0"
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Union[str, Any] = 1 # this will be return
for _ in range(n - 1 ):
lowerCAmelCase_ : Union[str, Any] = ans
ans += fiba
lowerCAmelCase_ : Optional[Any] = tmp
return ans | 262 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase : str = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
"""SpeechT5Config""",
"""SpeechT5HifiGanConfig""",
],
"""feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
"""processing_speecht5""": ["""SpeechT5Processor"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Optional[int] = ["""SpeechT5Tokenizer"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : int = [
"""SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SpeechT5ForSpeechToText""",
"""SpeechT5ForSpeechToSpeech""",
"""SpeechT5ForTextToSpeech""",
"""SpeechT5Model""",
"""SpeechT5PreTrainedModel""",
"""SpeechT5HifiGan""",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
lowerCAmelCase : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 13 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
_UpperCAmelCase : Union[str, Any] ="""pt"""
elif is_tf_available():
_UpperCAmelCase : List[Any] ="""tf"""
else:
_UpperCAmelCase : Optional[int] ="""jax"""
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = PerceiverTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def lowercase_ ( self ) -> Optional[int]:
super().setUp()
lowerCAmelCase_ : str = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase_ ( self ) -> Any:
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def lowercase_ ( self , **__lowercase ) -> PerceiverTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowercase )
def lowercase_ ( self , __lowercase , __lowercase=False , __lowercase=2_0 , __lowercase=5 ) -> Tuple[str, list]:
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for Perceiver 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.
lowerCAmelCase_ : Optional[Any] = []
for i in range(len(__lowercase ) ):
try:
lowerCAmelCase_ : List[str] = tokenizer.decode([i] , clean_up_tokenization_spaces=__lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCAmelCase_ : List[str] = list(filter(lambda __lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , __lowercase ) )
lowerCAmelCase_ : Optional[int] = list(filter(lambda __lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=__lowercase ) , __lowercase ) )
if max_length is not None and len(__lowercase ) > max_length:
lowerCAmelCase_ : Union[str, Any] = toks[:max_length]
if min_length is not None and len(__lowercase ) < min_length and len(__lowercase ) > 0:
while len(__lowercase ) < min_length:
lowerCAmelCase_ : Union[str, Any] = toks + toks
# toks_str = [t[1] for t in toks]
lowerCAmelCase_ : List[str] = [t[0] for t in toks]
# Ensure consistency
lowerCAmelCase_ : int = tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
if " " not in output_txt and len(__lowercase ) > 1:
lowerCAmelCase_ : Optional[int] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=__lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=__lowercase )
)
if with_prefix_space:
lowerCAmelCase_ : Any = ''' ''' + output_txt
lowerCAmelCase_ : List[str] = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
return output_txt, output_ids
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : List[str] = self.perceiver_tokenizer
lowerCAmelCase_ : Any = '''Unicode €.'''
lowerCAmelCase_ : Dict = tokenizer(__lowercase )
lowerCAmelCase_ : Any = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['''input_ids'''] , __lowercase )
# decoding
lowerCAmelCase_ : str = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , '''[CLS]Unicode €.[SEP]''' )
lowerCAmelCase_ : Optional[int] = tokenizer('''e è é ê ë''' )
lowerCAmelCase_ : str = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['''input_ids'''] , __lowercase )
# decoding
lowerCAmelCase_ : int = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.perceiver_tokenizer
lowerCAmelCase_ : Dict = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
lowerCAmelCase_ : Optional[int] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCAmelCase_ : Optional[int] = tokenizer(__lowercase , padding=__lowercase , return_tensors=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
if FRAMEWORK != "jax":
lowerCAmelCase_ : str = list(batch.input_ids.numpy()[0] )
else:
lowerCAmelCase_ : Union[str, Any] = list(batch.input_ids.tolist()[0] )
self.assertListEqual(__lowercase , __lowercase )
self.assertEqual((2, 3_8) , batch.input_ids.shape )
self.assertEqual((2, 3_8) , batch.attention_mask.shape )
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : int = self.perceiver_tokenizer
lowerCAmelCase_ : Optional[Any] = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowerCAmelCase_ : List[Any] = tokenizer(__lowercase , padding=__lowercase , return_tensors=__lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , __lowercase )
self.assertIn('''attention_mask''' , __lowercase )
self.assertNotIn('''decoder_input_ids''' , __lowercase )
self.assertNotIn('''decoder_attention_mask''' , __lowercase )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = self.perceiver_tokenizer
lowerCAmelCase_ : int = [
'''Summary of the text.''',
'''Another summary.''',
]
lowerCAmelCase_ : List[str] = tokenizer(
text_target=__lowercase , max_length=3_2 , padding='''max_length''' , truncation=__lowercase , return_tensors=__lowercase )
self.assertEqual(3_2 , targets['''input_ids'''].shape[1] )
def lowercase_ ( self ) -> Optional[Any]:
# safety check on max_len default value so we are sure the test works
lowerCAmelCase_ : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
lowerCAmelCase_ : Optional[int] = 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
lowerCAmelCase_ : Union[str, Any] = tempfile.mkdtemp()
lowerCAmelCase_ : str = ''' He is very happy, UNwant\u00E9d,running'''
lowerCAmelCase_ : Optional[int] = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Any = tokenizer.__class__.from_pretrained(__lowercase )
lowerCAmelCase_ : Tuple = after_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
shutil.rmtree(__lowercase )
lowerCAmelCase_ : Optional[int] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCAmelCase_ : Optional[int] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
lowerCAmelCase_ : Any = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
lowerCAmelCase_ : str = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : str = tokenizer.__class__.from_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = after_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
lowerCAmelCase_ : str = tokenizer.__class__.from_pretrained(__lowercase , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(__lowercase )
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : List[Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__lowercase )
with open(os.path.join(__lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
lowerCAmelCase_ : Tuple = json.load(__lowercase )
with open(os.path.join(__lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
lowerCAmelCase_ : Any = json.load(__lowercase )
lowerCAmelCase_ : Optional[int] = [f"""<extra_id_{i}>""" for i in range(1_2_5 )]
lowerCAmelCase_ : Optional[Any] = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
lowerCAmelCase_ : Any = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(__lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(__lowercase , __lowercase )
with open(os.path.join(__lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(__lowercase , __lowercase )
# 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
lowerCAmelCase_ : int = tokenizer_class.from_pretrained(
__lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
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
lowerCAmelCase_ : Tuple = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=__lowercase )]
lowerCAmelCase_ : Dict = tokenizer_class.from_pretrained(
__lowercase , additional_special_tokens=__lowercase , )
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 lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ) , '''�''' )
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> Any:
pass
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> List[str]:
pass
def lowercase_ ( self ) -> Dict:
# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
# strings and special added tokens as tokens
lowerCAmelCase_ : Tuple = self.get_tokenizers(fast=__lowercase , do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
lowerCAmelCase_ : List[str] = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
lowerCAmelCase_ : Optional[int] = tokenizer.convert_tokens_to_string(__lowercase )
self.assertIsInstance(__lowercase , __lowercase ) | 262 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : Dict = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase_ ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = SpeechTaTokenizer
UpperCAmelCase__ = False
UpperCAmelCase__ = True
def SCREAMING_SNAKE_CASE ( self : Any) ->Tuple:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
A__ = SpeechTaTokenizer(UpperCAmelCase__)
A__ = AddedToken('''<mask>''' , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__)
A__ = mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token})
tokenizer.add_tokens(['''<ctc_blank>'''])
tokenizer.save_pretrained(self.tmpdirname)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , UpperCAmelCase__ : List[Any]) ->List[Any]:
'''simple docstring'''
A__ = '''this is a test'''
A__ = '''this is a test'''
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : Any=20 , UpperCAmelCase__ : int=5) ->Any:
'''simple docstring'''
A__ , A__ = self.get_input_output_texts(UpperCAmelCase__)
A__ = tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__)
A__ = tokenizer.decode(UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__)
return text, ids
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->int:
'''simple docstring'''
A__ = '''<pad>'''
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__) , UpperCAmelCase__)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__) , UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[str]:
'''simple docstring'''
A__ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , '''<s>''')
self.assertEqual(vocab_keys[1] , '''<pad>''')
self.assertEqual(vocab_keys[-4] , '''œ''')
self.assertEqual(vocab_keys[-2] , '''<mask>''')
self.assertEqual(vocab_keys[-1] , '''<ctc_blank>''')
self.assertEqual(len(UpperCAmelCase__) , 81)
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->List[str]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 79)
def SCREAMING_SNAKE_CASE ( self : str) ->Union[str, Any]:
'''simple docstring'''
A__ = self.get_tokenizers(do_lower_case=UpperCAmelCase__)
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}"""):
A__ = tokenizer.vocab_size
A__ = len(UpperCAmelCase__)
self.assertNotEqual(UpperCAmelCase__ , 0)
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
A__ = tokenizer.add_tokens(UpperCAmelCase__)
A__ = tokenizer.vocab_size
A__ = len(UpperCAmelCase__)
self.assertNotEqual(UpperCAmelCase__ , 0)
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__)
self.assertEqual(UpperCAmelCase__ , len(UpperCAmelCase__))
self.assertEqual(UpperCAmelCase__ , all_size + len(UpperCAmelCase__))
A__ = tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=UpperCAmelCase__)
self.assertGreaterEqual(len(UpperCAmelCase__) , 4)
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1)
A__ = {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
A__ = tokenizer.add_special_tokens(UpperCAmelCase__)
A__ = tokenizer.vocab_size
A__ = len(UpperCAmelCase__)
self.assertNotEqual(UpperCAmelCase__ , 0)
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__)
self.assertEqual(UpperCAmelCase__ , len(UpperCAmelCase__))
self.assertEqual(UpperCAmelCase__ , all_size_a + len(UpperCAmelCase__))
A__ = tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=UpperCAmelCase__)
self.assertGreaterEqual(len(UpperCAmelCase__) , 6)
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[0] , tokens[1])
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3] , tokens[-4])
self.assertEqual(tokens[0] , tokenizer.eos_token_id)
self.assertEqual(tokens[-3] , tokenizer.pad_token_id)
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->List[Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[Any]:
'''simple docstring'''
A__ = self.get_tokenizer()
A__ = tokenizer.tokenize('''This is a test''')
# fmt: off
self.assertListEqual(UpperCAmelCase__ , [SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''])
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
A__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
self.assertListEqual(
UpperCAmelCase__ , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''])
A__ = tokenizer.convert_tokens_to_ids(UpperCAmelCase__)
# fmt: off
self.assertListEqual(UpperCAmelCase__ , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26])
# fmt: on
A__ = tokenizer.convert_ids_to_tokens(UpperCAmelCase__)
self.assertListEqual(
UpperCAmelCase__ , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''])
@slow
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->List[Any]:
'''simple docstring'''
A__ = [
'''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
'''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
'''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
'''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
'''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
'''conditioning on both left and right context in all layers.''',
'''The quick brown fox jumps over the lazy dog.''',
]
# fmt: off
A__ = {
'''input_ids''': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'''attention_mask''': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='''microsoft/speecht5_asr''' , revision='''c5ef64c71905caeccde0e4462ef3f9077224c524''' , sequences=UpperCAmelCase__ , )
| 14 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class snake_case__:
'''simple docstring'''
@staticmethod
def lowercase_ ( *__lowercase , **__lowercase ) -> Union[str, Any]:
pass
@is_pipeline_test
@require_vision
@require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase_ ( self , __lowercase , __lowercase , __lowercase ) -> List[str]:
lowerCAmelCase_ : Union[str, Any] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCAmelCase_ : str = [
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase_ ( self , __lowercase , __lowercase ) -> str:
lowerCAmelCase_ : Tuple = object_detector(examples[0] , threshold=0.0 )
lowerCAmelCase_ : Dict = len(__lowercase )
self.assertGreater(__lowercase , 0 )
self.assertEqual(
__lowercase , [
{
'''score''': ANY(__lowercase ),
'''label''': ANY(__lowercase ),
'''box''': {'''xmin''': ANY(__lowercase ), '''ymin''': ANY(__lowercase ), '''xmax''': ANY(__lowercase ), '''ymax''': ANY(__lowercase )},
}
for i in range(__lowercase )
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase_ ( self ) -> List[str]:
pass
@require_torch
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Union[str, Any] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCAmelCase_ : Union[str, Any] = object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
] , )
lowerCAmelCase_ : Union[str, Any] = object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
[
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
]
] , )
@require_torch
@slow
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Any = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Dict = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
] , )
lowerCAmelCase_ : Tuple = object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
] , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase_ ( self ) -> List[str]:
pass
@require_torch
@slow
def lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Any = 0.2
lowerCAmelCase_ : List[Any] = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Optional[Any] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=__lowercase , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
] , )
@require_torch
@slow
def lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Dict = 2
lowerCAmelCase_ : Union[str, Any] = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Optional[Any] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , top_k=__lowercase , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
] , ) | 262 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :str = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "sew-d"
def __init__( self : List[str] ,A : Dict=32 ,A : Optional[int]=7_68 ,A : Dict=12 ,A : Union[str, Any]=12 ,A : Any=30_72 ,A : List[str]=2 ,A : List[str]=5_12 ,A : str=2_56 ,A : int=True ,A : Any=True ,A : Tuple=("p2c", "c2p") ,A : str="layer_norm" ,A : List[str]="gelu_python" ,A : Union[str, Any]=0.1 ,A : Optional[Any]=0.1 ,A : Dict=0.1 ,A : Optional[int]=0.0 ,A : List[str]=0.1 ,A : int=0.02 ,A : Any=1E-7 ,A : List[Any]=1E-5 ,A : str="group" ,A : int="gelu" ,A : List[str]=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) ,A : Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,A : Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,A : Union[str, Any]=False ,A : Dict=1_28 ,A : Dict=16 ,A : Optional[Any]=True ,A : List[str]=0.05 ,A : Dict=10 ,A : Tuple=2 ,A : List[Any]=0.0 ,A : List[str]=10 ,A : List[Any]=0 ,A : Dict="mean" ,A : str=False ,A : int=False ,A : Optional[int]=2_56 ,A : Optional[int]=0 ,A : List[Any]=1 ,A : Tuple=2 ,**A : Dict ,):
super().__init__(**A ,pad_token_id=A ,bos_token_id=A ,eos_token_id=A )
__A = hidden_size
__A = feat_extract_norm
__A = feat_extract_activation
__A = list(A )
__A = list(A )
__A = list(A )
__A = conv_bias
__A = num_conv_pos_embeddings
__A = num_conv_pos_embedding_groups
__A = len(self.conv_dim )
__A = num_hidden_layers
__A = intermediate_size
__A = squeeze_factor
__A = max_position_embeddings
__A = position_buckets
__A = share_att_key
__A = relative_attention
__A = norm_rel_ebd
__A = list(A )
__A = hidden_act
__A = num_attention_heads
__A = hidden_dropout
__A = attention_dropout
__A = activation_dropout
__A = feat_proj_dropout
__A = final_dropout
__A = layer_norm_eps
__A = feature_layer_norm_eps
__A = initializer_range
__A = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect."
"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
f'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
f'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__A = apply_spec_augment
__A = mask_time_prob
__A = mask_time_length
__A = mask_time_min_masks
__A = mask_feature_prob
__A = mask_feature_length
__A = mask_feature_min_masks
# ctc loss
__A = ctc_loss_reduction
__A = ctc_zero_infinity
# sequence classification
__A = use_weighted_layer_sum
__A = classifier_proj_size
@property
def UpperCamelCase_ ( self : List[str] ):
return functools.reduce(operator.mul ,self.conv_stride ,1 )
| 15 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels | 262 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
]
)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
lowercase__ : Optional[Any] = state_dict.pop(__lowerCamelCase )
lowercase__ : Any = val
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[Any]:
lowercase__ : Optional[Any] = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase__ : Any = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
lowercase__ : str = value
else:
lowercase__ : Optional[Any] = value
return new_state_dict
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
lowercase__ : Any = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase__ : int = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
lowercase__ : List[str] = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase__ : Tuple = in_proj_weight[:2_56, :]
lowercase__ : Union[str, Any] = in_proj_bias[:2_56]
lowercase__ : List[str] = in_proj_weight[2_56:5_12, :]
lowercase__ : Any = in_proj_bias[2_56:5_12]
lowercase__ : Any = in_proj_weight[-2_56:, :]
lowercase__ : Union[str, Any] = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase__ : Tuple = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
lowercase__ : Any = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase__ : Any = in_proj_weight[:2_56, :]
lowercase__ : str = in_proj_bias[:2_56]
lowercase__ : Union[str, Any] = in_proj_weight[2_56:5_12, :]
lowercase__ : int = in_proj_bias[2_56:5_12]
lowercase__ : Optional[Any] = in_proj_weight[-2_56:, :]
lowercase__ : str = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
lowercase__ : Optional[Any] = state_dict.pop(
f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
lowercase__ : Optional[Any] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase__ : int = in_proj_weight_cross_attn[:2_56, :]
lowercase__ : Dict = in_proj_bias_cross_attn[:2_56]
lowercase__ : Any = in_proj_weight_cross_attn[2_56:5_12, :]
lowercase__ : Dict = in_proj_bias_cross_attn[2_56:5_12]
lowercase__ : Dict = in_proj_weight_cross_attn[-2_56:, :]
lowercase__ : Dict = in_proj_bias_cross_attn[-2_56:]
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
lowercase__ , lowercase__ : Tuple = image.size
lowercase__ : Optional[Any] = max(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Optional[Any] = 8_00 if '''detection''' in checkpoint_url else 10_00
lowercase__ : Any = target_max_size / current_max_size
lowercase__ : Optional[Any] = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
lowercase__ : List[Any] = F.to_tensor(__lowerCamelCase )
lowercase__ : Dict = F.normalize(__lowerCamelCase , mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] )
return image
@torch.no_grad()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
logger.info('''Converting model...''' )
# load original state dict
lowercase__ : Tuple = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowercase__ : int = rename_backbone_keys(__lowerCamelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(__lowerCamelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase__ : Tuple = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
lowercase__ : Optional[Any] = state_dict.pop(__lowerCamelCase )
lowercase__ : str = val
# create HuggingFace model and load state dict
lowercase__ : Dict = TableTransformerConfig(
backbone='''resnet18''' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowercase__ : int = 15
lowercase__ : int = 2
lowercase__ : Any = {0: '''table''', 1: '''table rotated'''}
lowercase__ : int = idalabel
lowercase__ : int = {v: k for k, v in idalabel.items()}
else:
lowercase__ : List[Any] = 1_25
lowercase__ : Optional[Any] = 6
lowercase__ : Dict = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
lowercase__ : Optional[Any] = idalabel
lowercase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
lowercase__ : Optional[int] = DetrImageProcessor(
format='''coco_detection''' , max_size=8_00 if '''detection''' in checkpoint_url else 10_00 )
lowercase__ : List[str] = TableTransformerForObjectDetection(__lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
model.eval()
# verify our conversion
lowercase__ : Optional[int] = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
lowercase__ : Union[str, Any] = hf_hub_download(repo_id='''nielsr/example-pdf''' , repo_type='''dataset''' , filename=__lowerCamelCase )
lowercase__ : List[Any] = Image.open(__lowerCamelCase ).convert('''RGB''' )
lowercase__ : Any = normalize(resize(__lowerCamelCase , __lowerCamelCase ) ).unsqueeze(0 )
lowercase__ : int = model(__lowerCamelCase )
if "detection" in checkpoint_url:
lowercase__ : List[Any] = (1, 15, 3)
lowercase__ : Dict = torch.tensor(
[[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]] )
lowercase__ : Any = torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]] )
else:
lowercase__ : Optional[Any] = (1, 1_25, 7)
lowercase__ : int = torch.tensor(
[[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]] )
lowercase__ : Tuple = torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __lowerCamelCase , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __lowerCamelCase , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
model.save_pretrained(__lowerCamelCase )
image_processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
lowercase__ : Optional[int] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(__lowerCamelCase )
image_processor.push_to_hub(__lowerCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
type=str,
choices=[
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
],
help='URL of the Table Transformer checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
lowerCAmelCase_ = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 16 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Dict ={
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = """ernie_m"""
SCREAMING_SNAKE_CASE__ : Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __lowercase = 2_5_0_0_0_2 , __lowercase = 7_6_8 , __lowercase = 1_2 , __lowercase = 1_2 , __lowercase = 3_0_7_2 , __lowercase = "gelu" , __lowercase = 0.1 , __lowercase = 0.1 , __lowercase = 5_1_4 , __lowercase = 0.02 , __lowercase = 1 , __lowercase = 1e-05 , __lowercase=None , __lowercase=False , __lowercase=0.0 , **__lowercase , ) -> Tuple:
super().__init__(pad_token_id=__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = vocab_size
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Tuple = num_hidden_layers
lowerCAmelCase_ : int = num_attention_heads
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : int = hidden_act
lowerCAmelCase_ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = max_position_embeddings
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : List[Any] = classifier_dropout
lowerCAmelCase_ : Any = is_decoder
lowerCAmelCase_ : List[Any] = act_dropout | 262 | 0 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_a = 16
_a = 32
def _A ( UpperCamelCase_ : Accelerator, UpperCamelCase_ : int = 16, UpperCamelCase_ : str = "bert-base-cased") -> List[str]:
'''simple docstring'''
__lowercase = AutoTokenizer.from_pretrained(UpperCamelCase_)
__lowercase = load_dataset("glue", "mrpc")
def tokenize_function(UpperCamelCase_ : Optional[Any]):
# max_length=None => use the model max length (it's actually the default)
__lowercase = tokenizer(examples["sentence1"], examples["sentence2"], truncation=UpperCamelCase_, max_length=UpperCamelCase_)
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__lowercase = datasets.map(
UpperCamelCase_, batched=UpperCamelCase_, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=UpperCamelCase_)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowercase = tokenized_datasets.rename_column("label", "labels")
def collate_fn(UpperCamelCase_ : Tuple):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase_, padding="max_length", max_length=128, return_tensors="pt")
return tokenizer.pad(UpperCamelCase_, padding="longest", return_tensors="pt")
# Instantiate dataloaders.
__lowercase = DataLoader(
tokenized_datasets["train"], shuffle=UpperCamelCase_, collate_fn=UpperCamelCase_, batch_size=UpperCamelCase_)
__lowercase = DataLoader(
tokenized_datasets["validation"], shuffle=UpperCamelCase_, collate_fn=UpperCamelCase_, batch_size=UpperCamelCase_)
return train_dataloader, eval_dataloader
def _A ( UpperCamelCase_ : Tuple, UpperCamelCase_ : Tuple) -> Tuple:
'''simple docstring'''
__lowercase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowercase = config["lr"]
__lowercase = int(config["num_epochs"])
__lowercase = int(config["seed"])
__lowercase = int(config["batch_size"])
__lowercase = args.model_name_or_path
set_seed(UpperCamelCase_)
__lowercase ,__lowercase = get_dataloaders(UpperCamelCase_, UpperCamelCase_, UpperCamelCase_)
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowercase = AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_, return_dict=UpperCamelCase_)
# Instantiate optimizer
__lowercase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__lowercase = optimizer_cls(params=model.parameters(), lr=UpperCamelCase_)
if accelerator.state.deepspeed_plugin is not None:
__lowercase = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
__lowercase = 1
__lowercase = (len(UpperCamelCase_) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
__lowercase = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase_, num_warmup_steps=0, num_training_steps=UpperCamelCase_, )
else:
__lowercase = DummyScheduler(UpperCamelCase_, total_num_steps=UpperCamelCase_, warmup_num_steps=0)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase = accelerator.prepare(
UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_)
# We need to keep track of how many total steps we have iterated over
__lowercase = 0
# We also need to keep track of the stating epoch so files are named properly
__lowercase = 0
# Now we train the model
__lowercase = evaluate.load("glue", "mrpc")
__lowercase = 0
__lowercase = {}
for epoch in range(UpperCamelCase_, UpperCamelCase_):
model.train()
for step, batch in enumerate(UpperCamelCase_):
__lowercase = model(**UpperCamelCase_)
__lowercase = outputs.loss
__lowercase = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase_)
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
__lowercase = 0
for step, batch in enumerate(UpperCamelCase_):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
with torch.no_grad():
__lowercase = model(**UpperCamelCase_)
__lowercase = outputs.logits.argmax(dim=-1)
# It is slightly faster to call this once, than multiple times
__lowercase ,__lowercase = accelerator.gather(
(predictions, batch["labels"])) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(UpperCamelCase_) - 1:
__lowercase = predictions[: len(eval_dataloader.dataset) - samples_seen]
__lowercase = references[: len(eval_dataloader.dataset) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=UpperCamelCase_, references=UpperCamelCase_, )
__lowercase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""", UpperCamelCase_)
__lowercase = eval_metric["accuracy"]
if best_performance < eval_metric["accuracy"]:
__lowercase = eval_metric["accuracy"]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
json.dump(UpperCamelCase_, UpperCamelCase_)
def _A ( ) -> List[str]:
'''simple docstring'''
__lowercase = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.")
parser.add_argument(
"--model_name_or_path", type=UpperCamelCase_, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=UpperCamelCase_, )
parser.add_argument(
"--output_dir", type=UpperCamelCase_, default=".", help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", )
parser.add_argument(
"--performance_lower_bound", type=UpperCamelCase_, default=UpperCamelCase_, help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.", )
parser.add_argument(
"--num_epochs", type=UpperCamelCase_, default=3, help="Number of train epochs.", )
__lowercase = parser.parse_args()
__lowercase = {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(UpperCamelCase_, UpperCamelCase_)
if __name__ == "__main__":
main()
| 17 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase=7 , __lowercase=3 , __lowercase=1_8 , __lowercase=3_0 , __lowercase=4_0_0 , __lowercase=True , __lowercase=None , __lowercase=True , __lowercase=None , ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = size if size is not None else {'''shortest_edge''': 2_0}
lowerCAmelCase_ : Any = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
lowerCAmelCase_ : Any = parent
lowerCAmelCase_ : Any = batch_size
lowerCAmelCase_ : Optional[int] = num_channels
lowerCAmelCase_ : Tuple = image_size
lowerCAmelCase_ : List[str] = min_resolution
lowerCAmelCase_ : Dict = max_resolution
lowerCAmelCase_ : Tuple = do_resize
lowerCAmelCase_ : Optional[Any] = size
lowerCAmelCase_ : Union[str, Any] = do_center_crop
lowerCAmelCase_ : Optional[Any] = crop_size
def lowercase_ ( self ) -> Union[str, Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = MobileNetVaImageProcessor if is_vision_available() else None
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Union[str, Any] = MobileNetVaImageProcessingTester(self )
@property
def lowercase_ ( self ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowercase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowercase , '''size''' ) )
self.assertTrue(hasattr(__lowercase , '''do_center_crop''' ) )
self.assertTrue(hasattr(__lowercase , '''crop_size''' ) )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 2_0} )
self.assertEqual(image_processor.crop_size , {'''height''': 1_8, '''width''': 1_8} )
lowerCAmelCase_ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2} )
self.assertEqual(image_processor.crop_size , {'''height''': 8_4, '''width''': 8_4} )
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> Union[str, Any]:
# Initialize image_processing
lowerCAmelCase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , Image.Image )
# Test not batched input
lowerCAmelCase_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Tuple = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self ) -> Optional[int]:
# Initialize image_processing
lowerCAmelCase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , np.ndarray )
# Test not batched input
lowerCAmelCase_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Tuple = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self ) -> Any:
# Initialize image_processing
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , torch.Tensor )
# Test not batched input
lowerCAmelCase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Dict = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , ) | 262 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import PoolFormerImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self : Any,_A : List[Any],_A : List[Any]=7,_A : Tuple=3,_A : Optional[Any]=30,_A : Optional[Any]=400,_A : Union[str, Any]=True,_A : Optional[int]=None,_A : str=0.9,_A : str=None,_A : str=True,_A : int=[0.5, 0.5, 0.5],_A : Dict=[0.5, 0.5, 0.5],):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = size if size is not None else {"shortest_edge": 30}
SCREAMING_SNAKE_CASE_ : int = crop_size if crop_size is not None else {"height": 30, "width": 30}
SCREAMING_SNAKE_CASE_ : Optional[Any] = parent
SCREAMING_SNAKE_CASE_ : Any = batch_size
SCREAMING_SNAKE_CASE_ : int = num_channels
SCREAMING_SNAKE_CASE_ : Any = min_resolution
SCREAMING_SNAKE_CASE_ : Union[str, Any] = max_resolution
SCREAMING_SNAKE_CASE_ : int = do_resize_and_center_crop
SCREAMING_SNAKE_CASE_ : Union[str, Any] = size
SCREAMING_SNAKE_CASE_ : List[str] = crop_pct
SCREAMING_SNAKE_CASE_ : str = crop_size
SCREAMING_SNAKE_CASE_ : List[str] = do_normalize
SCREAMING_SNAKE_CASE_ : Dict = image_mean
SCREAMING_SNAKE_CASE_ : Any = image_std
def __UpperCamelCase ( self : int ):
"""simple docstring"""
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class a__ ( A__ , unittest.TestCase ):
A = PoolFormerImageProcessor if is_vision_available() else None
def __UpperCamelCase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = PoolFormerImageProcessingTester(self )
@property
def __UpperCamelCase ( self : Tuple ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A,"do_resize_and_center_crop" ) )
self.assertTrue(hasattr(_A,"size" ) )
self.assertTrue(hasattr(_A,"crop_pct" ) )
self.assertTrue(hasattr(_A,"do_normalize" ) )
self.assertTrue(hasattr(_A,"image_mean" ) )
self.assertTrue(hasattr(_A,"image_std" ) )
def __UpperCamelCase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size,{"shortest_edge": 30} )
self.assertEqual(image_processor.crop_size,{"height": 30, "width": 30} )
SCREAMING_SNAKE_CASE_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict,size=42,crop_size=84 )
self.assertEqual(image_processor.size,{"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size,{"height": 84, "width": 84} )
def __UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
pass
def __UpperCamelCase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester,equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A,Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE_ : Optional[Any] = image_processing(image_inputs[0],return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),)
# Test batched
SCREAMING_SNAKE_CASE_ : Union[str, Any] = image_processing(_A,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),)
def __UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester,equal_resolution=_A,numpify=_A )
for image in image_inputs:
self.assertIsInstance(_A,np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE_ : List[Any] = image_processing(image_inputs[0],return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),)
# Test batched
SCREAMING_SNAKE_CASE_ : Dict = image_processing(_A,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),)
def __UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE_ : Dict = prepare_image_inputs(self.image_processor_tester,equal_resolution=_A,torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A,torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE_ : List[str] = image_processing(image_inputs[0],return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),)
# Test batched
SCREAMING_SNAKE_CASE_ : Dict = image_processing(_A,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),)
| 18 |
from __future__ import annotations
import math
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> None:
lowerCAmelCase_ : str = size
# approximate the overall size of segment tree with given value
lowerCAmelCase_ : Dict = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
lowerCAmelCase_ : Dict = [0 for i in range(0 , 4 * size )]
lowerCAmelCase_ : Optional[int] = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowercase_ ( self , __lowercase ) -> int:
return idx * 2
def lowercase_ ( self , __lowercase ) -> int:
return idx * 2 + 1
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase ) -> None:
if left_element == right_element:
lowerCAmelCase_ : Tuple = a[left_element - 1]
else:
lowerCAmelCase_ : int = (left_element + right_element) // 2
self.build(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase )
self.build(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase )
lowerCAmelCase_ : Any = max(
self.segment_tree[self.left(__lowercase )] , self.segment_tree[self.right(__lowercase )] )
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> bool:
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Union[str, Any] = False
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Any = self.lazy[idx]
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : Optional[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
lowerCAmelCase_ : Dict = val
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = val
lowerCAmelCase_ : List[Any] = val
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : List[str] = True
return True
lowerCAmelCase_ : Optional[Any] = (left_element + right_element) // 2
self.update(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
self.update(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase , __lowercase , __lowercase )
lowerCAmelCase_ : int = max(
self.segment_tree[self.left(__lowercase )] , self.segment_tree[self.right(__lowercase )] )
return True
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> int | float:
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Optional[Any] = False
if left_element != right_element:
lowerCAmelCase_ : List[Any] = self.lazy[idx]
lowerCAmelCase_ : Dict = self.lazy[idx]
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : Optional[int] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
lowerCAmelCase_ : List[Any] = (left_element + right_element) // 2
lowerCAmelCase_ : Tuple = self.query(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase , __lowercase )
lowerCAmelCase_ : List[Any] = self.query(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase , __lowercase )
return max(__lowercase , __lowercase )
def __str__( self ) -> str:
return str([self.query(1 , 1 , self.size , __lowercase , __lowercase ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
_UpperCAmelCase : str =[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
_UpperCAmelCase : List[str] =15
_UpperCAmelCase : Any =SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 262 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__A =logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , lowercase , lowercase , lowercase , **lowercase ) -> str:
lowerCamelCase_ = feature_size
lowerCamelCase_ = sampling_rate
lowerCamelCase_ = padding_value
lowerCamelCase_ = kwargs.pop("padding_side" , "right" )
lowerCamelCase_ = kwargs.pop("return_attention_mask" , lowercase )
super().__init__(**lowercase )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = True , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , ) -> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(lowercase , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
lowerCamelCase_ = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
f' to this method that includes {self.model_input_names[0]}, but you provided'
f' {list(processed_features.keys() )}' )
lowerCamelCase_ = processed_features[self.model_input_names[0]]
lowerCamelCase_ = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(lowercase ) == 0:
if return_attention_mask:
lowerCamelCase_ = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
lowerCamelCase_ = required_input[0]
if isinstance(lowercase , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
lowerCamelCase_ = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(lowercase ):
lowerCamelCase_ = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(lowercase ):
lowerCamelCase_ = "tf"
elif is_torch_tensor(lowercase ):
lowerCamelCase_ = "pt"
elif isinstance(lowercase , (int, float, list, tuple, np.ndarray) ):
lowerCamelCase_ = "np"
else:
raise ValueError(
f'type of {first_element} unknown: {type(lowercase )}. '
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
lowerCamelCase_ = to_numpy(lowercase )
else:
lowerCamelCase_ = [to_numpy(lowercase ) for v in value]
# Convert padding_strategy in PaddingStrategy
lowerCamelCase_ = self._get_padding_strategies(padding=lowercase , max_length=lowercase )
lowerCamelCase_ = processed_features[self.model_input_names[0]]
lowerCamelCase_ = len(lowercase )
if not all(len(lowercase ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
lowerCamelCase_ = []
for i in range(lowercase ):
lowerCamelCase_ = {k: v[i] for k, v in processed_features.items()}
# truncation
lowerCamelCase_ = self._truncate(
lowercase , max_length=lowercase , pad_to_multiple_of=lowercase , truncation=lowercase , )
truncated_inputs.append(lowercase )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
lowerCamelCase_ = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
lowerCamelCase_ = PaddingStrategy.MAX_LENGTH
lowerCamelCase_ = {}
for i in range(lowercase ):
# padding
lowerCamelCase_ = self._pad(
truncated_inputs[i] , max_length=lowercase , padding_strategy=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , )
for key, value in outputs.items():
if key not in batch_outputs:
lowerCamelCase_ = []
if value.dtype is np.dtype(np.floataa ):
lowerCamelCase_ = value.astype(np.floataa )
batch_outputs[key].append(lowercase )
return BatchFeature(lowercase , tensor_type=lowercase )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None , lowercase = PaddingStrategy.DO_NOT_PAD , lowercase = None , lowercase = None , ) -> dict:
lowerCamelCase_ = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
lowerCamelCase_ = len(lowercase )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowerCamelCase_ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowerCamelCase_ = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
lowerCamelCase_ = np.ones(len(lowercase ) , dtype=np.intaa )
if needs_to_be_padded:
lowerCamelCase_ = max_length - len(lowercase )
if self.padding_side == "right":
if return_attention_mask:
lowerCamelCase_ = np.pad(
processed_features["attention_mask"] , (0, difference) )
lowerCamelCase_ = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
lowerCamelCase_ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
lowerCamelCase_ = np.pad(
processed_features["attention_mask"] , (difference, 0) )
lowerCamelCase_ = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
lowerCamelCase_ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None , lowercase = None , lowercase = None , ) -> Dict:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
lowerCamelCase_ = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowerCamelCase_ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowerCamelCase_ = len(lowercase ) > max_length
if needs_to_be_truncated:
lowerCamelCase_ = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
lowerCamelCase_ = processed_features["attention_mask"][:max_length]
return processed_features
def SCREAMING_SNAKE_CASE_( self , lowercase=False , lowercase=None ) -> Any:
# Get padding strategy
if padding is not False:
if padding is True:
lowerCamelCase_ = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(lowercase , lowercase ):
lowerCamelCase_ = PaddingStrategy(lowercase )
elif isinstance(lowercase , lowercase ):
lowerCamelCase_ = padding
else:
lowerCamelCase_ = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 19 |
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
_UpperCAmelCase : Optional[int] ="""src/transformers"""
_UpperCAmelCase : str ="""docs/source/en"""
_UpperCAmelCase : Optional[int] ="""."""
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
with open(lowerCAmelCase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCAmelCase_ : int = f.readlines()
# Find the start prompt.
lowerCAmelCase_ : List[Any] = 0
while not lines[start_index].startswith(lowerCAmelCase_ ):
start_index += 1
start_index += 1
lowerCAmelCase_ : List[str] = start_index
while not lines[end_index].startswith(lowerCAmelCase_ ):
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 |
_UpperCAmelCase : Optional[Any] ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase : Optional[int] =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase : Dict =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.
_UpperCAmelCase : Optional[Any] =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase : Optional[int] =direct_transformers_import(TRANSFORMERS_PATH)
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : str = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , lowerCAmelCase_ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : Tuple = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_ )
lowerCAmelCase_ : int = (width - text_length) // 2
lowerCAmelCase_ : Union[str, Any] = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCAmelCase ( )-> str:
lowerCAmelCase_ : Any = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowerCAmelCase_ : Dict = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowerCAmelCase_ : List[Any] = {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.
lowerCAmelCase_ : Tuple = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[Any] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowerCAmelCase_ ):
lowerCAmelCase_ : Optional[int] = None
if attr_name.endswith('''Tokenizer''' ):
lowerCAmelCase_ : Union[str, Any] = slow_tokenizers
lowerCAmelCase_ : List[str] = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
lowerCAmelCase_ : int = fast_tokenizers
lowerCAmelCase_ : Union[str, Any] = attr_name[:-13]
elif _re_tf_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = tf_models
lowerCAmelCase_ : str = _re_tf_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_flax_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = flax_models
lowerCAmelCase_ : Union[str, Any] = _re_flax_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_pt_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Any = pt_models
lowerCAmelCase_ : List[Any] = _re_pt_models.match(lowerCAmelCase_ ).groups()[0]
if lookup_dict is not None:
while len(lowerCAmelCase_ ) > 0:
if attr_name in model_name_to_prefix.values():
lowerCAmelCase_ : Union[str, Any] = True
break
# Try again after removing the last word in the name
lowerCAmelCase_ : Any = ''''''.join(camel_case_split(lowerCAmelCase_ )[:-1] )
# Let's build that table!
lowerCAmelCase_ : int = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowerCAmelCase_ : Tuple = ['''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).
lowerCAmelCase_ : Union[str, Any] = [len(lowerCAmelCase_ ) + 2 for c in columns]
lowerCAmelCase_ : Optional[Any] = max([len(lowerCAmelCase_ ) for name in model_names] ) + 2
# Build the table per se
lowerCAmelCase_ : Dict = '''|''' + '''|'''.join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for c, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
lowerCAmelCase_ : List[str] = {True: '''✅''', False: '''❌'''}
for name in model_names:
lowerCAmelCase_ : List[Any] = model_name_to_prefix[name]
lowerCAmelCase_ : Union[str, Any] = [
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(lowerCAmelCase_ , lowerCAmelCase_ ) for l, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + "|\n"
return table
def lowerCAmelCase ( lowerCAmelCase_=False )-> Tuple:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = _find_text_in_file(
filename=os.path.join(lowerCAmelCase_ , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
lowerCAmelCase_ : Tuple = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowerCAmelCase_ , '''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__":
_UpperCAmelCase : List[Any] =argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_UpperCAmelCase : Tuple =parser.parse_args()
check_model_table(args.fix_and_overwrite) | 262 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
lowercase : List[Any] = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
lowercase : Dict = Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw ).convert("""RGB""" )
lowercase : int = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073) , (0.26862954, 0.26130258, 0.27577711) ),
] )
lowercase : Optional[Any] = transform(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 ).to(SCREAMING_SNAKE_CASE__ )
return image
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
if "visual_encoder" in key:
lowercase : Optional[int] = re.sub("""visual_encoder*""" , """vision_model.encoder""" , SCREAMING_SNAKE_CASE__ )
if "blocks" in key:
lowercase : Any = re.sub(R"""blocks""" , """layers""" , SCREAMING_SNAKE_CASE__ )
if "attn" in key:
lowercase : Dict = re.sub(R"""attn""" , """self_attn""" , SCREAMING_SNAKE_CASE__ )
if "norm1" in key:
lowercase : Any = re.sub(R"""norm1""" , """layer_norm1""" , SCREAMING_SNAKE_CASE__ )
if "norm2" in key:
lowercase : str = re.sub(R"""norm2""" , """layer_norm2""" , SCREAMING_SNAKE_CASE__ )
if "encoder.norm" in key:
lowercase : Union[str, Any] = re.sub(R"""encoder.norm""" , """post_layernorm""" , SCREAMING_SNAKE_CASE__ )
if "encoder.patch_embed.proj" in key:
lowercase : Union[str, Any] = re.sub(R"""encoder.patch_embed.proj""" , """embeddings.patch_embedding""" , SCREAMING_SNAKE_CASE__ )
if "encoder.pos_embed" in key:
lowercase : Tuple = re.sub(R"""encoder.pos_embed""" , """embeddings.position_embedding""" , SCREAMING_SNAKE_CASE__ )
if "encoder.cls_token" in key:
lowercase : Dict = re.sub(R"""encoder.cls_token""" , """embeddings.class_embedding""" , SCREAMING_SNAKE_CASE__ )
if "self_attn" in key:
lowercase : Tuple = re.sub(R"""self_attn.proj""" , """self_attn.projection""" , SCREAMING_SNAKE_CASE__ )
return key
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ) -> int:
if config_path is not None:
lowercase : Dict = BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
else:
lowercase : Tuple = BlipConfig(projection_dim=512 , text_config={} , vision_config={} )
lowercase : List[str] = BlipForConditionalGeneration(SCREAMING_SNAKE_CASE__ ).eval()
lowercase : Optional[int] = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""
lowercase : Tuple = blip_decoder(pretrained=SCREAMING_SNAKE_CASE__ , image_size=384 , vit="""base""" )
lowercase : Optional[int] = pt_model.eval()
lowercase : Union[str, Any] = pt_model.state_dict()
for key in modified_state_dict.copy():
lowercase : List[Any] = modified_state_dict.pop(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = rename_key(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = value
hf_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = 384
lowercase : Union[str, Any] = load_demo_image(image_size=SCREAMING_SNAKE_CASE__ , device="""cpu""" )
lowercase : Union[str, Any] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
lowercase : str = tokenizer(["""a picture of"""] ).input_ids
lowercase : Any = hf_model.generate(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]
lowercase : List[str] = hf_model.generate(SCREAMING_SNAKE_CASE__ )
assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
lowercase : Dict = (
"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
)
lowercase : Dict = blip_vqa(pretrained=SCREAMING_SNAKE_CASE__ , image_size=SCREAMING_SNAKE_CASE__ , vit="""base""" )
vqa_model.eval()
lowercase : str = vqa_model.state_dict()
for key in modified_state_dict.copy():
lowercase : Any = modified_state_dict.pop(SCREAMING_SNAKE_CASE__ )
lowercase : int = rename_key(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = value
lowercase : Dict = BlipForQuestionAnswering(SCREAMING_SNAKE_CASE__ )
hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = ["""How many dogs are in this image?"""]
lowercase : Any = tokenizer(SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" ).input_ids
lowercase : int = hf_vqa_model.generate(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + """_vqa""" )
lowercase : Any = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""
lowercase : Dict = blip_itm(pretrained=SCREAMING_SNAKE_CASE__ , image_size=SCREAMING_SNAKE_CASE__ , vit="""base""" )
itm_model.eval()
lowercase : int = itm_model.state_dict()
for key in modified_state_dict.copy():
lowercase : List[str] = modified_state_dict.pop(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = rename_key(SCREAMING_SNAKE_CASE__ )
lowercase : int = value
lowercase : Optional[int] = BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE__ )
lowercase : Dict = ["""A picture of a woman with a dog sitting in a beach"""]
lowercase : Tuple = tokenizer(
SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" , padding="""max_length""" , truncation=SCREAMING_SNAKE_CASE__ , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
hf_itm_model.eval()
lowercase : Union[str, Any] = hf_itm_model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , use_itm_head=SCREAMING_SNAKE_CASE__ )
lowercase : Any = hf_itm_model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , use_itm_head=SCREAMING_SNAKE_CASE__ )
assert out[0].item() == 0.2110687494277954
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.45698845386505127
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + """_itm""" )
if __name__ == "__main__":
lowercase : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
lowercase : int = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 20 |
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 ( )-> int:
lowerCAmelCase_ : int = ArgumentParser('''Transformers CLI tool''' , usage='''transformers-cli <command> [<args>]''' )
lowerCAmelCase_ : Dict = parser.add_subparsers(help='''transformers-cli command helpers''' )
# Register commands
ConvertCommand.register_subcommand(lowerCAmelCase_ )
DownloadCommand.register_subcommand(lowerCAmelCase_ )
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
RunCommand.register_subcommand(lowerCAmelCase_ )
ServeCommand.register_subcommand(lowerCAmelCase_ )
UserCommands.register_subcommand(lowerCAmelCase_ )
AddNewModelCommand.register_subcommand(lowerCAmelCase_ )
AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_ )
LfsCommands.register_subcommand(lowerCAmelCase_ )
PTtoTFCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
lowerCAmelCase_ : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , '''func''' ):
parser.print_help()
exit(1 )
# Run
lowerCAmelCase_ : List[Any] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main() | 262 | 0 |
def UpperCamelCase_( lowerCamelCase_ ) -> list:
if len(lowerCamelCase_ ) < 2:
return collection
def circle_sort_util(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> bool:
_lowercase : Any = False
if low == high:
return swapped
_lowercase : Union[str, Any] = low
_lowercase : List[Any] = high
while left < right:
if collection[left] > collection[right]:
_lowercase , _lowercase : Optional[int] = (
collection[right],
collection[left],
)
_lowercase : int = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
_lowercase , _lowercase : Optional[int] = (
collection[right + 1],
collection[left],
)
_lowercase : List[Any] = True
_lowercase : Tuple = low + int((high - low) / 2 )
_lowercase : Tuple = circle_sort_util(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
_lowercase : Any = circle_sort_util(lowerCamelCase_ , mid + 1 , lowerCamelCase_ )
return swapped or left_swap or right_swap
_lowercase : Union[str, Any] = True
while is_not_sorted is True:
_lowercase : Union[str, Any] = circle_sort_util(lowerCamelCase_ , 0 , len(lowerCamelCase_ ) - 1 )
return collection
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : str = input("Enter numbers separated by a comma:\n").strip()
SCREAMING_SNAKE_CASE : int = [int(item) for item in user_input.split(",")]
print(circle_sort(unsorted))
| 21 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
_UpperCAmelCase : Tuple =None
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : Dict ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
_UpperCAmelCase : Any ={
"""vocab_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
),
},
}
_UpperCAmelCase : int ={
"""facebook/nllb-large-en-ro""": 1024,
"""facebook/nllb-200-distilled-600M""": 1024,
}
# fmt: off
_UpperCAmelCase : Any =["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Dict = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""input_ids""", """attention_mask"""]
SCREAMING_SNAKE_CASE__ : int = NllbTokenizer
SCREAMING_SNAKE_CASE__ : List[int] = []
SCREAMING_SNAKE_CASE__ : List[int] = []
def __init__( self , __lowercase=None , __lowercase=None , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=False , **__lowercase , ) -> int:
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ : int = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
lowerCAmelCase_ : List[Any] = legacy_behaviour
super().__init__(
vocab_file=__lowercase , tokenizer_file=__lowercase , bos_token=__lowercase , eos_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , src_lang=__lowercase , tgt_lang=__lowercase , additional_special_tokens=__lowercase , legacy_behaviour=__lowercase , **__lowercase , )
lowerCAmelCase_ : Any = vocab_file
lowerCAmelCase_ : List[Any] = False if not self.vocab_file else True
lowerCAmelCase_ : Optional[Any] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
lowerCAmelCase_ : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(__lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowerCAmelCase_ : Any = src_lang if src_lang is not None else '''eng_Latn'''
lowerCAmelCase_ : str = self.convert_tokens_to_ids(self._src_lang )
lowerCAmelCase_ : Optional[int] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def lowercase_ ( self ) -> str:
return self._src_lang
@src_lang.setter
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : Any = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowercase_ ( self , __lowercase , __lowercase = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowercase_ ( self , __lowercase , __lowercase = None ) -> List[int]:
lowerCAmelCase_ : Optional[Any] = [self.sep_token_id]
lowerCAmelCase_ : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , **__lowercase ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : int = self(__lowercase , add_special_tokens=__lowercase , return_tensors=__lowercase , **__lowercase )
lowerCAmelCase_ : Dict = self.convert_tokens_to_ids(__lowercase )
lowerCAmelCase_ : List[Any] = tgt_lang_id
return inputs
def lowercase_ ( self , __lowercase , __lowercase = "eng_Latn" , __lowercase = None , __lowercase = "fra_Latn" , **__lowercase , ) -> BatchEncoding:
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : List[str] = tgt_lang
return super().prepare_seqaseq_batch(__lowercase , __lowercase , **__lowercase )
def lowercase_ ( self ) -> List[Any]:
return self.set_src_lang_special_tokens(self.src_lang )
def lowercase_ ( self ) -> str:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : List[str] = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
lowerCAmelCase_ : Any = []
lowerCAmelCase_ : List[str] = [self.eos_token_id, self.cur_lang_code]
else:
lowerCAmelCase_ : Optional[int] = [self.cur_lang_code]
lowerCAmelCase_ : List[Any] = [self.eos_token_id]
lowerCAmelCase_ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ : List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ : Any = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : Dict = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
lowerCAmelCase_ : List[Any] = []
lowerCAmelCase_ : Any = [self.eos_token_id, self.cur_lang_code]
else:
lowerCAmelCase_ : Any = [self.cur_lang_code]
lowerCAmelCase_ : Any = [self.eos_token_id]
lowerCAmelCase_ : List[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ : Tuple = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ : Optional[Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowercase_ ( self , __lowercase , __lowercase = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__lowercase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
return
lowerCAmelCase_ : Any = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
copyfile(self.vocab_file , __lowercase )
return (out_vocab_file,) | 262 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : str = CanineTokenizer
_lowerCamelCase : Tuple = False
def lowercase ( self : List[Any] ):
super().setUp()
_UpperCAmelCase = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : List[str] ):
return CanineTokenizer.from_pretrained("google/canine-s" )
def lowercase ( self : Union[str, Any] , **snake_case_ : List[Any] ):
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname , **snake_case_ )
_UpperCAmelCase = 1_0_2_4
return tokenizer
@require_torch
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.canine_tokenizer
_UpperCAmelCase = ["Life is like a box of chocolates.", "You never know what you're gonna get."]
# fmt: off
_UpperCAmelCase = [5_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 0, 0, 0, 0]
# fmt: on
_UpperCAmelCase = tokenizer(snake_case_ , padding=snake_case_ , return_tensors="pt" )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = list(batch.input_ids.numpy()[0] )
self.assertListEqual(snake_case_ , snake_case_ )
self.assertEqual((2, 3_9) , batch.input_ids.shape )
self.assertEqual((2, 3_9) , batch.attention_mask.shape )
@require_torch
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.canine_tokenizer
_UpperCAmelCase = ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
_UpperCAmelCase = tokenizer(snake_case_ , padding=snake_case_ , return_tensors="pt" )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("input_ids" , snake_case_ )
self.assertIn("attention_mask" , snake_case_ )
self.assertIn("token_type_ids" , snake_case_ )
@require_torch
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.canine_tokenizer
_UpperCAmelCase = [
"What's the weater?",
"It's about 25 degrees.",
]
_UpperCAmelCase = tokenizer(
text_target=snake_case_ , max_length=3_2 , padding="max_length" , truncation=snake_case_ , return_tensors="pt" )
self.assertEqual(3_2 , targets["input_ids"].shape[1] )
def lowercase ( self : Union[str, Any] ):
# safety check on max_len default value so we are sure the test works
_UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
_UpperCAmelCase = 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
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = " He is very happy, UNwant\u00E9d,running"
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
tokenizer.save_pretrained(snake_case_ )
_UpperCAmelCase = tokenizer.__class__.from_pretrained(snake_case_ )
_UpperCAmelCase = after_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
shutil.rmtree(snake_case_ )
_UpperCAmelCase = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = " He is very happy, UNwant\u00E9d,running"
_UpperCAmelCase = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
_UpperCAmelCase = chr(0Xe0_07 )
additional_special_tokens.append(snake_case_ )
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens} )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
tokenizer.save_pretrained(snake_case_ )
_UpperCAmelCase = tokenizer.__class__.from_pretrained(snake_case_ )
_UpperCAmelCase = after_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
self.assertIn(snake_case_ , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
_UpperCAmelCase = tokenizer.__class__.from_pretrained(snake_case_ , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(snake_case_ )
def lowercase ( self : int ):
_UpperCAmelCase = self.get_tokenizers(do_lower_case=snake_case_ )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
_UpperCAmelCase , _UpperCAmelCase = self.get_clean_sequence(snake_case_ )
# a special token for Canine can be defined as follows:
_UpperCAmelCase = 0Xe0_05
_UpperCAmelCase = chr(snake_case_ )
tokenizer.add_special_tokens({"cls_token": special_token} )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(len(snake_case_ ) , 1 )
_UpperCAmelCase = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=snake_case_ )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(snake_case_ , input_encoded + special_token_id )
_UpperCAmelCase = tokenizer.decode(snake_case_ , skip_special_tokens=snake_case_ )
self.assertTrue(special_token not in decoded )
def lowercase ( self : int ):
_UpperCAmelCase = self.get_tokenizers(do_lower_case=snake_case_ )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
_UpperCAmelCase = chr(0Xe0_05 )
_UpperCAmelCase = chr(0Xe0_06 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=snake_case_ )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]} )
_UpperCAmelCase = tokenizer.tokenize(snake_case_ )
_UpperCAmelCase = tokenizer.tokenize(snake_case_ )
self.assertEqual(len(snake_case_ ) , 1 )
self.assertEqual(len(snake_case_ ) , 1 )
self.assertEqual(token_a[0] , snake_case_ )
self.assertEqual(token_a[0] , snake_case_ )
@require_tokenizers
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_tokenizers(do_lower_case=snake_case_ )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# a special token for Canine can be defined as follows:
_UpperCAmelCase = 0Xe0_06
_UpperCAmelCase = chr(snake_case_ )
_UpperCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ )
tokenizer.add_special_tokens({"additional_special_tokens": [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(snake_case_ )
tokenizer.from_pretrained(snake_case_ )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(snake_case_ )
with open(os.path.join(snake_case_ , "special_tokens_map.json" ) , encoding="utf-8" ) as json_file:
_UpperCAmelCase = json.load(snake_case_ )
with open(os.path.join(snake_case_ , "tokenizer_config.json" ) , encoding="utf-8" ) as json_file:
_UpperCAmelCase = json.load(snake_case_ )
# a special token for Canine can be defined as follows:
_UpperCAmelCase = 0Xe0_06
_UpperCAmelCase = chr(snake_case_ )
_UpperCAmelCase = [new_token_a]
_UpperCAmelCase = [new_token_a]
with open(os.path.join(snake_case_ , "special_tokens_map.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(snake_case_ , snake_case_ )
with open(os.path.join(snake_case_ , "tokenizer_config.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(snake_case_ , snake_case_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
_UpperCAmelCase = tokenizer_class.from_pretrained(snake_case_ , extra_ids=0 )
self.assertIn(snake_case_ , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
_UpperCAmelCase = 0Xe0_07
_UpperCAmelCase = chr(snake_case_ )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
_UpperCAmelCase = [AddedToken(snake_case_ , lstrip=snake_case_ )]
_UpperCAmelCase = tokenizer_class.from_pretrained(
snake_case_ , additional_special_tokens=snake_case_ , extra_ids=0 )
self.assertIn(snake_case_ , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def lowercase ( self : Tuple ):
_UpperCAmelCase = self.get_tokenizers(do_lower_case=snake_case_ )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
_UpperCAmelCase = "hello world"
if self.space_between_special_tokens:
_UpperCAmelCase = "[CLS] hello world [SEP]"
else:
_UpperCAmelCase = input
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.decode(snake_case_ , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(snake_case_ , [output, output.lower()] )
def lowercase ( self : str ):
_UpperCAmelCase = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
_UpperCAmelCase = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
_UpperCAmelCase = "a"
_UpperCAmelCase = ord(snake_case_ )
for attr in attributes_list:
setattr(snake_case_ , attr + "_id" , snake_case_ )
self.assertEqual(getattr(snake_case_ , snake_case_ ) , snake_case_ )
self.assertEqual(getattr(snake_case_ , attr + "_id" ) , snake_case_ )
setattr(snake_case_ , attr + "_id" , snake_case_ )
self.assertEqual(getattr(snake_case_ , snake_case_ ) , snake_case_ )
self.assertEqual(getattr(snake_case_ , attr + "_id" ) , snake_case_ )
setattr(snake_case_ , "additional_special_tokens_ids" , [] )
self.assertListEqual(getattr(snake_case_ , "additional_special_tokens" ) , [] )
self.assertListEqual(getattr(snake_case_ , "additional_special_tokens_ids" ) , [] )
_UpperCAmelCase = 0Xe0_06
_UpperCAmelCase = chr(snake_case_ )
setattr(snake_case_ , "additional_special_tokens_ids" , [additional_special_token_id] )
self.assertListEqual(getattr(snake_case_ , "additional_special_tokens" ) , [additional_special_token] )
self.assertListEqual(getattr(snake_case_ , "additional_special_tokens_ids" ) , [additional_special_token_id] )
def lowercase ( self : Any ):
pass
def lowercase ( self : List[Any] ):
pass
def lowercase ( self : Union[str, Any] ):
pass
def lowercase ( self : List[Any] ):
pass
def lowercase ( self : List[Any] ):
pass
def lowercase ( self : int ):
pass
def lowercase ( self : int ):
pass
def lowercase ( self : Optional[Any] ):
pass
| 22 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
_UpperCAmelCase : Optional[Any] =NewType("""DataClass""", Any)
_UpperCAmelCase : Dict =NewType("""DataClassType""", Any)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def lowerCAmelCase ( lowerCAmelCase_ )-> Callable[[str], Any]:
lowerCAmelCase_ : str = {str(lowerCAmelCase_ ): choice for choice in choices}
return lambda lowerCAmelCase_ : str_to_choice.get(lowerCAmelCase_ , lowerCAmelCase_ )
def lowerCAmelCase ( *,
lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = dataclasses.MISSING , lowerCAmelCase_ = dataclasses.MISSING , lowerCAmelCase_ = None , **lowerCAmelCase_ , )-> dataclasses.Field:
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
lowerCAmelCase_ : Dict = {}
if aliases is not None:
lowerCAmelCase_ : str = aliases
if help is not None:
lowerCAmelCase_ : Tuple = help
return dataclasses.field(metadata=lowerCAmelCase_ , default=lowerCAmelCase_ , default_factory=lowerCAmelCase_ , **lowerCAmelCase_ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Iterable[DataClassType]
def __init__( self , __lowercase , **__lowercase ) -> List[str]:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
lowerCAmelCase_ : Optional[int] = ArgumentDefaultsHelpFormatter
super().__init__(**__lowercase )
if dataclasses.is_dataclass(__lowercase ):
lowerCAmelCase_ : Union[str, Any] = [dataclass_types]
lowerCAmelCase_ : List[Any] = list(__lowercase )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__lowercase )
@staticmethod
def lowercase_ ( __lowercase , __lowercase ) -> Union[str, Any]:
lowerCAmelCase_ : Optional[Any] = f"""--{field.name}"""
lowerCAmelCase_ : Tuple = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __lowercase ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
lowerCAmelCase_ : List[str] = kwargs.pop('''aliases''' , [] )
if isinstance(__lowercase , __lowercase ):
lowerCAmelCase_ : Optional[Any] = [aliases]
lowerCAmelCase_ : Any = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__lowercase , '''UnionType''' ) and isinstance(__lowercase , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__lowercase ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__lowercase ) not in field.type.__args__:
# filter `str` in Union
lowerCAmelCase_ : List[Any] = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
lowerCAmelCase_ : Dict = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
lowerCAmelCase_ : str = (
field.type.__args__[0] if isinstance(__lowercase , field.type.__args__[1] ) else field.type.__args__[1]
)
lowerCAmelCase_ : List[Any] = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
lowerCAmelCase_ : Dict = {}
if origin_type is Literal or (isinstance(field.type , __lowercase ) and issubclass(field.type , __lowercase )):
if origin_type is Literal:
lowerCAmelCase_ : Optional[Any] = field.type.__args__
else:
lowerCAmelCase_ : int = [x.value for x in field.type]
lowerCAmelCase_ : str = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
lowerCAmelCase_ : str = field.default
else:
lowerCAmelCase_ : Tuple = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
lowerCAmelCase_ : Tuple = copy(__lowercase )
# Hack because type=bool in argparse does not behave as we want.
lowerCAmelCase_ : Dict = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
lowerCAmelCase_ : Union[str, Any] = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
lowerCAmelCase_ : List[str] = default
# This tells argparse we accept 0 or 1 value after --field_name
lowerCAmelCase_ : int = '''?'''
# This is the value that will get picked if we do --field_name (without value)
lowerCAmelCase_ : List[Any] = True
elif isclass(__lowercase ) and issubclass(__lowercase , __lowercase ):
lowerCAmelCase_ : Union[str, Any] = field.type.__args__[0]
lowerCAmelCase_ : Dict = '''+'''
if field.default_factory is not dataclasses.MISSING:
lowerCAmelCase_ : Any = field.default_factory()
elif field.default is dataclasses.MISSING:
lowerCAmelCase_ : Optional[int] = True
else:
lowerCAmelCase_ : List[Any] = field.type
if field.default is not dataclasses.MISSING:
lowerCAmelCase_ : Dict = field.default
elif field.default_factory is not dataclasses.MISSING:
lowerCAmelCase_ : List[Any] = field.default_factory()
else:
lowerCAmelCase_ : int = True
parser.add_argument(__lowercase , *__lowercase , **__lowercase )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
lowerCAmelCase_ : Any = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__lowercase )
def lowercase_ ( self , __lowercase ) -> List[Any]:
if hasattr(__lowercase , '''_argument_group_name''' ):
lowerCAmelCase_ : str = self.add_argument_group(dtype._argument_group_name )
else:
lowerCAmelCase_ : Dict = self
try:
lowerCAmelCase_ : Dict[str, type] = get_type_hints(__lowercase )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 1_0) and "unsupported operand type(s) for |" in str(__lowercase ):
lowerCAmelCase_ : Any = '''.'''.join(map(__lowercase , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__lowercase ):
if not field.init:
continue
lowerCAmelCase_ : Optional[int] = type_hints[field.name]
self._parse_dataclass_field(__lowercase , __lowercase )
def lowercase_ ( self , __lowercase=None , __lowercase=False , __lowercase=True , __lowercase=None , __lowercase=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
lowerCAmelCase_ : str = []
if args_filename:
args_files.append(Path(__lowercase ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
lowerCAmelCase_ : str = ArgumentParser()
args_file_parser.add_argument(__lowercase , type=__lowercase , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = args_file_parser.parse_known_args(args=__lowercase )
lowerCAmelCase_ : int = vars(__lowercase ).get(args_file_flag.lstrip('''-''' ) , __lowercase )
if cmd_args_file_paths:
args_files.extend([Path(__lowercase ) for p in cmd_args_file_paths] )
lowerCAmelCase_ : Dict = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
lowerCAmelCase_ : Any = file_args + args if args is not None else file_args + sys.argv[1:]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = self.parse_known_args(args=__lowercase )
lowerCAmelCase_ : Any = []
for dtype in self.dataclass_types:
lowerCAmelCase_ : str = {f.name for f in dataclasses.fields(__lowercase ) if f.init}
lowerCAmelCase_ : str = {k: v for k, v in vars(__lowercase ).items() if k in keys}
for k in keys:
delattr(__lowercase , __lowercase )
lowerCAmelCase_ : Optional[int] = dtype(**__lowercase )
outputs.append(__lowercase )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__lowercase )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
lowerCAmelCase_ : int = set(args.keys() )
lowerCAmelCase_ : str = []
for dtype in self.dataclass_types:
lowerCAmelCase_ : int = {f.name for f in dataclasses.fields(__lowercase ) if f.init}
lowerCAmelCase_ : List[str] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
lowerCAmelCase_ : List[str] = dtype(**__lowercase )
outputs.append(__lowercase )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__lowercase )}""" )
return tuple(__lowercase )
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
with open(Path(__lowercase ) , encoding='''utf-8''' ) as open_json_file:
lowerCAmelCase_ : Dict = json.loads(open_json_file.read() )
lowerCAmelCase_ : str = self.parse_dict(__lowercase , allow_extra_keys=__lowercase )
return tuple(__lowercase )
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
lowerCAmelCase_ : Optional[Any] = self.parse_dict(yaml.safe_load(Path(__lowercase ).read_text() ) , allow_extra_keys=__lowercase )
return tuple(__lowercase ) | 262 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__: Optional[int] = logging.get_logger(__name__)
UpperCamelCase__: int = {
"google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
# See all ViT models at https://huggingface.co/models?filter=vit
}
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = """vit"""
def __init__( self : Dict , __snake_case : int=768 , __snake_case : Optional[int]=12 , __snake_case : Any=12 , __snake_case : Optional[Any]=3072 , __snake_case : Any="gelu" , __snake_case : str=0.0 , __snake_case : str=0.0 , __snake_case : Dict=0.02 , __snake_case : Optional[int]=1E-12 , __snake_case : List[str]=224 , __snake_case : Tuple=16 , __snake_case : Dict=3 , __snake_case : List[str]=True , __snake_case : Optional[int]=16 , **__snake_case : Dict , ) -> Optional[Any]:
super().__init__(**__snake_case )
UpperCAmelCase : str = hidden_size
UpperCAmelCase : int = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : str = intermediate_size
UpperCAmelCase : Union[str, Any] = hidden_act
UpperCAmelCase : List[str] = hidden_dropout_prob
UpperCAmelCase : Any = attention_probs_dropout_prob
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Optional[int] = layer_norm_eps
UpperCAmelCase : Union[str, Any] = image_size
UpperCAmelCase : Any = patch_size
UpperCAmelCase : Union[str, Any] = num_channels
UpperCAmelCase : Any = qkv_bias
UpperCAmelCase : Union[str, Any] = encoder_stride
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse("""1.11""" )
@property
def A ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def A ( self : Union[str, Any] ) -> float:
return 1E-4
| 23 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCAmelCase ( lowerCAmelCase_ )-> Union[str, Any]:
return EnvironmentCommand()
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def lowercase_ ( __lowercase ) -> List[Any]:
lowerCAmelCase_ : List[str] = parser.add_parser('''env''' )
download_parser.set_defaults(func=__lowercase )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Optional[Any] = huggingface_hub.__version__
lowerCAmelCase_ : str = '''not installed'''
lowerCAmelCase_ : str = '''NA'''
if is_torch_available():
import torch
lowerCAmelCase_ : Any = torch.__version__
lowerCAmelCase_ : str = torch.cuda.is_available()
lowerCAmelCase_ : List[str] = '''not installed'''
if is_transformers_available():
import transformers
lowerCAmelCase_ : Any = transformers.__version__
lowerCAmelCase_ : Optional[Any] = '''not installed'''
if is_accelerate_available():
import accelerate
lowerCAmelCase_ : List[Any] = accelerate.__version__
lowerCAmelCase_ : List[str] = '''not installed'''
if is_xformers_available():
import xformers
lowerCAmelCase_ : Optional[Any] = xformers.__version__
lowerCAmelCase_ : int = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(__lowercase ) )
return info
@staticmethod
def lowercase_ ( __lowercase ) -> str:
return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n" | 262 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( snake_case_ : str ) -> Any:
def decorator(snake_case_ : str ):
__snake_case = getattr(snake_case_ , '''handle_key''' , [] )
handle += [key]
setattr(snake_case_ , '''handle_key''' , snake_case_ )
return func
return decorator
def lowerCamelCase__ ( *snake_case_ : List[str] ) -> Optional[Any]:
def decorator(snake_case_ : Dict ):
__snake_case = getattr(snake_case_ , '''handle_key''' , [] )
handle += keys
setattr(snake_case_ , '''handle_key''' , snake_case_ )
return func
return decorator
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __new__(cls : Union[str, Any] , a__ : Union[str, Any] , a__ : Union[str, Any] , a__ : str ):
"""simple docstring"""
__snake_case = super().__new__(cls , a__ , a__ , a__ )
if not hasattr(a__ , '''key_handler''' ):
setattr(a__ , '''key_handler''' , {} )
setattr(a__ , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
__snake_case = getattr(a__ , '''handle_key''' , [] )
for key in handled_keys:
__snake_case = value
return new_cls
@staticmethod
def a (cls : Optional[int] ):
"""simple docstring"""
__snake_case = get_character()
if char != KEYMAP["undefined"]:
__snake_case = ord(a__ )
__snake_case = cls.key_handler.get(a__ )
if handler:
__snake_case = char
return handler(cls )
else:
return None
def lowerCamelCase__ ( cls : List[Any] ) -> Union[str, Any]:
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 24 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = JukeboxTokenizer
SCREAMING_SNAKE_CASE__ : int = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
import torch
lowerCAmelCase_ : Union[str, Any] = JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
lowerCAmelCase_ : Any = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : List[str] = [
torch.tensor([[
0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
7_6, 7_6]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def lowercase_ ( self ) -> List[Any]:
import torch
lowerCAmelCase_ : Any = JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
lowerCAmelCase_ : str = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : Tuple = [
torch.tensor([[
0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 7_7, 7_7]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) ) | 262 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase_ (a__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Dict = DiTPipeline
__UpperCamelCase : List[Any] = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__UpperCamelCase : Union[str, Any] = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
__UpperCamelCase : int = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__UpperCamelCase : List[str] = False
def __magic_name__ (self ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=SCREAMING_SNAKE_CASE__ , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=10_00 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=SCREAMING_SNAKE_CASE__ , )
SCREAMING_SNAKE_CASE__ : Dict = AutoencoderKL()
SCREAMING_SNAKE_CASE__ : Tuple = DDIMScheduler()
SCREAMING_SNAKE_CASE__ : List[Any] = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ) -> Dict:
"""simple docstring"""
if str(SCREAMING_SNAKE_CASE__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = """cpu"""
SCREAMING_SNAKE_CASE__ : Dict = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Optional[int] = self.pipeline_class(**SCREAMING_SNAKE_CASE__ )
pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = pipe(**SCREAMING_SNAKE_CASE__ ).images
SCREAMING_SNAKE_CASE__ : Dict = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE__ , 1E-3 )
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=SCREAMING_SNAKE_CASE__ , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def __magic_name__ (self ) -> str:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE__ : Dict = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
SCREAMING_SNAKE_CASE__ : int = pipe.get_label_ids(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = pipe(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Tuple = load_numpy(
F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy''' )
assert np.abs((expected_image - image).max() ) < 1E-2
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
SCREAMING_SNAKE_CASE__ : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ["""vase""", """umbrella"""]
SCREAMING_SNAKE_CASE__ : int = pipe.get_label_ids(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] = pipe(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : List[str] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
F'''/dit/{word}_512.npy''' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 25 |
from __future__ import annotations
import requests
def lowerCAmelCase ( lowerCAmelCase_ )-> dict:
lowerCAmelCase_ : List[Any] = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(lowerCAmelCase_ ).json()
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> list[dict]:
lowerCAmelCase_ : List[Any] = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
lowerCAmelCase_ : Tuple = requests.get(lowerCAmelCase_ ).json()[:max_stories]
return [get_hackernews_story(lowerCAmelCase_ ) for story_id in story_ids]
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> str:
lowerCAmelCase_ : Optional[Any] = hackernews_top_stories(lowerCAmelCase_ )
return "\n".join('''* [{title}]({url})'''.format(**lowerCAmelCase_ ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown()) | 262 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"CTRLForSequenceClassification",
"CTRLLMHeadModel",
"CTRLModel",
"CTRLPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCTRLForSequenceClassification",
"TFCTRLLMHeadModel",
"TFCTRLModel",
"TFCTRLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 |
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
_UpperCAmelCase : List[str] =get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_UpperCAmelCase : Optional[int] =25_0004
_UpperCAmelCase : Tuple =25_0020
@require_sentencepiece
@require_tokenizers
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = MBartTokenizer
SCREAMING_SNAKE_CASE__ : Dict = MBartTokenizerFast
SCREAMING_SNAKE_CASE__ : Tuple = True
SCREAMING_SNAKE_CASE__ : List[str] = True
def lowercase_ ( self ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase_ : str = MBartTokenizer(__lowercase , keep_accents=__lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[int] = MBartTokenizer(__lowercase , keep_accents=__lowercase )
lowerCAmelCase_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowerCAmelCase_ : Optional[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [
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''',
'''.''',
] , )
lowerCAmelCase_ : Dict = tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(
__lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCAmelCase_ : Union[str, Any] = tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [
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 lowercase_ ( 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
lowerCAmelCase_ : int = (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})""" ):
lowerCAmelCase_ : Optional[int] = self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : int = self.tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = tempfile.mkdtemp()
lowerCAmelCase_ : Union[str, Any] = tokenizer_r.save_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.save_pretrained(__lowercase )
# 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 ) )
lowerCAmelCase_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Tuple = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=True
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : int = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Tuple = tokenizer_p.save_pretrained(__lowercase )
# Checks it save with the same files
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Optional[int] = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=False
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Optional[int] = tokenizer_p.save_pretrained(__lowercase )
# 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
lowerCAmelCase_ : Dict = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = """facebook/mbart-large-en-ro"""
SCREAMING_SNAKE_CASE__ : int = [
""" 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.""",
]
SCREAMING_SNAKE_CASE__ : Optional[int] = [
"""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.""",
]
SCREAMING_SNAKE_CASE__ : str = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def lowercase_ ( cls ) -> Optional[int]:
lowerCAmelCase_ : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
lowerCAmelCase_ : Optional[Any] = 1
return cls
def lowercase_ ( self ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 2_5_0_0_2_0 )
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
def lowercase_ ( self ) -> Any:
self.assertIn(__lowercase , self.tokenizer.all_special_ids )
lowerCAmelCase_ : Union[str, Any] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
lowerCAmelCase_ : Tuple = self.tokenizer.decode(__lowercase , skip_special_tokens=__lowercase )
lowerCAmelCase_ : List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertNotIn(self.tokenizer.eos_token , __lowercase )
def lowercase_ ( self ) -> Any:
lowerCAmelCase_ : Union[str, Any] = ['''this is gunna be a long sentence ''' * 2_0]
assert isinstance(src_text[0] , __lowercase )
lowerCAmelCase_ : str = 1_0
lowerCAmelCase_ : Tuple = self.tokenizer(__lowercase , max_length=__lowercase , truncation=__lowercase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __lowercase )
self.assertEqual(len(__lowercase ) , __lowercase )
def lowercase_ ( self ) -> int:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = tempfile.mkdtemp()
lowerCAmelCase_ : int = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = MBartTokenizer.from_pretrained(__lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __lowercase )
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Tuple = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowercase , return_tensors='''pt''' )
lowerCAmelCase_ : Tuple = 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 lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
lowerCAmelCase_ : int = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
lowerCAmelCase_ : str = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
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 lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = self.tokenizer(self.src_text , padding=__lowercase , truncation=__lowercase , max_length=3 , return_tensors='''pt''' )
lowerCAmelCase_ : Any = self.tokenizer(
text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=1_0 , return_tensors='''pt''' )
lowerCAmelCase_ : int = targets['''input_ids''']
lowerCAmelCase_ : Optional[Any] = shift_tokens_right(__lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(__lowercase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 2_5_0_0_0_1,
} , ) | 262 | 0 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__lowercase : Any = 20_48
__lowercase : Optional[int] = 40_96
__lowercase : int = 42
__lowercase : Tuple = os.environ.pop('PROCESS_TRAIN', 'false')
__lowercase : List[Any] = {'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict ):
def choose_first(_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[str]=False ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) == 1:
__a : Optional[int] = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__a : Any = {k: [a[k]] for k in a}
if len(a['start_token'] ) > 0:
break
return a
__a : int = {'id': example['id']}
__a : str = example['annotations']
__a : str = annotation['yes_no_answer']
if 0 in yes_no_answer or 1 in yes_no_answer:
__a : Any = ['yes'] if 1 in yes_no_answer else ['no']
__a : List[Any] = []
__a : Optional[Any] = []
__a : Tuple = ['<cls>']
else:
__a : Optional[int] = ['short']
__a : List[Any] = choose_first(annotation['short_answers'] )
if len(out['start_token'] ) == 0:
# answer will be long if short is not available
__a : Optional[Any] = ['long']
__a : Any = choose_first(annotation['long_answer'] , is_long_answer=_SCREAMING_SNAKE_CASE )
__a : List[str] = []
answer.update(_SCREAMING_SNAKE_CASE )
# disregard some samples
if len(answer['start_token'] ) > 1 or answer["start_token"] == answer["end_token"]:
__a : List[str] = True
else:
__a : Optional[Any] = False
__a : List[str] = ['start_token', 'end_token', 'start_byte', 'end_byte', 'text']
if not all(isinstance(answer[k] , _SCREAMING_SNAKE_CASE ) for k in cols ):
raise ValueError('Issue in ID' , example['id'] )
return answer
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : int=False ):
__a : Optional[int] = _get_single_answer(_SCREAMING_SNAKE_CASE )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__a : int = example['document']['tokens']
__a : Tuple = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
return {
"context": " ".join(_SCREAMING_SNAKE_CASE ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
__a : List[Any] = ['start_token', 'end_token']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
__a : Tuple = example['document']['tokens']
__a : int = answer['start_token']
__a : str = answer['end_token']
__a : List[Any] = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
__a : List[str] = ' '.join(context[start_token:end_token] )
# checking above code
if assertion:
__a : Dict = doc['is_html'][answer['start_token'] : answer['end_token']]
__a : str = doc['token'][answer['start_token'] : answer['end_token']]
__a : Optional[int] = ' '.join([old[i] for i in range(len(_SCREAMING_SNAKE_CASE ) ) if not is_html[i]] )
if new != old:
print('ID:' , example['id'] )
print('New:' , _SCREAMING_SNAKE_CASE , end='\n' )
print('Old:' , _SCREAMING_SNAKE_CASE , end='\n\n' )
return {
"context": " ".join(_SCREAMING_SNAKE_CASE ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Tuple=2_048 , _SCREAMING_SNAKE_CASE : Optional[Any]=4_096 , _SCREAMING_SNAKE_CASE : int=True ):
# overlap will be of doc_stride - q_len
__a : int = get_context_and_ans(_SCREAMING_SNAKE_CASE , assertion=_SCREAMING_SNAKE_CASE )
__a : int = out['answer']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
__a : Optional[Any] = tokenizer(example['question']['text'] , out['context'] ).input_ids
__a : Any = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__a : Dict = []
__a : Tuple = []
__a : Optional[Any] = input_ids[:q_len]
__a : Tuple = range(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) , max_length - doc_stride )
for i in doc_start_indices:
__a : Dict = i + max_length - q_len
__a : int = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer['category'][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(_SCREAMING_SNAKE_CASE ),
"end_token": [-100] * len(_SCREAMING_SNAKE_CASE ),
"category": category,
},
}
__a : Dict = out['context'].split()
__a : Optional[Any] = splitted_context[answer['end_token']]
__a : Optional[Any] = len(
tokenizer(
' '.join(splitted_context[: answer['start_token']] ) , add_special_tokens=_SCREAMING_SNAKE_CASE , ).input_ids )
__a : Optional[int] = len(
tokenizer(' '.join(splitted_context[: answer['end_token']] ) , add_special_tokens=_SCREAMING_SNAKE_CASE ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__a : Optional[Any] = len(tokenizer(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__a : List[str] = input_ids[answer['start_token'] : answer['end_token'] + 1] # right & left are inclusive
__a : List[Any] = answer['start_token']
__a : Dict = answer['end_token']
if assertion:
__a : List[str] = tokenizer.decode(_SCREAMING_SNAKE_CASE )
if answer["span"] != new:
print('ISSUE IN TOKENIZATION' )
print('OLD:' , answer['span'] )
print('NEW:' , _SCREAMING_SNAKE_CASE , end='\n\n' )
if len(_SCREAMING_SNAKE_CASE ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
__a : Optional[Any] = input_ids[:q_len]
__a : Optional[int] = range(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) , max_length - doc_stride )
__a : Optional[Any] = []
__a : Optional[int] = []
__a : int = []
__a : int = [] # null, yes, no, long, short
for i in doc_start_indices:
__a : Tuple = i + max_length - q_len
__a : Dict = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
__a : Optional[int] = start_token - i + q_len
__a : Dict = end_token - i + q_len
answers_category.append(answer['category'][0] ) # ["short"] -> "short"
else:
__a : int = -100
__a : Tuple = -100
answers_category.append('null' )
__a : Union[str, Any] = inputs[-1][start_token : end_token + 1]
answers_start_token.append(_SCREAMING_SNAKE_CASE )
answers_end_token.append(_SCREAMING_SNAKE_CASE )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('ISSUE in strided for ID:' , example['id'] )
print('New:' , tokenizer.decode(_SCREAMING_SNAKE_CASE ) )
print('Old:' , tokenizer.decode(_SCREAMING_SNAKE_CASE ) , end='\n\n' )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : int=2_048 , _SCREAMING_SNAKE_CASE : Optional[Any]=4_096 , _SCREAMING_SNAKE_CASE : Tuple=False ):
__a : Dict = get_strided_contexts_and_ans(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , doc_stride=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , assertion=_SCREAMING_SNAKE_CASE , )
return example
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Any ):
with jsonlines.open(_SCREAMING_SNAKE_CASE , 'a' ) as writer:
for example in tqdm(_SCREAMING_SNAKE_CASE , total=len(_SCREAMING_SNAKE_CASE ) , desc='Saving samples ... ' ):
__a : Optional[Any] = example['labels']
for ids, start, end, cat in zip(
example['input_ids'] , labels['start_token'] , labels['end_token'] , labels['category'] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'input_ids': ids,
'start_token': start,
'end_token': end,
'category': CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
__lowercase : Union[str, Any] = load_dataset('natural_questions')
__lowercase : int = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
__lowercase : List[str] = data['train' if PROCESS_TRAIN == 'true' else 'validation']
__lowercase : Any = {
'tokenizer': tokenizer,
'doc_stride': DOC_STRIDE,
'max_length': MAX_LENGTH,
'assertion': False,
}
__lowercase : List[str] = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__lowercase : Dict = data.remove_columns(['annotations', 'document', 'id', 'question'])
print(data)
np.random.seed(SEED)
__lowercase : Union[str, Any] = 'nq-training.jsonl' if PROCESS_TRAIN == 'true' else 'nq-validation.jsonl'
save_to_disk(data, file_name=cache_file_name)
| 27 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = "cpu" , lowerCAmelCase_ = None )-> None:
lowerCAmelCase_ : str = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase_ , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowerCAmelCase_ : int = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Tuple = src_path
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
fire.Fire(convert) | 262 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
_lowerCamelCase : Tuple = random.Random()
def __lowerCamelCase ( A__ , A__=1.0 , A__=None , A__=None ) -> Union[str, Any]:
"""simple docstring"""
if rng is None:
UpperCamelCase = global_rng
UpperCamelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any]=7 , UpperCamelCase__ : List[str]=4_0_0 , UpperCamelCase__ : str=2_0_0_0 , UpperCamelCase__ : Any=1 , UpperCamelCase__ : Optional[Any]=0.0 , UpperCamelCase__ : Tuple=1_6_0_0_0 , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : Dict=True , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = min_seq_length
UpperCamelCase = max_seq_length
UpperCamelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase = feature_size
UpperCamelCase = padding_value
UpperCamelCase = sampling_rate
UpperCamelCase = return_attention_mask
UpperCamelCase = do_normalize
def A ( self : Optional[int] ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def A ( self : Union[str, Any] , UpperCamelCase__ : Optional[int]=False , UpperCamelCase__ : Union[str, Any]=False ):
"""simple docstring"""
def _flatten(UpperCamelCase__ : Optional[Any] ):
return list(itertools.chain(*UpperCamelCase__ ) )
if equal_length:
UpperCamelCase = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
UpperCamelCase = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase = [np.asarray(UpperCamelCase__ ) for x in speech_inputs]
return speech_inputs
class SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor
def A ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = WavaVecaFeatureExtractionTester(self )
def A ( self : Optional[Any] , UpperCamelCase__ : List[str] ):
"""simple docstring"""
self.assertTrue(np.all(np.mean(UpperCamelCase__ , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__ , axis=0 ) - 1 ) < 1E-3 ) )
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = [np.asarray(UpperCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
UpperCamelCase = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
UpperCamelCase = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 ) )
# Test batched
UpperCamelCase = feat_extract(UpperCamelCase__ , return_tensors='np' ).input_values
UpperCamelCase = feat_extract(UpperCamelCase__ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
UpperCamelCase = np.asarray(UpperCamelCase__ )
UpperCamelCase = feat_extract(UpperCamelCase__ , return_tensors='np' ).input_values
UpperCamelCase = feat_extract(UpperCamelCase__ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 ) )
def A ( self : Dict ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = ['longest', 'max_length', 'do_not_pad']
UpperCamelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = feat_extract(UpperCamelCase__ , padding=UpperCamelCase__ , max_length=UpperCamelCase__ , return_tensors='np' )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def A ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = range(8_0_0 , 1_4_0_0 , 2_0_0 )
UpperCamelCase = [floats_list((1, x) )[0] for x in lengths]
UpperCamelCase = ['longest', 'max_length', 'do_not_pad']
UpperCamelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = feat_extract(UpperCamelCase__ , max_length=UpperCamelCase__ , padding=UpperCamelCase__ )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def A ( self : str ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = feat_extract(
UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=1_0_0_0 , padding='max_length' , return_tensors='np' )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = feat_extract(
UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=1_0_0_0 , padding='longest' , return_tensors='np' )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1_0_0_0) )
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = feat_extract(
UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=2_0_0_0 , padding='longest' , return_tensors='np' )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1_2_0_0) )
@require_torch
def A ( self : Optional[Any] ):
"""simple docstring"""
import torch
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = np.random.rand(1_0_0 ).astype(np.floataa )
UpperCamelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
UpperCamelCase = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def A ( self : Any ):
"""simple docstring"""
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
UpperCamelCase = WavaVecaConfig.from_pretrained(UpperCamelCase__ )
UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__ )
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == 'layer' )
| 28 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel | 262 | 0 |
__UpperCAmelCase = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.6_0_9_3_4_4,
"knot": 1.8_5_2,
}
__UpperCAmelCase = {
"km/h": 1.0,
"m/s": 0.2_7_7_7_7_7_7_7_8,
"mph": 0.6_2_1_3_7_1_1_9_2,
"knot": 0.5_3_9_9_5_6_8_0_3,
}
def lowercase__ ( __snake_case : float , __snake_case : str , __snake_case : str ):
'''simple docstring'''
if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
UpperCAmelCase_ : Optional[int] = (
F"Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"
F"Valid values are: {', '.join(__snake_case )}"
)
raise ValueError(__snake_case )
return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] , 3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 29 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : str ={
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = """vit_mae"""
def __init__( self , __lowercase=7_6_8 , __lowercase=1_2 , __lowercase=1_2 , __lowercase=3_0_7_2 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1e-12 , __lowercase=2_2_4 , __lowercase=1_6 , __lowercase=3 , __lowercase=True , __lowercase=1_6 , __lowercase=5_1_2 , __lowercase=8 , __lowercase=2_0_4_8 , __lowercase=0.75 , __lowercase=False , **__lowercase , ) -> str:
super().__init__(**__lowercase )
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Any = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : int = intermediate_size
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : int = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : Union[str, Any] = image_size
lowerCAmelCase_ : Optional[int] = patch_size
lowerCAmelCase_ : Tuple = num_channels
lowerCAmelCase_ : List[str] = qkv_bias
lowerCAmelCase_ : List[Any] = decoder_num_attention_heads
lowerCAmelCase_ : int = decoder_hidden_size
lowerCAmelCase_ : Optional[int] = decoder_num_hidden_layers
lowerCAmelCase_ : Tuple = decoder_intermediate_size
lowerCAmelCase_ : Tuple = mask_ratio
lowerCAmelCase_ : Any = norm_pix_loss | 262 | 0 |
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
return number | (1 << position)
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
return number & ~(1 << position)
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
return number ^ (1 << position)
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
return ((number >> position) & 1) == 1
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30 |
def lowerCAmelCase ( lowerCAmelCase_ = 10**9 )-> int:
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[int] = 2
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : str = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
lowerCAmelCase_ : Any = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""") | 262 | 0 |
'''simple docstring'''
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Dict = """#"""
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] ):
_UpperCAmelCase : dict = {}
def _A ( self : int , A : str ):
_UpperCAmelCase : Optional[Any] = self._trie
for char in text:
if char not in trie:
_UpperCAmelCase : Dict = {}
_UpperCAmelCase : Tuple = trie[char]
_UpperCAmelCase : Tuple = True
def _A ( self : Optional[Any] , A : str ):
_UpperCAmelCase : str = self._trie
for char in prefix:
if char in trie:
_UpperCAmelCase : Optional[int] = trie[char]
else:
return []
return self._elements(A )
def _A ( self : str , A : dict ):
_UpperCAmelCase : Tuple = []
for c, v in d.items():
_UpperCAmelCase : List[Any] = [" "] if c == END else [(c + s) for s in self._elements(A )]
result.extend(A )
return tuple(A )
__SCREAMING_SNAKE_CASE : Any = Trie()
__SCREAMING_SNAKE_CASE : Optional[Any] = ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
trie.insert_word(word)
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> tuple:
"""simple docstring"""
_UpperCAmelCase : List[Any] = trie.find_word(_UpperCAmelCase )
return tuple(string + word for word in suffixes )
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 31 |
import inspect
import unittest
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def lowercase_ ( self ) -> List[str]:
import diffusers
from diffusers.dependency_versions_table import deps
lowerCAmelCase_ : Any = inspect.getmembers(__lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowerCAmelCase_ : Optional[int] = '''k-diffusion'''
elif backend == "invisible_watermark":
lowerCAmelCase_ : Dict = '''invisible-watermark'''
assert backend in deps, f"""{backend} is not in the deps table!""" | 262 | 0 |
import datasets
from .evaluate import evaluate
UpperCAmelCase_ : Optional[Any] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
UpperCAmelCase_ : int = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
UpperCAmelCase_ : List[Any] = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': {
'id': datasets.Value('string' ),
'prediction_text': datasets.features.Sequence(datasets.Value('string' ) ),
},
'references': {
'id': datasets.Value('string' ),
'answers': datasets.features.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
},
} ) , codebase_urls=['https://www.atticusprojectai.org/cuad'] , reference_urls=['https://www.atticusprojectai.org/cuad'] , )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple ) -> Union[str, Any]:
a_ : int = {prediction['id']: prediction['prediction_text'] for prediction in predictions}
a_ : List[Any] = [
{
'paragraphs': [
{
'qas': [
{
'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
'id': ref['id'],
}
for ref in references
]
}
]
}
]
a_ : Any = evaluate(dataset=SCREAMING_SNAKE_CASE__ , predictions=SCREAMING_SNAKE_CASE__ )
return score
| 32 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_UpperCAmelCase : Any =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__lowercase , **__lowercase ) -> None:
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __lowercase , )
super().__init__(*__lowercase , **__lowercase ) | 262 | 0 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def lowercase ( __snake_case : np.ndarray , __snake_case : float ):
# For applying gaussian function for each element in matrix.
lowercase_ : Union[str, Any] = math.sqrt(__snake_case )
lowercase_ : Any = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigma) ** 2) * 0.5 )
def lowercase ( __snake_case : np.ndarray , __snake_case : int , __snake_case : int , __snake_case : int ):
lowercase_ : List[str] = kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def lowercase ( __snake_case : int , __snake_case : float ):
# Creates a gaussian kernel of given dimension.
lowercase_ : Tuple = np.zeros((kernel_size, kernel_size) )
for i in range(0 , __snake_case ):
for j in range(0 , __snake_case ):
lowercase_ : List[str] = math.sqrt(
abs(i - kernel_size // 2 ) ** 2 + abs(j - kernel_size // 2 ) ** 2 )
return vec_gaussian(__snake_case , __snake_case )
def lowercase ( __snake_case : np.ndarray , __snake_case : float , __snake_case : float , __snake_case : int , ):
lowercase_ : Tuple = np.zeros(img.shape )
lowercase_ : Union[str, Any] = get_gauss_kernel(__snake_case , __snake_case )
lowercase_ , lowercase_ : List[Any] = img.shape
for i in range(kernel_size // 2 , size_x - kernel_size // 2 ):
for j in range(kernel_size // 2 , size_y - kernel_size // 2 ):
lowercase_ : str = get_slice(__snake_case , __snake_case , __snake_case , __snake_case )
lowercase_ : Any = img_s - img_s[kernel_size // 2, kernel_size // 2]
lowercase_ : List[Any] = vec_gaussian(__snake_case , __snake_case )
lowercase_ : List[Any] = np.multiply(__snake_case , __snake_case )
lowercase_ : Union[str, Any] = np.multiply(__snake_case , __snake_case )
lowercase_ : Any = np.sum(__snake_case ) / np.sum(__snake_case )
lowercase_ : Optional[Any] = val
return imga
def lowercase ( __snake_case : list ):
lowercase_ : Optional[Any] = args[1] if args[1:] else '''../image_data/lena.jpg'''
lowercase_ : Dict = float(args[2] ) if args[2:] else 1.0
lowercase_ : int = float(args[3] ) if args[3:] else 1.0
if args[4:]:
lowercase_ : str = int(args[4] )
lowercase_ : Optional[int] = kernel_size + abs(kernel_size % 2 - 1 )
else:
lowercase_ : Dict = 5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
__A , __A , __A , __A : List[str] = parse_args(sys.argv)
__A : str = cva.imread(filename, 0)
cva.imshow('''input image''', img)
__A : str = img / 255
__A : Any = out.astype('''float32''')
__A : List[Any] = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
__A : Any = out * 255
__A : Optional[int] = np.uinta(out)
cva.imshow('''output image''', out)
cva.waitKey(0)
cva.destroyAllWindows()
| 33 |
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : Optional[int] = set()
# edges = list of graph's edges
lowerCAmelCase_ : List[str] = get_edges(lowerCAmelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = edges.pop()
chosen_vertices.add(lowerCAmelCase_ )
chosen_vertices.add(lowerCAmelCase_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(lowerCAmelCase_ )
return chosen_vertices
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}") | 262 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( __a ):
__a : Optional[Any] = (DEISMultistepScheduler,)
__a : Any = (("""num_inference_steps""", 25),)
def A ( self : Any , **lowercase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = {
'''num_train_timesteps''': 1_000,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
}
config.update(**lowercase )
return config
def A ( self : Union[str, Any] , lowercase : Optional[Any]=0 , **lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config(**lowercase )
UpperCAmelCase = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# copy over dummy past residuals
UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase )
UpperCAmelCase = scheduler_class.from_pretrained(lowercase )
new_scheduler.set_timesteps(lowercase )
# copy over dummy past residuals
UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
UpperCAmelCase , UpperCAmelCase = sample, sample
for t in range(lowercase , time_step + scheduler.config.solver_order + 1 ):
UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
UpperCAmelCase = new_scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : int ):
'''simple docstring'''
pass
def A ( self : str , lowercase : Any=0 , **lowercase : Tuple ):
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config()
UpperCAmelCase = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase )
UpperCAmelCase = scheduler_class.from_pretrained(lowercase )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
UpperCAmelCase = new_scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : Any , lowercase : List[str]=None , **lowercase : List[Any] ):
'''simple docstring'''
if scheduler is None:
UpperCAmelCase = self.scheduler_classes[0]
UpperCAmelCase = self.get_scheduler_config(**lowercase )
UpperCAmelCase = scheduler_class(**lowercase )
UpperCAmelCase = self.scheduler_classes[0]
UpperCAmelCase = self.get_scheduler_config(**lowercase )
UpperCAmelCase = scheduler_class(**lowercase )
UpperCAmelCase = 10
UpperCAmelCase = self.dummy_model()
UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(lowercase )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase = model(lowercase , lowercase )
UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase ).prev_sample
return sample
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config()
UpperCAmelCase = scheduler_class(**lowercase )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase , '''set_timesteps''' ):
scheduler.set_timesteps(lowercase )
elif num_inference_steps is not None and not hasattr(lowercase , '''set_timesteps''' ):
UpperCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
UpperCAmelCase = scheduler.timesteps[5]
UpperCAmelCase = scheduler.timesteps[6]
UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = DEISMultistepScheduler(**self.get_scheduler_config() )
UpperCAmelCase = self.full_loop(scheduler=lowercase )
UpperCAmelCase = torch.mean(torch.abs(lowercase ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase = self.full_loop(scheduler=lowercase )
UpperCAmelCase = torch.mean(torch.abs(lowercase ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
def A ( self : Dict ):
'''simple docstring'''
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=lowercase )
def A ( self : int ):
'''simple docstring'''
self.check_over_configs(thresholding=lowercase )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowercase , prediction_type=lowercase , sample_max_value=lowercase , algorithm_type='''deis''' , solver_order=lowercase , solver_type=lowercase , )
def A ( self : Optional[int] ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase )
def A ( self : Tuple ):
'''simple docstring'''
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowercase , solver_type=lowercase , prediction_type=lowercase , algorithm_type=lowercase , )
UpperCAmelCase = self.full_loop(
solver_order=lowercase , solver_type=lowercase , prediction_type=lowercase , algorithm_type=lowercase , )
assert not torch.isnan(lowercase ).any(), "Samples have nan numbers"
def A ( self : int ):
'''simple docstring'''
self.check_over_configs(lower_order_final=lowercase )
self.check_over_configs(lower_order_final=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=lowercase , time_step=0 )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.full_loop()
UpperCAmelCase = torch.mean(torch.abs(lowercase ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' )
UpperCAmelCase = torch.mean(torch.abs(lowercase ) )
assert abs(result_mean.item() - 0.091 ) < 1E-3
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.scheduler_classes[0]
UpperCAmelCase = self.get_scheduler_config(thresholding=lowercase , dynamic_thresholding_ratio=0 )
UpperCAmelCase = scheduler_class(**lowercase )
UpperCAmelCase = 10
UpperCAmelCase = self.dummy_model()
UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowercase )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase = model(lowercase , lowercase )
UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase ).prev_sample
assert sample.dtype == torch.floataa
| 34 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
lowerCAmelCase_ : Optional[int] = False
for divisor in range(2 , int(round(sqrt(lowerCAmelCase_ ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
lowerCAmelCase_ : Tuple = False
break
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'status' must been from type bool"
return status
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
lowerCAmelCase_ : Tuple = list(range(2 , n + 1 ) )
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(lowerCAmelCase_ ) ):
for j in range(i + 1 , len(lowerCAmelCase_ ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
lowerCAmelCase_ : str = 0
# filters actual prime numbers.
lowerCAmelCase_ : Optional[int] = [x for x in begin_list if x != 0]
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
lowerCAmelCase_ : List[Any] = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(lowerCAmelCase_ ):
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and number >= 0, "'number' must been an int and >= 0"
lowerCAmelCase_ : int = [] # this list will be returns of the function.
# potential prime number factors.
lowerCAmelCase_ : List[Any] = 2
lowerCAmelCase_ : Optional[int] = number
if number == 0 or number == 1:
ans.append(lowerCAmelCase_ )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(lowerCAmelCase_ ):
while quotient != 1:
if is_prime(lowerCAmelCase_ ) and (quotient % factor == 0):
ans.append(lowerCAmelCase_ )
quotient /= factor
else:
factor += 1
else:
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : Dict = 0
# prime factorization of 'number'
lowerCAmelCase_ : Any = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = max(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : List[Any] = 0
# prime factorization of 'number'
lowerCAmelCase_ : Dict = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = min(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 == 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 == 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 != 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 != 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[str]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (number > 2) and is_even(lowerCAmelCase_ )
), "'number' must been an int, even and > 2"
lowerCAmelCase_ : str = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
lowerCAmelCase_ : int = get_prime_numbers(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = len(lowerCAmelCase_ )
# run variable for while-loops.
lowerCAmelCase_ : Union[str, Any] = 0
lowerCAmelCase_ : Tuple = None
# exit variable. for break up the loops
lowerCAmelCase_ : int = True
while i < len_pn and loop:
lowerCAmelCase_ : int = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
lowerCAmelCase_ : Tuple = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (len(lowerCAmelCase_ ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : int = 0
while numbera != 0:
lowerCAmelCase_ : str = numbera % numbera
lowerCAmelCase_ : List[Any] = numbera
lowerCAmelCase_ : Any = rest
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : List[Any] = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
elif numbera == 1 or numbera == 1:
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : Union[str, Any] = max(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Dict = 0
lowerCAmelCase_ : Union[str, Any] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(max(lowerCAmelCase_ , lowerCAmelCase_ ) ):
ans *= n
else:
lowerCAmelCase_ : List[str] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'number' must been a positive int"
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : Optional[int] = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(lowerCAmelCase_ ):
ans += 1
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and is_prime(
lowerCAmelCase_ ), "'ans' must been a prime number and from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
assert (
is_prime(lowerCAmelCase_ ) and is_prime(lowerCAmelCase_ ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
lowerCAmelCase_ : Union[str, Any] = p_number_a + 1 # jump to the next number
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
while number < p_number_a:
ans.append(lowerCAmelCase_ )
number += 1
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and ans[0] != p_number_a
and ans[len(lowerCAmelCase_ ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 1), "'n' must been int and >= 1"
lowerCAmelCase_ : List[Any] = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(lowerCAmelCase_ )
# precondition
assert ans[0] == 1 and ans[len(lowerCAmelCase_ ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number > 1
), "'number' must been an int and >= 1"
lowerCAmelCase_ : Union[str, Any] = get_divisors(lowerCAmelCase_ )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (divisors[0] == 1)
and (divisors[len(lowerCAmelCase_ ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
lowerCAmelCase_ : Optional[Any] = gcd(abs(lowerCAmelCase_ ) , abs(lowerCAmelCase_ ) )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been a int and >= 0"
lowerCAmelCase_ : Any = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been an int and >= 0"
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Union[str, Any] = 1 # this will be return
for _ in range(n - 1 ):
lowerCAmelCase_ : Union[str, Any] = ans
ans += fiba
lowerCAmelCase_ : Optional[Any] = tmp
return ans | 262 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def __snake_case( ) -> Generator[int, None, None]:
snake_case__ : dict[int, int] = {}
snake_case__ : Any = 2
while True:
snake_case__ : int = factor_map.pop(_lowerCAmelCase , _lowerCAmelCase )
if factor:
snake_case__ : Any = factor + prime
while x in factor_map:
x += factor
snake_case__ : Any = factor
else:
snake_case__ : Dict = prime
yield prime
prime += 1
def __snake_case( _lowerCAmelCase = 1e10 ) -> int:
snake_case__ : Tuple = sieve()
snake_case__ : List[Any] = 1
while True:
snake_case__ : Tuple = next(_lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(_lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 35 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
_UpperCAmelCase : Union[str, Any] ="""pt"""
elif is_tf_available():
_UpperCAmelCase : List[Any] ="""tf"""
else:
_UpperCAmelCase : Optional[int] ="""jax"""
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = PerceiverTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def lowercase_ ( self ) -> Optional[int]:
super().setUp()
lowerCAmelCase_ : str = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase_ ( self ) -> Any:
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def lowercase_ ( self , **__lowercase ) -> PerceiverTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowercase )
def lowercase_ ( self , __lowercase , __lowercase=False , __lowercase=2_0 , __lowercase=5 ) -> Tuple[str, list]:
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for Perceiver 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.
lowerCAmelCase_ : Optional[Any] = []
for i in range(len(__lowercase ) ):
try:
lowerCAmelCase_ : List[str] = tokenizer.decode([i] , clean_up_tokenization_spaces=__lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCAmelCase_ : List[str] = list(filter(lambda __lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , __lowercase ) )
lowerCAmelCase_ : Optional[int] = list(filter(lambda __lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=__lowercase ) , __lowercase ) )
if max_length is not None and len(__lowercase ) > max_length:
lowerCAmelCase_ : Union[str, Any] = toks[:max_length]
if min_length is not None and len(__lowercase ) < min_length and len(__lowercase ) > 0:
while len(__lowercase ) < min_length:
lowerCAmelCase_ : Union[str, Any] = toks + toks
# toks_str = [t[1] for t in toks]
lowerCAmelCase_ : List[str] = [t[0] for t in toks]
# Ensure consistency
lowerCAmelCase_ : int = tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
if " " not in output_txt and len(__lowercase ) > 1:
lowerCAmelCase_ : Optional[int] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=__lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=__lowercase )
)
if with_prefix_space:
lowerCAmelCase_ : Any = ''' ''' + output_txt
lowerCAmelCase_ : List[str] = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
return output_txt, output_ids
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : List[str] = self.perceiver_tokenizer
lowerCAmelCase_ : Any = '''Unicode €.'''
lowerCAmelCase_ : Dict = tokenizer(__lowercase )
lowerCAmelCase_ : Any = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['''input_ids'''] , __lowercase )
# decoding
lowerCAmelCase_ : str = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , '''[CLS]Unicode €.[SEP]''' )
lowerCAmelCase_ : Optional[int] = tokenizer('''e è é ê ë''' )
lowerCAmelCase_ : str = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['''input_ids'''] , __lowercase )
# decoding
lowerCAmelCase_ : int = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.perceiver_tokenizer
lowerCAmelCase_ : Dict = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
lowerCAmelCase_ : Optional[int] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCAmelCase_ : Optional[int] = tokenizer(__lowercase , padding=__lowercase , return_tensors=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
if FRAMEWORK != "jax":
lowerCAmelCase_ : str = list(batch.input_ids.numpy()[0] )
else:
lowerCAmelCase_ : Union[str, Any] = list(batch.input_ids.tolist()[0] )
self.assertListEqual(__lowercase , __lowercase )
self.assertEqual((2, 3_8) , batch.input_ids.shape )
self.assertEqual((2, 3_8) , batch.attention_mask.shape )
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : int = self.perceiver_tokenizer
lowerCAmelCase_ : Optional[Any] = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowerCAmelCase_ : List[Any] = tokenizer(__lowercase , padding=__lowercase , return_tensors=__lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , __lowercase )
self.assertIn('''attention_mask''' , __lowercase )
self.assertNotIn('''decoder_input_ids''' , __lowercase )
self.assertNotIn('''decoder_attention_mask''' , __lowercase )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = self.perceiver_tokenizer
lowerCAmelCase_ : int = [
'''Summary of the text.''',
'''Another summary.''',
]
lowerCAmelCase_ : List[str] = tokenizer(
text_target=__lowercase , max_length=3_2 , padding='''max_length''' , truncation=__lowercase , return_tensors=__lowercase )
self.assertEqual(3_2 , targets['''input_ids'''].shape[1] )
def lowercase_ ( self ) -> Optional[Any]:
# safety check on max_len default value so we are sure the test works
lowerCAmelCase_ : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
lowerCAmelCase_ : Optional[int] = 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
lowerCAmelCase_ : Union[str, Any] = tempfile.mkdtemp()
lowerCAmelCase_ : str = ''' He is very happy, UNwant\u00E9d,running'''
lowerCAmelCase_ : Optional[int] = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Any = tokenizer.__class__.from_pretrained(__lowercase )
lowerCAmelCase_ : Tuple = after_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
shutil.rmtree(__lowercase )
lowerCAmelCase_ : Optional[int] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCAmelCase_ : Optional[int] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
lowerCAmelCase_ : Any = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
lowerCAmelCase_ : str = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : str = tokenizer.__class__.from_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = after_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
lowerCAmelCase_ : str = tokenizer.__class__.from_pretrained(__lowercase , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(__lowercase )
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : List[Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__lowercase )
with open(os.path.join(__lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
lowerCAmelCase_ : Tuple = json.load(__lowercase )
with open(os.path.join(__lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
lowerCAmelCase_ : Any = json.load(__lowercase )
lowerCAmelCase_ : Optional[int] = [f"""<extra_id_{i}>""" for i in range(1_2_5 )]
lowerCAmelCase_ : Optional[Any] = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
lowerCAmelCase_ : Any = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(__lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(__lowercase , __lowercase )
with open(os.path.join(__lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(__lowercase , __lowercase )
# 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
lowerCAmelCase_ : int = tokenizer_class.from_pretrained(
__lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
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
lowerCAmelCase_ : Tuple = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=__lowercase )]
lowerCAmelCase_ : Dict = tokenizer_class.from_pretrained(
__lowercase , additional_special_tokens=__lowercase , )
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 lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ) , '''�''' )
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> Any:
pass
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> List[str]:
pass
def lowercase_ ( self ) -> Dict:
# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
# strings and special added tokens as tokens
lowerCAmelCase_ : Tuple = self.get_tokenizers(fast=__lowercase , do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
lowerCAmelCase_ : List[str] = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
lowerCAmelCase_ : Optional[int] = tokenizer.convert_tokens_to_string(__lowercase )
self.assertIsInstance(__lowercase , __lowercase ) | 262 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def A ( _lowerCamelCase ):
'''simple docstring'''
if (
(cp >= 0X4E_00 and cp <= 0X9F_FF)
or (cp >= 0X34_00 and cp <= 0X4D_BF) #
or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF) #
or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F) #
or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F) #
or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF) #
or (cp >= 0XF9_00 and cp <= 0XFA_FF)
or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F) #
): #
return True
return False
def A ( _lowerCamelCase ):
'''simple docstring'''
for char in word:
_lowerCAmelCase : int = ord(_lowerCamelCase )
if not _is_chinese_char(_lowerCamelCase ):
return 0
return 1
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = set()
for token in tokens:
_lowerCAmelCase : Optional[Any] = len(_lowerCamelCase ) > 1 and is_chinese(_lowerCamelCase )
if chinese_word:
word_set.add(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = list(_lowerCamelCase )
return word_list
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
_lowerCAmelCase : Any = max([len(_lowerCamelCase ) for w in chinese_word_set] )
_lowerCAmelCase : Tuple = bert_tokens
_lowerCAmelCase , _lowerCAmelCase : List[str] = 0, len(_lowerCamelCase )
while start < end:
_lowerCAmelCase : List[str] = True
if is_chinese(bert_word[start] ):
_lowerCAmelCase : Optional[Any] = min(end - start , _lowerCamelCase )
for i in range(_lowerCamelCase , 1 , -1 ):
_lowerCAmelCase : Tuple = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
_lowerCAmelCase : Optional[int] = "##" + bert_word[j]
_lowerCAmelCase : Dict = start + i
_lowerCAmelCase : Optional[Any] = False
break
if single_word:
start += 1
return bert_word
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = []
for i in range(0 , len(_lowerCamelCase ) , 100 ):
_lowerCAmelCase : List[Any] = ltp_tokenizer.seg(lines[i : i + 100] )[0]
_lowerCAmelCase : Tuple = [get_chinese_word(_lowerCamelCase ) for r in res]
ltp_res.extend(_lowerCamelCase )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
_lowerCAmelCase : str = []
for i in range(0 , len(_lowerCamelCase ) , 100 ):
_lowerCAmelCase : List[Any] = bert_tokenizer(lines[i : i + 100] , add_special_tokens=_lowerCamelCase , truncation=_lowerCamelCase , max_length=512 )
bert_res.extend(res["input_ids"] )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
_lowerCAmelCase : Any = []
for input_ids, chinese_word in zip(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = []
for id in input_ids:
_lowerCAmelCase : List[Any] = bert_tokenizer._convert_id_to_token(_lowerCamelCase )
input_tokens.append(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = add_sub_symbol(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : List[str] = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(_lowerCamelCase ):
if token[:2] == "##":
_lowerCAmelCase : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(_lowerCamelCase ) == 1 and _is_chinese_char(ord(_lowerCamelCase ) ):
ref_id.append(_lowerCamelCase )
ref_ids.append(_lowerCamelCase )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
return ref_ids
def A ( _lowerCamelCase ):
'''simple docstring'''
with open(args.file_name , "r" , encoding="utf-8" ) as f:
_lowerCAmelCase : List[str] = f.readlines()
_lowerCAmelCase : int = [line.strip() for line in data if len(_lowerCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_lowerCAmelCase : Dict = LTP(args.ltp ) # faster in GPU device
_lowerCAmelCase : int = BertTokenizer.from_pretrained(args.bert )
_lowerCAmelCase : Optional[Any] = prepare_ref(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
with open(args.save_path , "w" , encoding="utf-8" ) as f:
_lowerCAmelCase : Union[str, Any] = [json.dumps(_lowerCamelCase ) + "\n" for ref in ref_ids]
f.writelines(_lowerCamelCase )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
_snake_case = parser.parse_args()
main(args)
| 36 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class snake_case__:
'''simple docstring'''
@staticmethod
def lowercase_ ( *__lowercase , **__lowercase ) -> Union[str, Any]:
pass
@is_pipeline_test
@require_vision
@require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase_ ( self , __lowercase , __lowercase , __lowercase ) -> List[str]:
lowerCAmelCase_ : Union[str, Any] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCAmelCase_ : str = [
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase_ ( self , __lowercase , __lowercase ) -> str:
lowerCAmelCase_ : Tuple = object_detector(examples[0] , threshold=0.0 )
lowerCAmelCase_ : Dict = len(__lowercase )
self.assertGreater(__lowercase , 0 )
self.assertEqual(
__lowercase , [
{
'''score''': ANY(__lowercase ),
'''label''': ANY(__lowercase ),
'''box''': {'''xmin''': ANY(__lowercase ), '''ymin''': ANY(__lowercase ), '''xmax''': ANY(__lowercase ), '''ymax''': ANY(__lowercase )},
}
for i in range(__lowercase )
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase_ ( self ) -> List[str]:
pass
@require_torch
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Union[str, Any] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCAmelCase_ : Union[str, Any] = object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
] , )
lowerCAmelCase_ : Union[str, Any] = object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
[
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
]
] , )
@require_torch
@slow
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Any = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Dict = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
] , )
lowerCAmelCase_ : Tuple = object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
] , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase_ ( self ) -> List[str]:
pass
@require_torch
@slow
def lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Any = 0.2
lowerCAmelCase_ : List[Any] = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Optional[Any] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=__lowercase , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
] , )
@require_torch
@slow
def lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Dict = 2
lowerCAmelCase_ : Union[str, Any] = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Optional[Any] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , top_k=__lowercase , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
] , ) | 262 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if depth < 0:
raise ValueError("""Depth cannot be less than 0""" )
if not scores:
raise ValueError("""Scores cannot be empty""" )
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1 , node_index * 2 , UpperCamelCase , UpperCamelCase , UpperCamelCase ) , minimax(depth + 1 , node_index * 2 + 1 , UpperCamelCase , UpperCamelCase , UpperCamelCase ) , )
if is_max
else min(
minimax(depth + 1 , node_index * 2 , UpperCamelCase , UpperCamelCase , UpperCamelCase ) , minimax(depth + 1 , node_index * 2 + 1 , UpperCamelCase , UpperCamelCase , UpperCamelCase ) , )
)
def _SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = [90, 23, 6, 33, 21, 65, 123, 34423]
lowerCAmelCase__ : Optional[int] = math.log(len(UpperCamelCase ) , 2 )
print(f"""Optimal value : {minimax(0 , 0 , UpperCamelCase , UpperCamelCase , UpperCamelCase )}""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 37 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels | 262 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase_ : Tuple = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
UpperCAmelCase_ : List[str] = [0, 25, 50]
UpperCAmelCase_ : Dict = [25, 50, 75]
UpperCAmelCase_ : Tuple = fuzz.membership.trimf(X, abca)
UpperCAmelCase_ : str = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
UpperCAmelCase_ : Tuple = np.ones(75)
UpperCAmelCase_ : Optional[int] = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
UpperCAmelCase_ : Tuple = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
UpperCAmelCase_ : str = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
UpperCAmelCase_ : int = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
UpperCAmelCase_ : int = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
UpperCAmelCase_ : Union[str, Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
UpperCAmelCase_ : Optional[int] = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
UpperCAmelCase_ : int = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
UpperCAmelCase_ : Tuple = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('''Young''')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('''Middle aged''')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('''union''')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('''intersection''')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('''complement_a''')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('''difference a/b''')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('''alg_sum''')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('''alg_product''')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('''bdd_sum''')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('''bdd_difference''')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 38 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Dict ={
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = """ernie_m"""
SCREAMING_SNAKE_CASE__ : Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __lowercase = 2_5_0_0_0_2 , __lowercase = 7_6_8 , __lowercase = 1_2 , __lowercase = 1_2 , __lowercase = 3_0_7_2 , __lowercase = "gelu" , __lowercase = 0.1 , __lowercase = 0.1 , __lowercase = 5_1_4 , __lowercase = 0.02 , __lowercase = 1 , __lowercase = 1e-05 , __lowercase=None , __lowercase=False , __lowercase=0.0 , **__lowercase , ) -> Tuple:
super().__init__(pad_token_id=__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = vocab_size
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Tuple = num_hidden_layers
lowerCAmelCase_ : int = num_attention_heads
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : int = hidden_act
lowerCAmelCase_ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = max_position_embeddings
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : List[Any] = classifier_dropout
lowerCAmelCase_ : Any = is_decoder
lowerCAmelCase_ : List[Any] = act_dropout | 262 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )-> float | int:
"""simple docstring"""
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
_UpperCAmelCase = cst_fwd.get(__lowerCAmelCase , np.inf )
_UpperCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
_UpperCAmelCase = new_cost_f
_UpperCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
_UpperCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> int:
"""simple docstring"""
_UpperCAmelCase = -1
_UpperCAmelCase = set()
_UpperCAmelCase = set()
_UpperCAmelCase = {source: 0}
_UpperCAmelCase = {destination: 0}
_UpperCAmelCase = {source: None}
_UpperCAmelCase = {destination: None}
_UpperCAmelCase = PriorityQueue()
_UpperCAmelCase = PriorityQueue()
_UpperCAmelCase = 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():
_UpperCAmelCase , _UpperCAmelCase = queue_forward.get()
visited_forward.add(__lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = queue_backward.get()
visited_backward.add(__lowerCAmelCase )
_UpperCAmelCase = pass_and_relaxation(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
_UpperCAmelCase = pass_and_relaxation(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
_UpperCAmelCase = shortest_distance
return shortest_path_distance
_a = {
'''B''': [['''C''', 1]],
'''C''': [['''D''', 1]],
'''D''': [['''F''', 1]],
'''E''': [['''B''', 1], ['''G''', 2]],
'''F''': [],
'''G''': [['''F''', 1]],
}
_a = {
'''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()
| 39 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase=7 , __lowercase=3 , __lowercase=1_8 , __lowercase=3_0 , __lowercase=4_0_0 , __lowercase=True , __lowercase=None , __lowercase=True , __lowercase=None , ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = size if size is not None else {'''shortest_edge''': 2_0}
lowerCAmelCase_ : Any = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
lowerCAmelCase_ : Any = parent
lowerCAmelCase_ : Any = batch_size
lowerCAmelCase_ : Optional[int] = num_channels
lowerCAmelCase_ : Tuple = image_size
lowerCAmelCase_ : List[str] = min_resolution
lowerCAmelCase_ : Dict = max_resolution
lowerCAmelCase_ : Tuple = do_resize
lowerCAmelCase_ : Optional[Any] = size
lowerCAmelCase_ : Union[str, Any] = do_center_crop
lowerCAmelCase_ : Optional[Any] = crop_size
def lowercase_ ( self ) -> Union[str, Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = MobileNetVaImageProcessor if is_vision_available() else None
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Union[str, Any] = MobileNetVaImageProcessingTester(self )
@property
def lowercase_ ( self ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowercase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowercase , '''size''' ) )
self.assertTrue(hasattr(__lowercase , '''do_center_crop''' ) )
self.assertTrue(hasattr(__lowercase , '''crop_size''' ) )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 2_0} )
self.assertEqual(image_processor.crop_size , {'''height''': 1_8, '''width''': 1_8} )
lowerCAmelCase_ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2} )
self.assertEqual(image_processor.crop_size , {'''height''': 8_4, '''width''': 8_4} )
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> Union[str, Any]:
# Initialize image_processing
lowerCAmelCase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , Image.Image )
# Test not batched input
lowerCAmelCase_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Tuple = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self ) -> Optional[int]:
# Initialize image_processing
lowerCAmelCase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , np.ndarray )
# Test not batched input
lowerCAmelCase_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Tuple = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self ) -> Any:
# Initialize image_processing
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , torch.Tensor )
# Test not batched input
lowerCAmelCase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Dict = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , ) | 262 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__lowercase = datasets.utils.logging.get_logger(__name__)
class _A ( folder_based_builder.FolderBasedBuilderConfig ):
"""simple docstring"""
UpperCAmelCase : bool = None
UpperCAmelCase : bool = None
class _A ( folder_based_builder.FolderBasedBuilder ):
"""simple docstring"""
UpperCAmelCase : Any = datasets.Audio()
UpperCAmelCase : str = """audio"""
UpperCAmelCase : Optional[int] = AudioFolderConfig
UpperCAmelCase : List[str] # definition at the bottom of the script
UpperCAmelCase : str = AudioClassification(audio_column="""audio""" ,label_column="""label""" )
__lowercase = [
""".aiff""",
""".au""",
""".avr""",
""".caf""",
""".flac""",
""".htk""",
""".svx""",
""".mat4""",
""".mat5""",
""".mpc2k""",
""".ogg""",
""".paf""",
""".pvf""",
""".raw""",
""".rf64""",
""".sd2""",
""".sds""",
""".ircam""",
""".voc""",
""".w64""",
""".wav""",
""".nist""",
""".wavex""",
""".wve""",
""".xi""",
""".mp3""",
""".opus""",
]
__lowercase = AUDIO_EXTENSIONS
| 40 |
from __future__ import annotations
import math
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> None:
lowerCAmelCase_ : str = size
# approximate the overall size of segment tree with given value
lowerCAmelCase_ : Dict = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
lowerCAmelCase_ : Dict = [0 for i in range(0 , 4 * size )]
lowerCAmelCase_ : Optional[int] = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowercase_ ( self , __lowercase ) -> int:
return idx * 2
def lowercase_ ( self , __lowercase ) -> int:
return idx * 2 + 1
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase ) -> None:
if left_element == right_element:
lowerCAmelCase_ : Tuple = a[left_element - 1]
else:
lowerCAmelCase_ : int = (left_element + right_element) // 2
self.build(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase )
self.build(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase )
lowerCAmelCase_ : Any = max(
self.segment_tree[self.left(__lowercase )] , self.segment_tree[self.right(__lowercase )] )
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> bool:
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Union[str, Any] = False
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Any = self.lazy[idx]
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : Optional[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
lowerCAmelCase_ : Dict = val
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = val
lowerCAmelCase_ : List[Any] = val
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : List[str] = True
return True
lowerCAmelCase_ : Optional[Any] = (left_element + right_element) // 2
self.update(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
self.update(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase , __lowercase , __lowercase )
lowerCAmelCase_ : int = max(
self.segment_tree[self.left(__lowercase )] , self.segment_tree[self.right(__lowercase )] )
return True
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> int | float:
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Optional[Any] = False
if left_element != right_element:
lowerCAmelCase_ : List[Any] = self.lazy[idx]
lowerCAmelCase_ : Dict = self.lazy[idx]
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : Optional[int] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
lowerCAmelCase_ : List[Any] = (left_element + right_element) // 2
lowerCAmelCase_ : Tuple = self.query(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase , __lowercase )
lowerCAmelCase_ : List[Any] = self.query(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase , __lowercase )
return max(__lowercase , __lowercase )
def __str__( self ) -> str:
return str([self.query(1 , 1 , self.size , __lowercase , __lowercase ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
_UpperCAmelCase : str =[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
_UpperCAmelCase : List[str] =15
_UpperCAmelCase : Any =SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 262 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
def __init__( self: List[Any] , UpperCamelCase__: Optional[Any] , UpperCamelCase__: int=3 , UpperCamelCase__: Union[str, Any]=32 , UpperCamelCase__: Any=3 , UpperCamelCase__: Optional[int]=10 , UpperCamelCase__: List[str]=[10, 20, 30, 40] , UpperCamelCase__: Tuple=[1, 1, 2, 1] , UpperCamelCase__: Union[str, Any]=True , UpperCamelCase__: List[str]=True , UpperCamelCase__: str="relu" , UpperCamelCase__: Any=3 , UpperCamelCase__: Optional[int]=None , ):
lowerCamelCase__ : List[str] = parent
lowerCamelCase__ : str = batch_size
lowerCamelCase__ : str = image_size
lowerCamelCase__ : List[str] = num_channels
lowerCamelCase__ : List[str] = embeddings_size
lowerCamelCase__ : Dict = hidden_sizes
lowerCamelCase__ : Optional[Any] = depths
lowerCamelCase__ : Dict = is_training
lowerCamelCase__ : str = use_labels
lowerCamelCase__ : Any = hidden_act
lowerCamelCase__ : str = num_labels
lowerCamelCase__ : List[str] = scope
lowerCamelCase__ : Optional[int] = len(UpperCamelCase__ )
def lowerCamelCase_ ( self: str ):
lowerCamelCase__ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ : int = None
if self.use_labels:
lowerCamelCase__ : Dict = ids_tensor([self.batch_size] , self.num_labels )
lowerCamelCase__ : str = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self: Any ):
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def lowerCamelCase_ ( self: Union[str, Any] , UpperCamelCase__: str , UpperCamelCase__: str , UpperCamelCase__: int ):
lowerCamelCase__ : Union[str, Any] = TFResNetModel(config=UpperCamelCase__ )
lowerCamelCase__ : int = model(UpperCamelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self: Any , UpperCamelCase__: List[Any] , UpperCamelCase__: List[str] , UpperCamelCase__: int ):
lowerCamelCase__ : Union[str, Any] = self.num_labels
lowerCamelCase__ : Dict = TFResNetForImageClassification(UpperCamelCase__ )
lowerCamelCase__ : int = model(UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ : Union[str, Any] = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = config_and_inputs
lowerCamelCase__ : Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( _lowercase , _lowercase , unittest.TestCase ):
a = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
a = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
a = False
a = False
a = False
a = False
a = False
def lowerCamelCase_ ( self: Any ):
lowerCamelCase__ : Union[str, Any] = TFResNetModelTester(self )
lowerCamelCase__ : Any = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def lowerCamelCase_ ( self: Optional[int] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self: int ):
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
def lowerCamelCase_ ( self: Optional[int] ):
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
def lowerCamelCase_ ( self: Union[str, Any] ):
pass
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : Optional[Any] = model_class(UpperCamelCase__ )
lowerCamelCase__ : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ : Union[str, Any] = [*signature.parameters.keys()]
lowerCamelCase__ : Dict = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def lowerCamelCase_ ( self: int ):
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowerCamelCase_ ( self: Union[str, Any] ):
def check_hidden_states_output(UpperCamelCase__: Optional[Any] , UpperCamelCase__: Optional[int] , UpperCamelCase__: int ):
lowerCamelCase__ : List[str] = model_class(UpperCamelCase__ )
lowerCamelCase__ : Tuple = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
lowerCamelCase__ : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase__ : Optional[int] = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase__ ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Tuple = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowerCamelCase__ : Tuple = layer_type
lowerCamelCase__ : Optional[Any] = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase__ : Union[str, Any] = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( self: List[Any] ):
lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@slow
def lowerCamelCase_ ( self: List[Any] ):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ : List[str] = TFResNetModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ () -> Union[str, Any]:
lowerCamelCase__ : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self: int ):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ : List[str] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCamelCase__ : Union[str, Any] = self.default_image_processor
lowerCamelCase__ : Optional[Any] = prepare_img()
lowerCamelCase__ : Optional[Any] = image_processor(images=UpperCamelCase__ , return_tensors="""tf""" )
# forward pass
lowerCamelCase__ : Optional[int] = model(**UpperCamelCase__ )
# verify the logits
lowerCamelCase__ : List[Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
lowerCamelCase__ : Tuple = tf.constant([-11.1_069, -9.7_877, -8.3_777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , UpperCamelCase__ , atol=1e-4 ) )
| 41 |
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
_UpperCAmelCase : Optional[int] ="""src/transformers"""
_UpperCAmelCase : str ="""docs/source/en"""
_UpperCAmelCase : Optional[int] ="""."""
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
with open(lowerCAmelCase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCAmelCase_ : int = f.readlines()
# Find the start prompt.
lowerCAmelCase_ : List[Any] = 0
while not lines[start_index].startswith(lowerCAmelCase_ ):
start_index += 1
start_index += 1
lowerCAmelCase_ : List[str] = start_index
while not lines[end_index].startswith(lowerCAmelCase_ ):
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 |
_UpperCAmelCase : Optional[Any] ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase : Optional[int] =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase : Dict =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.
_UpperCAmelCase : Optional[Any] =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase : Optional[int] =direct_transformers_import(TRANSFORMERS_PATH)
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : str = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , lowerCAmelCase_ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : Tuple = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_ )
lowerCAmelCase_ : int = (width - text_length) // 2
lowerCAmelCase_ : Union[str, Any] = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCAmelCase ( )-> str:
lowerCAmelCase_ : Any = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowerCAmelCase_ : Dict = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowerCAmelCase_ : List[Any] = {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.
lowerCAmelCase_ : Tuple = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[Any] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowerCAmelCase_ ):
lowerCAmelCase_ : Optional[int] = None
if attr_name.endswith('''Tokenizer''' ):
lowerCAmelCase_ : Union[str, Any] = slow_tokenizers
lowerCAmelCase_ : List[str] = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
lowerCAmelCase_ : int = fast_tokenizers
lowerCAmelCase_ : Union[str, Any] = attr_name[:-13]
elif _re_tf_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = tf_models
lowerCAmelCase_ : str = _re_tf_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_flax_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = flax_models
lowerCAmelCase_ : Union[str, Any] = _re_flax_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_pt_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Any = pt_models
lowerCAmelCase_ : List[Any] = _re_pt_models.match(lowerCAmelCase_ ).groups()[0]
if lookup_dict is not None:
while len(lowerCAmelCase_ ) > 0:
if attr_name in model_name_to_prefix.values():
lowerCAmelCase_ : Union[str, Any] = True
break
# Try again after removing the last word in the name
lowerCAmelCase_ : Any = ''''''.join(camel_case_split(lowerCAmelCase_ )[:-1] )
# Let's build that table!
lowerCAmelCase_ : int = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowerCAmelCase_ : Tuple = ['''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).
lowerCAmelCase_ : Union[str, Any] = [len(lowerCAmelCase_ ) + 2 for c in columns]
lowerCAmelCase_ : Optional[Any] = max([len(lowerCAmelCase_ ) for name in model_names] ) + 2
# Build the table per se
lowerCAmelCase_ : Dict = '''|''' + '''|'''.join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for c, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
lowerCAmelCase_ : List[str] = {True: '''✅''', False: '''❌'''}
for name in model_names:
lowerCAmelCase_ : List[Any] = model_name_to_prefix[name]
lowerCAmelCase_ : Union[str, Any] = [
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(lowerCAmelCase_ , lowerCAmelCase_ ) for l, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + "|\n"
return table
def lowerCAmelCase ( lowerCAmelCase_=False )-> Tuple:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = _find_text_in_file(
filename=os.path.join(lowerCAmelCase_ , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
lowerCAmelCase_ : Tuple = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowerCAmelCase_ , '''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__":
_UpperCAmelCase : List[Any] =argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_UpperCAmelCase : Tuple =parser.parse_args()
check_model_table(args.fix_and_overwrite) | 262 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCAmelCase ( _lowerCamelCase , unittest.TestCase ):
__lowercase = """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""
def lowerCamelCase ( self , lowerCAmelCase_=0 ):
"""simple docstring"""
_snake_case = floats_tensor((1, 3, 1_28, 1_28) , rng=random.Random(lowerCAmelCase_ ) )
_snake_case = np.random.RandomState(lowerCAmelCase_ )
_snake_case = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.75,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = self.get_dummy_inputs()
_snake_case = pipe(**lowerCAmelCase_ ).images
_snake_case = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1_28, 1_28, 3)
_snake_case = np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
_snake_case = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = self.get_dummy_inputs()
_snake_case = pipe(**lowerCAmelCase_ ).images
_snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_snake_case = np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
_snake_case = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
# warmup pass to apply optimizations
_snake_case = pipe(**self.get_dummy_inputs() )
_snake_case = self.get_dummy_inputs()
_snake_case = pipe(**lowerCAmelCase_ ).images
_snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_snake_case = np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
_snake_case = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = self.get_dummy_inputs()
_snake_case = pipe(**lowerCAmelCase_ ).images
_snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_snake_case = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
_snake_case = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = self.get_dummy_inputs()
_snake_case = pipe(**lowerCAmelCase_ ).images
_snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_snake_case = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
_snake_case = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = self.get_dummy_inputs()
_snake_case = pipe(**lowerCAmelCase_ ).images
_snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_snake_case = np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCAmelCase ( unittest.TestCase ):
@property
def lowerCamelCase ( self ):
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = ort.SessionOptions()
_snake_case = False
return options
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
_snake_case = init_image.resize((7_68, 5_12) )
# using the PNDM scheduler by default
_snake_case = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=lowerCAmelCase_ , feature_extractor=lowerCAmelCase_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = 'A fantasy landscape, trending on artstation'
_snake_case = np.random.RandomState(0 )
_snake_case = pipe(
prompt=lowerCAmelCase_ , image=lowerCAmelCase_ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCAmelCase_ , output_type='np' , )
_snake_case = output.images
_snake_case = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
_snake_case = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
_snake_case = init_image.resize((7_68, 5_12) )
_snake_case = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
_snake_case = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCAmelCase_ , safety_checker=lowerCAmelCase_ , feature_extractor=lowerCAmelCase_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_snake_case = 'A fantasy landscape, trending on artstation'
_snake_case = np.random.RandomState(0 )
_snake_case = pipe(
prompt=lowerCAmelCase_ , image=lowerCAmelCase_ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCAmelCase_ , output_type='np' , )
_snake_case = output.images
_snake_case = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
_snake_case = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 42 |
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 ( )-> int:
lowerCAmelCase_ : int = ArgumentParser('''Transformers CLI tool''' , usage='''transformers-cli <command> [<args>]''' )
lowerCAmelCase_ : Dict = parser.add_subparsers(help='''transformers-cli command helpers''' )
# Register commands
ConvertCommand.register_subcommand(lowerCAmelCase_ )
DownloadCommand.register_subcommand(lowerCAmelCase_ )
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
RunCommand.register_subcommand(lowerCAmelCase_ )
ServeCommand.register_subcommand(lowerCAmelCase_ )
UserCommands.register_subcommand(lowerCAmelCase_ )
AddNewModelCommand.register_subcommand(lowerCAmelCase_ )
AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_ )
LfsCommands.register_subcommand(lowerCAmelCase_ )
PTtoTFCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
lowerCAmelCase_ : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , '''func''' ):
parser.print_help()
exit(1 )
# Run
lowerCAmelCase_ : List[Any] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main() | 262 | 0 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> List[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
__UpperCamelCase :Dict = FlaxDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__lowercase , cache_dir=__lowercase)
__UpperCamelCase :int = [t[-1] for t in os.walk(os.path.join(__lowercase , os.listdir(__lowercase)[0] , '''snapshots'''))]
__UpperCamelCase :Tuple = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('''.bin''') for f in files)
@slow
@require_flax
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase , __UpperCamelCase :Tuple = FlaxStableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__lowercase)
__UpperCamelCase :Optional[int] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__UpperCamelCase :Optional[Any] = jax.random.PRNGKey(0)
__UpperCamelCase :str = 4
__UpperCamelCase :Union[str, Any] = jax.device_count()
__UpperCamelCase :Dict = num_samples * [prompt]
__UpperCamelCase :Optional[int] = pipeline.prepare_inputs(__lowercase)
# shard inputs and rng
__UpperCamelCase :str = replicate(__lowercase)
__UpperCamelCase :Optional[Any] = jax.random.split(__lowercase , __lowercase)
__UpperCamelCase :Optional[Any] = shard(__lowercase)
__UpperCamelCase :Optional[Any] = pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 4.1_51_47_45) < 1E-3
assert np.abs(np.abs(__lowercase , dtype=np.floataa).sum() - 4_99_47.8_75) < 5E-1
__UpperCamelCase :Optional[int] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
assert len(__lowercase) == num_samples
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase , __UpperCamelCase :Dict = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__lowercase)
__UpperCamelCase :List[str] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__UpperCamelCase :List[Any] = jax.random.PRNGKey(0)
__UpperCamelCase :str = 50
__UpperCamelCase :Union[str, Any] = jax.device_count()
__UpperCamelCase :Any = num_samples * [prompt]
__UpperCamelCase :Dict = pipeline.prepare_inputs(__lowercase)
# shard inputs and rng
__UpperCamelCase :List[str] = replicate(__lowercase)
__UpperCamelCase :List[Any] = jax.random.split(__lowercase , __lowercase)
__UpperCamelCase :str = shard(__lowercase)
__UpperCamelCase :List[str] = pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 0.05_65_24_01)) < 1E-3
assert np.abs((np.abs(__lowercase , dtype=np.floataa).sum() - 2_38_38_08.2)) < 5E-1
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase , __UpperCamelCase :List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__lowercase)
__UpperCamelCase :Optional[int] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__UpperCamelCase :str = jax.random.PRNGKey(0)
__UpperCamelCase :Dict = 50
__UpperCamelCase :Optional[Any] = jax.device_count()
__UpperCamelCase :Optional[int] = num_samples * [prompt]
__UpperCamelCase :Optional[int] = pipeline.prepare_inputs(__lowercase)
# shard inputs and rng
__UpperCamelCase :List[str] = replicate(__lowercase)
__UpperCamelCase :Optional[Any] = jax.random.split(__lowercase , __lowercase)
__UpperCamelCase :Optional[int] = shard(__lowercase)
__UpperCamelCase :Optional[Any] = pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 0.04_00_39_06)) < 1E-3
assert np.abs((np.abs(__lowercase , dtype=np.floataa).sum() - 2_37_35_16.75)) < 5E-1
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase , __UpperCamelCase :Optional[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa)
__UpperCamelCase :List[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__UpperCamelCase :List[Any] = jax.random.PRNGKey(0)
__UpperCamelCase :List[Any] = 50
__UpperCamelCase :Any = jax.device_count()
__UpperCamelCase :List[str] = num_samples * [prompt]
__UpperCamelCase :Tuple = pipeline.prepare_inputs(__lowercase)
# shard inputs and rng
__UpperCamelCase :Any = replicate(__lowercase)
__UpperCamelCase :Optional[int] = jax.random.split(__lowercase , __lowercase)
__UpperCamelCase :Dict = shard(__lowercase)
__UpperCamelCase :Optional[Any] = pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 0.04_00_39_06)) < 1E-3
assert np.abs((np.abs(__lowercase , dtype=np.floataa).sum() - 2_37_35_16.75)) < 5E-1
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase :Tuple = FlaxDDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__lowercase , steps_offset=1 , )
__UpperCamelCase , __UpperCamelCase :Any = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__lowercase , safety_checker=__lowercase , )
__UpperCamelCase :str = scheduler.create_state()
__UpperCamelCase :Any = scheduler_state
__UpperCamelCase :Any = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__UpperCamelCase :Union[str, Any] = jax.random.PRNGKey(0)
__UpperCamelCase :Any = 50
__UpperCamelCase :str = jax.device_count()
__UpperCamelCase :Optional[int] = num_samples * [prompt]
__UpperCamelCase :List[str] = pipeline.prepare_inputs(__lowercase)
# shard inputs and rng
__UpperCamelCase :Optional[int] = replicate(__lowercase)
__UpperCamelCase :List[Any] = jax.random.split(__lowercase , __lowercase)
__UpperCamelCase :int = shard(__lowercase)
__UpperCamelCase :List[Any] = pipeline(__lowercase , __lowercase , __lowercase , __lowercase , jit=__lowercase).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa).sum() - 0.0_45_04_39_45)) < 1E-3
assert np.abs((np.abs(__lowercase , dtype=np.floataa).sum() - 2_34_76_93.5)) < 5E-1
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :Dict = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__UpperCamelCase :Optional[int] = jax.device_count()
__UpperCamelCase :int = num_samples * [prompt]
__UpperCamelCase :Optional[Any] = jax.random.split(jax.random.PRNGKey(0) , __lowercase)
__UpperCamelCase , __UpperCamelCase :Dict = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__lowercase , )
__UpperCamelCase :List[str] = replicate(__lowercase)
__UpperCamelCase :Optional[int] = pipeline.prepare_inputs(__lowercase)
__UpperCamelCase :List[Any] = shard(__lowercase)
__UpperCamelCase :str = pipeline(__lowercase , __lowercase , __lowercase , jit=__lowercase).images
assert images.shape == (num_samples, 1, 512, 512, 3)
__UpperCamelCase :int = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
__UpperCamelCase , __UpperCamelCase :Dict = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__lowercase , use_memory_efficient_attention=__lowercase , )
__UpperCamelCase :List[str] = replicate(__lowercase)
__UpperCamelCase :Dict = pipeline.prepare_inputs(__lowercase)
__UpperCamelCase :Union[str, Any] = shard(__lowercase)
__UpperCamelCase :str = pipeline(__lowercase , __lowercase , __lowercase , jit=__lowercase).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
__UpperCamelCase :Union[str, Any] = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice).max() < 1E-2
| 43 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
_UpperCAmelCase : Tuple =None
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : Dict ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
_UpperCAmelCase : Any ={
"""vocab_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
),
},
}
_UpperCAmelCase : int ={
"""facebook/nllb-large-en-ro""": 1024,
"""facebook/nllb-200-distilled-600M""": 1024,
}
# fmt: off
_UpperCAmelCase : Any =["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Dict = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""input_ids""", """attention_mask"""]
SCREAMING_SNAKE_CASE__ : int = NllbTokenizer
SCREAMING_SNAKE_CASE__ : List[int] = []
SCREAMING_SNAKE_CASE__ : List[int] = []
def __init__( self , __lowercase=None , __lowercase=None , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=False , **__lowercase , ) -> int:
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ : int = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
lowerCAmelCase_ : List[Any] = legacy_behaviour
super().__init__(
vocab_file=__lowercase , tokenizer_file=__lowercase , bos_token=__lowercase , eos_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , src_lang=__lowercase , tgt_lang=__lowercase , additional_special_tokens=__lowercase , legacy_behaviour=__lowercase , **__lowercase , )
lowerCAmelCase_ : Any = vocab_file
lowerCAmelCase_ : List[Any] = False if not self.vocab_file else True
lowerCAmelCase_ : Optional[Any] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
lowerCAmelCase_ : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(__lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowerCAmelCase_ : Any = src_lang if src_lang is not None else '''eng_Latn'''
lowerCAmelCase_ : str = self.convert_tokens_to_ids(self._src_lang )
lowerCAmelCase_ : Optional[int] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def lowercase_ ( self ) -> str:
return self._src_lang
@src_lang.setter
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : Any = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowercase_ ( self , __lowercase , __lowercase = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowercase_ ( self , __lowercase , __lowercase = None ) -> List[int]:
lowerCAmelCase_ : Optional[Any] = [self.sep_token_id]
lowerCAmelCase_ : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , **__lowercase ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : int = self(__lowercase , add_special_tokens=__lowercase , return_tensors=__lowercase , **__lowercase )
lowerCAmelCase_ : Dict = self.convert_tokens_to_ids(__lowercase )
lowerCAmelCase_ : List[Any] = tgt_lang_id
return inputs
def lowercase_ ( self , __lowercase , __lowercase = "eng_Latn" , __lowercase = None , __lowercase = "fra_Latn" , **__lowercase , ) -> BatchEncoding:
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : List[str] = tgt_lang
return super().prepare_seqaseq_batch(__lowercase , __lowercase , **__lowercase )
def lowercase_ ( self ) -> List[Any]:
return self.set_src_lang_special_tokens(self.src_lang )
def lowercase_ ( self ) -> str:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : List[str] = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
lowerCAmelCase_ : Any = []
lowerCAmelCase_ : List[str] = [self.eos_token_id, self.cur_lang_code]
else:
lowerCAmelCase_ : Optional[int] = [self.cur_lang_code]
lowerCAmelCase_ : List[Any] = [self.eos_token_id]
lowerCAmelCase_ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ : List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ : Any = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : Dict = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
lowerCAmelCase_ : List[Any] = []
lowerCAmelCase_ : Any = [self.eos_token_id, self.cur_lang_code]
else:
lowerCAmelCase_ : Any = [self.cur_lang_code]
lowerCAmelCase_ : Any = [self.eos_token_id]
lowerCAmelCase_ : List[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ : Tuple = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ : Optional[Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowercase_ ( self , __lowercase , __lowercase = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__lowercase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
return
lowerCAmelCase_ : Any = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
copyfile(self.vocab_file , __lowercase )
return (out_vocab_file,) | 262 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class __A :
@staticmethod
def __A ( *a__ , **a__ ):
pass
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image ) -> str:
_lowerCAmelCase : str = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image ) -> Dict:
_lowerCAmelCase : Dict = np.array(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = npimg.shape
return {"hash": hashimage(_lowerCamelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class __A ( unittest.TestCase ):
_UpperCamelCase : Optional[Any] = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
_UpperCamelCase : Union[str, Any] = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def __A ( self , a__ , a__ , a__ ):
_lowerCAmelCase : Union[str, Any] = MaskGenerationPipeline(model=a__ , image_processor=a__ )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __A ( self , a__ , a__ ):
pass
@require_tf
@unittest.skip("""Image segmentation not implemented in TF""" )
def __A ( self ):
pass
@slow
@require_torch
def __A ( self ):
_lowerCAmelCase : Dict = pipeline("""mask-generation""" , model="""facebook/sam-vit-huge""" )
_lowerCAmelCase : Optional[int] = image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg""" , points_per_batch=256 )
# Shortening by hashing
_lowerCAmelCase : Dict = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(a__ ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0_4_4_4},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_2_1},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_1_6_7},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_1_3_2},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_0_5_3},
{"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9_9_6_7},
{"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.9_9_3},
{"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9_9_0_9},
{"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9_8_7_9},
{"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9_8_3_4},
{"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9_7_1_6},
{"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9_6_1_2},
{"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9_5_9_9},
{"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9_5_5_2},
{"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9_5_3_2},
{"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9_5_1_6},
{"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9_4_9_9},
{"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9_4_8_3},
{"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9_4_6_4},
{"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.9_4_3},
{"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.9_4_3},
{"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9_4_0_8},
{"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9_3_3_5},
{"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9_3_2_6},
{"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9_2_6_2},
{"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8_9_9_9},
{"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8_9_8_6},
{"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8_9_8_4},
{"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8_8_7_3},
{"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8_8_7_1}
] , )
# fmt: on
@require_torch
@slow
def __A ( self ):
_lowerCAmelCase : Optional[int] = """facebook/sam-vit-huge"""
_lowerCAmelCase : Any = pipeline("""mask-generation""" , model=a__ )
_lowerCAmelCase : Optional[int] = image_segmenter(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
_lowerCAmelCase : Tuple = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(a__ ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0_4_4_4},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_2_1_0},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_1_6_7},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_1_3_2},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_0_5_3},
] , )
| 44 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
_UpperCAmelCase : Optional[Any] =NewType("""DataClass""", Any)
_UpperCAmelCase : Dict =NewType("""DataClassType""", Any)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def lowerCAmelCase ( lowerCAmelCase_ )-> Callable[[str], Any]:
lowerCAmelCase_ : str = {str(lowerCAmelCase_ ): choice for choice in choices}
return lambda lowerCAmelCase_ : str_to_choice.get(lowerCAmelCase_ , lowerCAmelCase_ )
def lowerCAmelCase ( *,
lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = dataclasses.MISSING , lowerCAmelCase_ = dataclasses.MISSING , lowerCAmelCase_ = None , **lowerCAmelCase_ , )-> dataclasses.Field:
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
lowerCAmelCase_ : Dict = {}
if aliases is not None:
lowerCAmelCase_ : str = aliases
if help is not None:
lowerCAmelCase_ : Tuple = help
return dataclasses.field(metadata=lowerCAmelCase_ , default=lowerCAmelCase_ , default_factory=lowerCAmelCase_ , **lowerCAmelCase_ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Iterable[DataClassType]
def __init__( self , __lowercase , **__lowercase ) -> List[str]:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
lowerCAmelCase_ : Optional[int] = ArgumentDefaultsHelpFormatter
super().__init__(**__lowercase )
if dataclasses.is_dataclass(__lowercase ):
lowerCAmelCase_ : Union[str, Any] = [dataclass_types]
lowerCAmelCase_ : List[Any] = list(__lowercase )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__lowercase )
@staticmethod
def lowercase_ ( __lowercase , __lowercase ) -> Union[str, Any]:
lowerCAmelCase_ : Optional[Any] = f"""--{field.name}"""
lowerCAmelCase_ : Tuple = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __lowercase ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
lowerCAmelCase_ : List[str] = kwargs.pop('''aliases''' , [] )
if isinstance(__lowercase , __lowercase ):
lowerCAmelCase_ : Optional[Any] = [aliases]
lowerCAmelCase_ : Any = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__lowercase , '''UnionType''' ) and isinstance(__lowercase , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__lowercase ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__lowercase ) not in field.type.__args__:
# filter `str` in Union
lowerCAmelCase_ : List[Any] = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
lowerCAmelCase_ : Dict = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
lowerCAmelCase_ : str = (
field.type.__args__[0] if isinstance(__lowercase , field.type.__args__[1] ) else field.type.__args__[1]
)
lowerCAmelCase_ : List[Any] = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
lowerCAmelCase_ : Dict = {}
if origin_type is Literal or (isinstance(field.type , __lowercase ) and issubclass(field.type , __lowercase )):
if origin_type is Literal:
lowerCAmelCase_ : Optional[Any] = field.type.__args__
else:
lowerCAmelCase_ : int = [x.value for x in field.type]
lowerCAmelCase_ : str = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
lowerCAmelCase_ : str = field.default
else:
lowerCAmelCase_ : Tuple = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
lowerCAmelCase_ : Tuple = copy(__lowercase )
# Hack because type=bool in argparse does not behave as we want.
lowerCAmelCase_ : Dict = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
lowerCAmelCase_ : Union[str, Any] = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
lowerCAmelCase_ : List[str] = default
# This tells argparse we accept 0 or 1 value after --field_name
lowerCAmelCase_ : int = '''?'''
# This is the value that will get picked if we do --field_name (without value)
lowerCAmelCase_ : List[Any] = True
elif isclass(__lowercase ) and issubclass(__lowercase , __lowercase ):
lowerCAmelCase_ : Union[str, Any] = field.type.__args__[0]
lowerCAmelCase_ : Dict = '''+'''
if field.default_factory is not dataclasses.MISSING:
lowerCAmelCase_ : Any = field.default_factory()
elif field.default is dataclasses.MISSING:
lowerCAmelCase_ : Optional[int] = True
else:
lowerCAmelCase_ : List[Any] = field.type
if field.default is not dataclasses.MISSING:
lowerCAmelCase_ : Dict = field.default
elif field.default_factory is not dataclasses.MISSING:
lowerCAmelCase_ : List[Any] = field.default_factory()
else:
lowerCAmelCase_ : int = True
parser.add_argument(__lowercase , *__lowercase , **__lowercase )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
lowerCAmelCase_ : Any = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__lowercase )
def lowercase_ ( self , __lowercase ) -> List[Any]:
if hasattr(__lowercase , '''_argument_group_name''' ):
lowerCAmelCase_ : str = self.add_argument_group(dtype._argument_group_name )
else:
lowerCAmelCase_ : Dict = self
try:
lowerCAmelCase_ : Dict[str, type] = get_type_hints(__lowercase )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 1_0) and "unsupported operand type(s) for |" in str(__lowercase ):
lowerCAmelCase_ : Any = '''.'''.join(map(__lowercase , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__lowercase ):
if not field.init:
continue
lowerCAmelCase_ : Optional[int] = type_hints[field.name]
self._parse_dataclass_field(__lowercase , __lowercase )
def lowercase_ ( self , __lowercase=None , __lowercase=False , __lowercase=True , __lowercase=None , __lowercase=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
lowerCAmelCase_ : str = []
if args_filename:
args_files.append(Path(__lowercase ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
lowerCAmelCase_ : str = ArgumentParser()
args_file_parser.add_argument(__lowercase , type=__lowercase , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = args_file_parser.parse_known_args(args=__lowercase )
lowerCAmelCase_ : int = vars(__lowercase ).get(args_file_flag.lstrip('''-''' ) , __lowercase )
if cmd_args_file_paths:
args_files.extend([Path(__lowercase ) for p in cmd_args_file_paths] )
lowerCAmelCase_ : Dict = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
lowerCAmelCase_ : Any = file_args + args if args is not None else file_args + sys.argv[1:]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = self.parse_known_args(args=__lowercase )
lowerCAmelCase_ : Any = []
for dtype in self.dataclass_types:
lowerCAmelCase_ : str = {f.name for f in dataclasses.fields(__lowercase ) if f.init}
lowerCAmelCase_ : str = {k: v for k, v in vars(__lowercase ).items() if k in keys}
for k in keys:
delattr(__lowercase , __lowercase )
lowerCAmelCase_ : Optional[int] = dtype(**__lowercase )
outputs.append(__lowercase )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__lowercase )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
lowerCAmelCase_ : int = set(args.keys() )
lowerCAmelCase_ : str = []
for dtype in self.dataclass_types:
lowerCAmelCase_ : int = {f.name for f in dataclasses.fields(__lowercase ) if f.init}
lowerCAmelCase_ : List[str] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
lowerCAmelCase_ : List[str] = dtype(**__lowercase )
outputs.append(__lowercase )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__lowercase )}""" )
return tuple(__lowercase )
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
with open(Path(__lowercase ) , encoding='''utf-8''' ) as open_json_file:
lowerCAmelCase_ : Dict = json.loads(open_json_file.read() )
lowerCAmelCase_ : str = self.parse_dict(__lowercase , allow_extra_keys=__lowercase )
return tuple(__lowercase )
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
lowerCAmelCase_ : Optional[Any] = self.parse_dict(yaml.safe_load(Path(__lowercase ).read_text() ) , allow_extra_keys=__lowercase )
return tuple(__lowercase ) | 262 | 0 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self , _a = None , _a = None , _a=None , _a=None ):
if not conversation_id:
__a = uuid.uuida()
if past_user_inputs is None:
__a = []
if generated_responses is None:
__a = []
__a = conversation_id
__a = past_user_inputs
__a = generated_responses
__a = text
def __eq__( self , _a ):
if not isinstance(_a , _a ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def __UpperCAmelCase ( self , _a , _a = False ):
if self.new_user_input:
if overwrite:
logger.warning(
f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
f'''with: "{text}".''' )
__a = text
else:
logger.warning(
f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input''' )
else:
__a = text
def __UpperCAmelCase ( self ):
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
__a = None
def __UpperCAmelCase ( self , _a ):
self.generated_responses.append(_a )
def __UpperCAmelCase ( self ):
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
__a = f'''Conversation id: {self.uuid} \n'''
for is_user, text in self.iter_texts():
__a = '''user''' if is_user else '''bot'''
output += f'''{name} >> {text} \n'''
return output
@add_end_docstrings(
__SCREAMING_SNAKE_CASE , r'\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n ' , )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , *_a , **_a ):
super().__init__(*_a , **_a )
if self.tokenizer.pad_token_id is None:
__a = self.tokenizer.eos_token
def __UpperCAmelCase ( self , _a=None , _a=None , _a=None , **_a ):
__a = {}
__a = {}
__a = {}
if min_length_for_response is not None:
__a = min_length_for_response
if minimum_tokens is not None:
__a = minimum_tokens
if "max_length" in generate_kwargs:
__a = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
__a = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(_a )
return preprocess_params, forward_params, postprocess_params
def __call__( self , _a , _a=0 , **_a ):
__a = super().__call__(_a , num_workers=_a , **_a )
if isinstance(_a , _a ) and len(_a ) == 1:
return outputs[0]
return outputs
def __UpperCAmelCase ( self , _a , _a=32 ):
if not isinstance(_a , _a ):
raise ValueError('''ConversationalPipeline, expects Conversation as inputs''' )
if conversation.new_user_input is None:
raise ValueError(
f'''Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '''
'''Add user inputs with the conversation\'s `add_user_input` method''' )
if hasattr(self.tokenizer , '''_build_conversation_input_ids''' ):
__a = self.tokenizer._build_conversation_input_ids(_a )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
__a = self._legacy_parse_and_tokenize(_a )
if self.framework == "pt":
__a = torch.LongTensor([input_ids] )
elif self.framework == "tf":
__a = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def __UpperCAmelCase ( self , _a , _a=10 , **_a ):
__a = generate_kwargs.get('''max_length''' , self.model.config.max_length )
__a = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})''' )
__a = max_length - minimum_tokens
__a = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
__a = model_inputs['''attention_mask'''][:, -trim:]
__a = model_inputs.pop('''conversation''' )
__a = max_length
__a = self.model.generate(**_a , **_a )
if self.model.config.is_encoder_decoder:
__a = 1
else:
__a = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def __UpperCAmelCase ( self , _a , _a=True ):
__a = model_outputs['''output_ids''']
__a = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=_a , clean_up_tokenization_spaces=_a , )
__a = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(_a )
return conversation
def __UpperCAmelCase ( self , _a ):
__a = self.tokenizer.eos_token_id
__a = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(_a , add_special_tokens=_a ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(_a , add_special_tokens=_a ) )
if len(_a ) > self.tokenizer.model_max_length:
__a = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 45 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCAmelCase ( lowerCAmelCase_ )-> Union[str, Any]:
return EnvironmentCommand()
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def lowercase_ ( __lowercase ) -> List[Any]:
lowerCAmelCase_ : List[str] = parser.add_parser('''env''' )
download_parser.set_defaults(func=__lowercase )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Optional[Any] = huggingface_hub.__version__
lowerCAmelCase_ : str = '''not installed'''
lowerCAmelCase_ : str = '''NA'''
if is_torch_available():
import torch
lowerCAmelCase_ : Any = torch.__version__
lowerCAmelCase_ : str = torch.cuda.is_available()
lowerCAmelCase_ : List[str] = '''not installed'''
if is_transformers_available():
import transformers
lowerCAmelCase_ : Any = transformers.__version__
lowerCAmelCase_ : Optional[Any] = '''not installed'''
if is_accelerate_available():
import accelerate
lowerCAmelCase_ : List[Any] = accelerate.__version__
lowerCAmelCase_ : List[str] = '''not installed'''
if is_xformers_available():
import xformers
lowerCAmelCase_ : Optional[Any] = xformers.__version__
lowerCAmelCase_ : int = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(__lowercase ) )
return info
@staticmethod
def lowercase_ ( __lowercase ) -> str:
return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n" | 262 | 0 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class lowercase ( _UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = BertJapaneseTokenizer
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = True
def _snake_case ( self ) -> Optional[int]:
super().setUp()
lowerCAmelCase = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""こんにちは""",
"""こん""",
"""にちは""",
"""ばんは""",
"""##こん""",
"""##にちは""",
"""##ばんは""",
"""世界""",
"""##世界""",
"""、""",
"""##、""",
"""。""",
"""##。""",
]
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def _snake_case ( self , lowercase ) -> Optional[int]:
lowerCAmelCase = """こんにちは、世界。 \nこんばんは、世界。"""
lowerCAmelCase = """こんにちは 、 世界 。 こんばんは 、 世界 。"""
return input_text, output_text
def _snake_case ( self , lowercase ) -> int:
lowerCAmelCase , lowerCAmelCase = self.get_input_output_texts(lowercase )
lowerCAmelCase = tokenizer.encode(lowercase , add_special_tokens=lowercase )
lowerCAmelCase = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
return text, ids
def _snake_case ( self ) -> Tuple:
pass # TODO add if relevant
def _snake_case ( self ) -> Any:
pass # TODO add if relevant
def _snake_case ( self ) -> int:
pass # TODO add if relevant
def _snake_case ( self ) -> Any:
lowerCAmelCase = self.tokenizer_class(self.vocab_file )
lowerCAmelCase = tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。""" )
self.assertListEqual(lowercase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""mecab""" )
self.assertIsNotNone(lowercase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowercase , """wb""" ) as handle:
pickle.dump(lowercase , lowercase )
with open(lowercase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowercase )
lowerCAmelCase = tokenizer_new.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
def _snake_case ( self ) -> List[str]:
lowerCAmelCase = MecabTokenizer(mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def _snake_case ( self ) -> List[Any]:
try:
lowerCAmelCase = MecabTokenizer(mecab_dic="""unidic_lite""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def _snake_case ( self ) -> Any:
try:
lowerCAmelCase = MecabTokenizer(mecab_dic="""unidic""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase = MecabTokenizer(do_lower_case=lowercase , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def _snake_case ( self ) -> Union[str, Any]:
try:
lowerCAmelCase = MecabTokenizer(
do_lower_case=lowercase , normalize_text=lowercase , mecab_option="""-d /usr/local/lib/mecab/dic/jumandic""" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
def _snake_case ( self ) -> Dict:
lowerCAmelCase = MecabTokenizer(normalize_text=lowercase , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """ """, """。"""] , )
@require_sudachi
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""sudachi""" )
self.assertIsNotNone(lowercase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowercase , """wb""" ) as handle:
pickle.dump(lowercase , lowercase )
with open(lowercase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowercase )
lowerCAmelCase = tokenizer_new.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
@require_sudachi
def _snake_case ( self ) -> List[str]:
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def _snake_case ( self ) -> Any:
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""A""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国""", """人""", """参政""", """権"""] )
@require_sudachi
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""B""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人""", """参政権"""] )
@require_sudachi
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""C""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人参政権"""] )
@require_sudachi
def _snake_case ( self ) -> Optional[int]:
lowerCAmelCase = SudachiTokenizer(do_lower_case=lowercase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = SudachiTokenizer(normalize_text=lowercase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """] , )
@require_sudachi
def _snake_case ( self ) -> List[str]:
lowerCAmelCase = SudachiTokenizer(trim_whitespace=lowercase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
@require_jumanpp
def _snake_case ( self ) -> int:
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""jumanpp""" )
self.assertIsNotNone(lowercase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowercase , """wb""" ) as handle:
pickle.dump(lowercase , lowercase )
with open(lowercase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowercase )
lowerCAmelCase = tokenizer_new.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
@require_jumanpp
def _snake_case ( self ) -> Dict:
lowerCAmelCase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def _snake_case ( self ) -> Optional[int]:
lowerCAmelCase = JumanppTokenizer(do_lower_case=lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def _snake_case ( self ) -> Dict:
lowerCAmelCase = JumanppTokenizer(normalize_text=lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def _snake_case ( self ) -> Tuple:
lowerCAmelCase = JumanppTokenizer(trim_whitespace=lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""] , )
@require_jumanpp
def _snake_case ( self ) -> Dict:
lowerCAmelCase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。""" ) , ["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""] , )
def _snake_case ( self ) -> Tuple:
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]
lowerCAmelCase = {}
for i, token in enumerate(lowercase ):
lowerCAmelCase = i
lowerCAmelCase = WordpieceTokenizer(vocab=lowercase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こんにちは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは""" ) , ["""こん""", """##ばんは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは""" ) , ["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""] )
def _snake_case ( self ) -> List[str]:
lowerCAmelCase = BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp""" )
lowerCAmelCase = tokenizer.subword_tokenizer
lowerCAmelCase = subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。""" )
self.assertListEqual(lowercase , ["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""] )
lowerCAmelCase = subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは""" )
self.assertListEqual(lowercase , ["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""] )
def _snake_case ( self ) -> Tuple:
lowerCAmelCase = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese""" )
lowerCAmelCase = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowercase )
lowerCAmelCase = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowercase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowercase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowercase , lowercase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class lowercase ( _UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = BertJapaneseTokenizer
_SCREAMING_SNAKE_CASE = False
def _snake_case ( self ) -> Optional[int]:
super().setUp()
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def _snake_case ( self , **lowercase ) -> Optional[int]:
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="""character""" , **lowercase )
def _snake_case ( self , lowercase ) -> Union[str, Any]:
lowerCAmelCase = """こんにちは、世界。 \nこんばんは、世界。"""
lowerCAmelCase = """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
return input_text, output_text
def _snake_case ( self ) -> Dict:
pass # TODO add if relevant
def _snake_case ( self ) -> Tuple:
pass # TODO add if relevant
def _snake_case ( self ) -> Tuple:
pass # TODO add if relevant
def _snake_case ( self ) -> Tuple:
lowerCAmelCase = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="""character""" )
lowerCAmelCase = tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。""" )
self.assertListEqual(
lowercase , ["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def _snake_case ( self ) -> Optional[int]:
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowerCAmelCase = {}
for i, token in enumerate(lowercase ):
lowerCAmelCase = i
lowerCAmelCase = CharacterTokenizer(vocab=lowercase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こ""", """ん""", """に""", """ち""", """は"""] )
self.assertListEqual(tokenizer.tokenize("""こんにちほ""" ) , ["""こ""", """ん""", """に""", """ち""", """[UNK]"""] )
def _snake_case ( self ) -> Dict:
lowerCAmelCase = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char""" )
lowerCAmelCase = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowercase )
lowerCAmelCase = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowercase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowercase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowercase , lowercase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class lowercase ( unittest.TestCase ):
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = """cl-tohoku/bert-base-japanese"""
lowerCAmelCase = AutoTokenizer.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
class lowercase ( unittest.TestCase ):
def _snake_case ( self ) -> Dict:
lowerCAmelCase = """cl-tohoku/bert-base-japanese"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertTokenizer.from_pretrained(lowercase )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
lowerCAmelCase = """bert-base-cased"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertJapaneseTokenizer.from_pretrained(lowercase )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
| 46 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = JukeboxTokenizer
SCREAMING_SNAKE_CASE__ : int = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
import torch
lowerCAmelCase_ : Union[str, Any] = JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
lowerCAmelCase_ : Any = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : List[str] = [
torch.tensor([[
0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
7_6, 7_6]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def lowercase_ ( self ) -> List[Any]:
import torch
lowerCAmelCase_ : Any = JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
lowerCAmelCase_ : str = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : Tuple = [
torch.tensor([[
0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 7_7, 7_7]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) ) | 262 | 0 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCamelCase : Any = logging.getLogger(__name__)
lowerCamelCase : Optional[Any] = tf.data.AUTOTUNE
def _lowerCAmelCase ( ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Train a masked language model on TPU.' )
parser.add_argument(
'--pretrained_model_config' , type=_UpperCamelCase , default='roberta-base' , help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!' , )
parser.add_argument(
'--tokenizer' , type=_UpperCamelCase , default='unigram-tokenizer-wikitext' , help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.' , )
parser.add_argument(
'--per_replica_batch_size' , type=_UpperCamelCase , default=8 , help='Batch size per TPU core.' , )
parser.add_argument(
'--no_tpu' , action='store_true' , help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.' , )
parser.add_argument(
'--tpu_name' , type=_UpperCamelCase , help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.' , default='local' , )
parser.add_argument(
'--tpu_zone' , type=_UpperCamelCase , help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.' , )
parser.add_argument(
'--gcp_project' , type=_UpperCamelCase , help='Google cloud project name. Only used for non-Colab TPU nodes.' )
parser.add_argument(
'--bfloat16' , action='store_true' , help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.' , )
parser.add_argument(
'--train_dataset' , type=_UpperCamelCase , help='Path to training dataset to load. If the path begins with `gs://`'
' then the dataset will be loaded from a Google Cloud Storage bucket.' , )
parser.add_argument(
'--shuffle_buffer_size' , type=_UpperCamelCase , default=2**18 , help='Size of the shuffle buffer (in samples)' , )
parser.add_argument(
'--eval_dataset' , type=_UpperCamelCase , help='Path to evaluation dataset to load. If the path begins with `gs://`'
' then the dataset will be loaded from a Google Cloud Storage bucket.' , )
parser.add_argument(
'--num_epochs' , type=_UpperCamelCase , default=1 , help='Number of epochs to train for.' , )
parser.add_argument(
'--learning_rate' , type=_UpperCamelCase , default=1E-4 , help='Learning rate to use for training.' , )
parser.add_argument(
'--weight_decay_rate' , type=_UpperCamelCase , default=1E-3 , help='Weight decay rate to use for training.' , )
parser.add_argument(
'--max_length' , type=_UpperCamelCase , default=5_12 , help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py' , )
parser.add_argument(
'--mlm_probability' , type=_UpperCamelCase , default=0.15 , help='Fraction of tokens to mask during training.' , )
parser.add_argument('--output_dir' , type=_UpperCamelCase , required=_UpperCamelCase , help='Path to save model checkpoints to.' )
parser.add_argument('--hub_model_id' , type=_UpperCamelCase , help='Model ID to upload to on the Hugging Face Hub.' )
_SCREAMING_SNAKE_CASE =parser.parse_args()
return args
def _lowerCAmelCase ( _UpperCamelCase : Any ) -> int:
"""simple docstring"""
try:
if args.tpu_name:
_SCREAMING_SNAKE_CASE =tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name , zone=args.tpu_zone , project=args.gcp_project )
else:
_SCREAMING_SNAKE_CASE =tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
'--gcp_project. When running on a TPU VM, use --tpu_name local.' )
tf.config.experimental_connect_to_cluster(_UpperCamelCase )
tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase )
return tpu
def _lowerCAmelCase ( _UpperCamelCase : List[Any] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
for file in file_list:
_SCREAMING_SNAKE_CASE =file.split('/' )[-1]
_SCREAMING_SNAKE_CASE =re.search(r'-\d+-(\d+)\.tfrecord' , _UpperCamelCase ).group(1 )
_SCREAMING_SNAKE_CASE =int(_UpperCamelCase )
num_samples += sample_count
return num_samples
def _lowerCAmelCase ( _UpperCamelCase : List[Any] , _UpperCamelCase : Dict , _UpperCamelCase : Dict , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Any , _UpperCamelCase : str=None ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =count_samples(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =tf.data.Dataset.from_tensor_slices(_UpperCamelCase )
if shuffle:
_SCREAMING_SNAKE_CASE =dataset.shuffle(len(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =tf.data.TFRecordDataset(_UpperCamelCase , num_parallel_reads=_UpperCamelCase )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
_SCREAMING_SNAKE_CASE =dataset.apply(tf.data.experimental.assert_cardinality(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =dataset.map(_UpperCamelCase , num_parallel_calls=_UpperCamelCase )
if shuffle:
assert shuffle_buffer_size is not None
_SCREAMING_SNAKE_CASE =dataset.shuffle(args.shuffle_buffer_size )
_SCREAMING_SNAKE_CASE =dataset.batch(_UpperCamelCase , drop_remainder=_UpperCamelCase )
_SCREAMING_SNAKE_CASE =dataset.map(_UpperCamelCase , num_parallel_calls=_UpperCamelCase )
_SCREAMING_SNAKE_CASE =dataset.prefetch(_UpperCamelCase )
return dataset
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] ) -> Tuple:
"""simple docstring"""
if not args.no_tpu:
_SCREAMING_SNAKE_CASE =initialize_tpu(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =tf.distribute.TPUStrategy(_UpperCamelCase )
else:
_SCREAMING_SNAKE_CASE =tf.distribute.OneDeviceStrategy(device='/gpu:0' )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy('mixed_bfloat16' )
_SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(args.tokenizer )
_SCREAMING_SNAKE_CASE =AutoConfig.from_pretrained(args.pretrained_model_config )
_SCREAMING_SNAKE_CASE =tokenizer.vocab_size
_SCREAMING_SNAKE_CASE =tf.io.gfile.glob(os.path.join(args.train_dataset , '*.tfrecord' ) )
if not training_records:
raise ValueError(f"No .tfrecord files found in {args.train_dataset}." )
_SCREAMING_SNAKE_CASE =tf.io.gfile.glob(os.path.join(args.eval_dataset , '*.tfrecord' ) )
if not eval_records:
raise ValueError(f"No .tfrecord files found in {args.eval_dataset}." )
_SCREAMING_SNAKE_CASE =count_samples(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
_SCREAMING_SNAKE_CASE =steps_per_epoch * args.num_epochs
with strategy.scope():
_SCREAMING_SNAKE_CASE =TFAutoModelForMaskedLM.from_config(_UpperCamelCase )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =create_optimizer(
num_train_steps=_UpperCamelCase , num_warmup_steps=total_train_steps // 20 , init_lr=args.learning_rate , weight_decay_rate=args.weight_decay_rate , )
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=_UpperCamelCase , metrics=['accuracy'] )
def decode_fn(_UpperCamelCase : Any ):
_SCREAMING_SNAKE_CASE ={
'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
}
return tf.io.parse_single_example(_UpperCamelCase , _UpperCamelCase )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
_SCREAMING_SNAKE_CASE =DataCollatorForLanguageModeling(
tokenizer=_UpperCamelCase , mlm_probability=args.mlm_probability , mlm=_UpperCamelCase , return_tensors='tf' )
def mask_with_collator(_UpperCamelCase : List[str] ):
# TF really needs an isin() function
_SCREAMING_SNAKE_CASE =(
~tf.cast(batch['attention_mask'] , tf.bool )
| (batch['input_ids'] == tokenizer.cls_token_id)
| (batch['input_ids'] == tokenizer.sep_token_id)
)
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =data_collator.tf_mask_tokens(
batch['input_ids'] , vocab_size=len(_UpperCamelCase ) , mask_token_id=tokenizer.mask_token_id , special_tokens_mask=_UpperCamelCase , )
return batch
_SCREAMING_SNAKE_CASE =args.per_replica_batch_size * strategy.num_replicas_in_sync
_SCREAMING_SNAKE_CASE =prepare_dataset(
_UpperCamelCase , decode_fn=_UpperCamelCase , mask_fn=_UpperCamelCase , batch_size=_UpperCamelCase , shuffle=_UpperCamelCase , shuffle_buffer_size=args.shuffle_buffer_size , )
_SCREAMING_SNAKE_CASE =prepare_dataset(
_UpperCamelCase , decode_fn=_UpperCamelCase , mask_fn=_UpperCamelCase , batch_size=_UpperCamelCase , shuffle=_UpperCamelCase , )
_SCREAMING_SNAKE_CASE =[]
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir , hub_model_id=args.hub_model_id , tokenizer=_UpperCamelCase ) )
model.fit(
_UpperCamelCase , validation_data=_UpperCamelCase , epochs=args.num_epochs , callbacks=_UpperCamelCase , )
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
lowerCamelCase : Union[str, Any] = parse_args()
main(args)
| 47 |
from __future__ import annotations
import requests
def lowerCAmelCase ( lowerCAmelCase_ )-> dict:
lowerCAmelCase_ : List[Any] = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(lowerCAmelCase_ ).json()
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> list[dict]:
lowerCAmelCase_ : List[Any] = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
lowerCAmelCase_ : Tuple = requests.get(lowerCAmelCase_ ).json()[:max_stories]
return [get_hackernews_story(lowerCAmelCase_ ) for story_id in story_ids]
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> str:
lowerCAmelCase_ : Optional[Any] = hackernews_top_stories(lowerCAmelCase_ )
return "\n".join('''* [{title}]({url})'''.format(**lowerCAmelCase_ ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown()) | 262 | 0 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase__ (lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : Optional[int] = XLMTokenizer
lowerCamelCase_ : Dict = False
def _lowercase ( self ) -> List[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase : Union[str, Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
lowerCamelCase : Optional[Any] = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
lowerCamelCase : Any = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
lowerCamelCase : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowerCamelCase : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" ) as fp:
fp.write(json.dumps(UpperCamelCase__ ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(UpperCamelCase__ ) )
def _lowercase ( self , UpperCamelCase__ ) -> str:
lowerCamelCase : Union[str, Any] = "lower newer"
lowerCamelCase : List[Any] = "lower newer"
return input_text, output_text
def _lowercase ( self ) -> List[Any]:
lowerCamelCase : str = XLMTokenizer(self.vocab_file , self.merges_file )
lowerCamelCase : Tuple = "lower"
lowerCamelCase : Optional[int] = ["low", "er</w>"]
lowerCamelCase : Any = tokenizer.tokenize(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Dict = tokens + ["<unk>"]
lowerCamelCase : List[Any] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , UpperCamelCase__ )
@slow
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : Union[str, Any] = XLMTokenizer.from_pretrained("xlm-mlm-en-2048" )
lowerCamelCase : int = tokenizer.encode("sequence builders" , add_special_tokens=UpperCamelCase__ )
lowerCamelCase : Optional[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__ )
lowerCamelCase : Any = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__ , UpperCamelCase__ )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 48 |
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
_UpperCAmelCase : List[str] =get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_UpperCAmelCase : Optional[int] =25_0004
_UpperCAmelCase : Tuple =25_0020
@require_sentencepiece
@require_tokenizers
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = MBartTokenizer
SCREAMING_SNAKE_CASE__ : Dict = MBartTokenizerFast
SCREAMING_SNAKE_CASE__ : Tuple = True
SCREAMING_SNAKE_CASE__ : List[str] = True
def lowercase_ ( self ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase_ : str = MBartTokenizer(__lowercase , keep_accents=__lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[int] = MBartTokenizer(__lowercase , keep_accents=__lowercase )
lowerCAmelCase_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowerCAmelCase_ : Optional[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [
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''',
'''.''',
] , )
lowerCAmelCase_ : Dict = tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(
__lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCAmelCase_ : Union[str, Any] = tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [
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 lowercase_ ( 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
lowerCAmelCase_ : int = (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})""" ):
lowerCAmelCase_ : Optional[int] = self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : int = self.tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = tempfile.mkdtemp()
lowerCAmelCase_ : Union[str, Any] = tokenizer_r.save_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.save_pretrained(__lowercase )
# 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 ) )
lowerCAmelCase_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Tuple = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=True
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : int = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Tuple = tokenizer_p.save_pretrained(__lowercase )
# Checks it save with the same files
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Optional[int] = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=False
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Optional[int] = tokenizer_p.save_pretrained(__lowercase )
# 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
lowerCAmelCase_ : Dict = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = """facebook/mbart-large-en-ro"""
SCREAMING_SNAKE_CASE__ : int = [
""" 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.""",
]
SCREAMING_SNAKE_CASE__ : Optional[int] = [
"""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.""",
]
SCREAMING_SNAKE_CASE__ : str = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def lowercase_ ( cls ) -> Optional[int]:
lowerCAmelCase_ : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
lowerCAmelCase_ : Optional[Any] = 1
return cls
def lowercase_ ( self ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 2_5_0_0_2_0 )
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
def lowercase_ ( self ) -> Any:
self.assertIn(__lowercase , self.tokenizer.all_special_ids )
lowerCAmelCase_ : Union[str, Any] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
lowerCAmelCase_ : Tuple = self.tokenizer.decode(__lowercase , skip_special_tokens=__lowercase )
lowerCAmelCase_ : List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertNotIn(self.tokenizer.eos_token , __lowercase )
def lowercase_ ( self ) -> Any:
lowerCAmelCase_ : Union[str, Any] = ['''this is gunna be a long sentence ''' * 2_0]
assert isinstance(src_text[0] , __lowercase )
lowerCAmelCase_ : str = 1_0
lowerCAmelCase_ : Tuple = self.tokenizer(__lowercase , max_length=__lowercase , truncation=__lowercase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __lowercase )
self.assertEqual(len(__lowercase ) , __lowercase )
def lowercase_ ( self ) -> int:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = tempfile.mkdtemp()
lowerCAmelCase_ : int = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = MBartTokenizer.from_pretrained(__lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __lowercase )
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Tuple = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowercase , return_tensors='''pt''' )
lowerCAmelCase_ : Tuple = 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 lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
lowerCAmelCase_ : int = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
lowerCAmelCase_ : str = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
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 lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = self.tokenizer(self.src_text , padding=__lowercase , truncation=__lowercase , max_length=3 , return_tensors='''pt''' )
lowerCAmelCase_ : Any = self.tokenizer(
text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=1_0 , return_tensors='''pt''' )
lowerCAmelCase_ : int = targets['''input_ids''']
lowerCAmelCase_ : Optional[Any] = shift_tokens_right(__lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(__lowercase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 2_5_0_0_0_1,
} , ) | 262 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case :Any = logging.get_logger(__name__)
__snake_case :Tuple = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _A ( __UpperCAmelCase ,__UpperCAmelCase ):
UpperCamelCase__ : Any = '''focalnet'''
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Tuple=224 , __SCREAMING_SNAKE_CASE : Tuple=4 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : Optional[int]=96 , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : Optional[Any]=[192, 384, 768, 768] , __SCREAMING_SNAKE_CASE : Optional[int]=[2, 2, 6, 2] , __SCREAMING_SNAKE_CASE : List[str]=[2, 2, 2, 2] , __SCREAMING_SNAKE_CASE : Dict=[3, 3, 3, 3] , __SCREAMING_SNAKE_CASE : int="gelu" , __SCREAMING_SNAKE_CASE : str=4.0 , __SCREAMING_SNAKE_CASE : List[Any]=0.0 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : Dict=False , __SCREAMING_SNAKE_CASE : Optional[Any]=1E-4 , __SCREAMING_SNAKE_CASE : List[Any]=False , __SCREAMING_SNAKE_CASE : str=False , __SCREAMING_SNAKE_CASE : int=False , __SCREAMING_SNAKE_CASE : Dict=0.02 , __SCREAMING_SNAKE_CASE : Dict=1E-5 , __SCREAMING_SNAKE_CASE : Any=32 , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : Any=None , **__SCREAMING_SNAKE_CASE : List[Any] , ):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE)
__a = image_size
__a = patch_size
__a = num_channels
__a = embed_dim
__a = use_conv_embed
__a = hidden_sizes
__a = depths
__a = focal_levels
__a = focal_windows
__a = hidden_act
__a = mlp_ratio
__a = hidden_dropout_prob
__a = drop_path_rate
__a = use_layerscale
__a = layerscale_value
__a = use_post_layernorm
__a = use_post_layernorm_in_modulation
__a = normalize_modulator
__a = initializer_range
__a = layer_norm_eps
__a = encoder_stride
__a = ['''stem'''] + [F'stage{idx}' for idx in range(1 , len(self.depths) + 1)]
__a , __a = get_aligned_output_features_output_indices(
out_features=__SCREAMING_SNAKE_CASE , out_indices=__SCREAMING_SNAKE_CASE , stage_names=self.stage_names)
| 49 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = "cpu" , lowerCAmelCase_ = None )-> None:
lowerCAmelCase_ : str = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase_ , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowerCAmelCase_ : int = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Tuple = src_path
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
fire.Fire(convert) | 262 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
_UpperCAmelCase : str = pytest.mark.integration
@require_faiss
class lowerCAmelCase ( __UpperCamelCase ):
def A_ ( self : List[Any] ) -> Union[str, Any]:
lowerCamelCase__ : List[Any] = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(UpperCAmelCase ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Optional[Any] ) -> Optional[int]:
import faiss
lowerCamelCase__ : Dataset = self._create_dummy_dataset()
lowerCamelCase__ : List[Any] = dset.map(
lambda UpperCAmelCase , UpperCAmelCase : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=UpperCAmelCase , keep_in_memory=UpperCAmelCase )
lowerCamelCase__ : Tuple = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCamelCase__ , lowerCamelCase__ : str = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Union[str, Any] ) -> int:
import faiss
lowerCamelCase__ : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[str] ) -> Tuple:
import faiss
lowerCamelCase__ : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=UpperCAmelCase ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
lowerCamelCase__ , lowerCamelCase__ : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Any ) -> Optional[Any]:
lowerCamelCase__ : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(UpperCAmelCase , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : Dict ) -> Dict:
from elasticsearch import Elasticsearch
lowerCamelCase__ : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCamelCase__ : List[Any] = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
lowerCamelCase__ : int = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
lowerCamelCase__ : List[str] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=UpperCAmelCase )
lowerCamelCase__ , lowerCamelCase__ : Dict = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class lowerCAmelCase ( __UpperCamelCase ):
def A_ ( self : Any ) -> Dict:
import faiss
lowerCamelCase__ : Tuple = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
lowerCamelCase__ : int = np.zeros(5 , dtype=np.floataa )
lowerCamelCase__ : Any = 1
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = index.search(UpperCAmelCase )
self.assertRaises(UpperCAmelCase , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
lowerCamelCase__ : str = np.eye(5 , dtype=np.floataa )[::-1]
lowerCamelCase__ , lowerCamelCase__ : Dict = index.search_batch(UpperCAmelCase )
self.assertRaises(UpperCAmelCase , index.search_batch , queries[0] )
lowerCamelCase__ : str = [scores[0] for scores in total_scores]
lowerCamelCase__ : List[str] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(UpperCAmelCase ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , UpperCAmelCase )
def A_ ( self : List[Any] ) -> List[Any]:
import faiss
lowerCamelCase__ : Any = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
lowerCamelCase__ : Tuple = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(UpperCAmelCase ):
lowerCamelCase__ : List[str] = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : List[str] ) -> Optional[int]:
import faiss
lowerCamelCase__ : Optional[Any] = faiss.IndexFlat(5 )
lowerCamelCase__ : int = FaissIndex(custom_index=UpperCAmelCase )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : Any ) -> Optional[int]:
import faiss
lowerCamelCase__ : Any = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=UpperCAmelCase ) as tmp_file:
index.save(tmp_file.name )
lowerCamelCase__ : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
lowerCamelCase__ : List[str] = np.zeros(5 , dtype=np.floataa )
lowerCamelCase__ : Tuple = 1
lowerCamelCase__ , lowerCamelCase__ : str = index.search(UpperCAmelCase )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> Any:
import faiss
lowerCamelCase__ : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
lowerCamelCase__ : Optional[int] = 'index.faiss'
lowerCamelCase__ : Optional[Any] = F"""mock://{index_name}"""
index.save(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCamelCase__ : Tuple = FaissIndex.load(_UpperCAmelCase , storage_options=mockfs.storage_options )
lowerCamelCase__ : Optional[int] = np.zeros(5 , dtype=np.floataa )
lowerCamelCase__ : Dict = 1
lowerCamelCase__ , lowerCamelCase__ : str = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class lowerCAmelCase ( __UpperCamelCase ):
def A_ ( self : Dict ) -> List[Any]:
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
lowerCamelCase__ : Any = Elasticsearch()
lowerCamelCase__ : Tuple = {'acknowledged': True}
lowerCamelCase__ : Tuple = ElasticSearchIndex(es_client=UpperCAmelCase )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
lowerCamelCase__ : Optional[int] = 'foo'
lowerCamelCase__ : str = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = index.search(UpperCAmelCase )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
lowerCamelCase__ : Any = 'foo'
lowerCamelCase__ : List[str] = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
lowerCamelCase__ , lowerCamelCase__ : Tuple = index.search(UpperCAmelCase , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
lowerCamelCase__ : List[str] = ['foo', 'bar', 'foobar']
lowerCamelCase__ : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCamelCase__ , lowerCamelCase__ : str = index.search_batch(UpperCAmelCase )
lowerCamelCase__ : List[str] = [scores[0] for scores in total_scores]
lowerCamelCase__ : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(UpperCAmelCase ) , 0 )
self.assertListEqual([1, 1, 1] , UpperCAmelCase )
# batched queries with timeout
lowerCamelCase__ : str = ['foo', 'bar', 'foobar']
lowerCamelCase__ : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = index.search_batch(UpperCAmelCase , request_timeout=30 )
lowerCamelCase__ : Optional[Any] = [scores[0] for scores in total_scores]
lowerCamelCase__ : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(UpperCAmelCase ) , 0 )
self.assertListEqual([1, 1, 1] , UpperCAmelCase )
| 50 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel | 262 | 0 |
import warnings
from functools import wraps
from typing import Callable
def A (__A : Callable ) -> Callable:
"""simple docstring"""
@wraps(__A )
def _inner_fn(*__A : Dict , **__A : int ):
warnings.warn(
(F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""") , __A , )
return fn(*__A , **__A )
return _inner_fn
| 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : str ={
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = """vit_mae"""
def __init__( self , __lowercase=7_6_8 , __lowercase=1_2 , __lowercase=1_2 , __lowercase=3_0_7_2 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1e-12 , __lowercase=2_2_4 , __lowercase=1_6 , __lowercase=3 , __lowercase=True , __lowercase=1_6 , __lowercase=5_1_2 , __lowercase=8 , __lowercase=2_0_4_8 , __lowercase=0.75 , __lowercase=False , **__lowercase , ) -> str:
super().__init__(**__lowercase )
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Any = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : int = intermediate_size
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : int = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : Union[str, Any] = image_size
lowerCAmelCase_ : Optional[int] = patch_size
lowerCAmelCase_ : Tuple = num_channels
lowerCAmelCase_ : List[str] = qkv_bias
lowerCAmelCase_ : List[Any] = decoder_num_attention_heads
lowerCAmelCase_ : int = decoder_hidden_size
lowerCAmelCase_ : Optional[int] = decoder_num_hidden_layers
lowerCAmelCase_ : Tuple = decoder_intermediate_size
lowerCAmelCase_ : Tuple = mask_ratio
lowerCAmelCase_ : Any = norm_pix_loss | 262 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
__lowerCamelCase : Union[str, Any] = pytest.mark.integration
@require_faiss
class A__ ( __snake_case ):
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = Dataset.from_dict({"filename": ["my_name-train" + "_" + str(A_ ) for x in np.arange(30 ).tolist()]} )
return dset
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Dataset = self._create_dummy_dataset()
UpperCamelCase : List[Any] = dset.map(
lambda A_ , A_ : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=A_ , keep_in_memory=A_ )
UpperCamelCase : List[str] = dset.add_faiss_index("vecs" , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
UpperCamelCase , UpperCamelCase : Tuple = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
dset.drop_index("vecs" )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
UpperCamelCase , UpperCamelCase : int = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=A_ ) as tmp_file:
dset.save_faiss_index("vecs" , tmp_file.name )
dset.load_faiss_index("vecs2" , tmp_file.name )
os.unlink(tmp_file.name )
UpperCamelCase , UpperCamelCase : List[str] = dset.get_nearest_examples("vecs2" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" )
dset.drop_index("vecs" )
self.assertRaises(A_ , partial(dset.get_nearest_examples , "vecs2" , np.ones(5 , dtype=np.floataa ) ) )
def __UpperCamelCase( self ):
'''simple docstring'''
from elasticsearch import Elasticsearch
UpperCamelCase : Dataset = self._create_dummy_dataset()
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
UpperCamelCase : List[str] = {"acknowledged": True}
mocked_bulk.return_value([(True, None)] * 30 )
UpperCamelCase : List[Any] = {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
UpperCamelCase : Optional[Any] = Elasticsearch()
dset.add_elasticsearch_index("filename" , es_client=A_ )
UpperCamelCase , UpperCamelCase : List[str] = dset.get_nearest_examples("filename" , "my_name-train_29" )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
@require_faiss
class A__ ( __snake_case ):
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Optional[int] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
UpperCamelCase : Any = np.zeros(5 , dtype=np.floataa )
UpperCamelCase : Optional[Any] = 1
UpperCamelCase , UpperCamelCase : Optional[Any] = index.search(A_ )
self.assertRaises(A_ , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
UpperCamelCase : Optional[int] = np.eye(5 , dtype=np.floataa )[::-1]
UpperCamelCase , UpperCamelCase : Tuple = index.search_batch(A_ )
self.assertRaises(A_ , index.search_batch , queries[0] )
UpperCamelCase : Optional[int] = [scores[0] for scores in total_scores]
UpperCamelCase : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A_ ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : List[str] = FaissIndex(string_factory="Flat" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
UpperCamelCase : List[str] = FaissIndex(string_factory="LSH" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(A_ ):
UpperCamelCase : List[str] = FaissIndex(string_factory="Flat" , custom_index=faiss.IndexFlat(5 ) )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : Dict = faiss.IndexFlat(5 )
UpperCamelCase : Union[str, Any] = FaissIndex(custom_index=A_ )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def __UpperCamelCase( self ):
'''simple docstring'''
import faiss
UpperCamelCase : str = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=A_ ) as tmp_file:
index.save(tmp_file.name )
UpperCamelCase : int = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
UpperCamelCase : str = np.zeros(5 , dtype=np.floataa )
UpperCamelCase : int = 1
UpperCamelCase , UpperCamelCase : Dict = index.search(A_ )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def A_ ( _lowerCAmelCase ) -> Optional[int]:
import faiss
UpperCamelCase : Union[str, Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
UpperCamelCase : List[Any] = "index.faiss"
UpperCamelCase : List[str] = F"""mock://{index_name}"""
index.save(_lowerCAmelCase , storage_options=mockfs.storage_options )
UpperCamelCase : List[str] = FaissIndex.load(_lowerCAmelCase , storage_options=mockfs.storage_options )
UpperCamelCase : List[str] = np.zeros(5 , dtype=np.floataa )
UpperCamelCase : Optional[int] = 1
UpperCamelCase , UpperCamelCase : List[str] = index.search(_lowerCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class A__ ( __snake_case ):
def __UpperCamelCase( self ):
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
UpperCamelCase : List[str] = Elasticsearch()
UpperCamelCase : Union[str, Any] = {"acknowledged": True}
UpperCamelCase : Union[str, Any] = ElasticSearchIndex(es_client=A_ )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(["foo", "bar", "foobar"] )
# single query
UpperCamelCase : str = "foo"
UpperCamelCase : Dict = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
UpperCamelCase , UpperCamelCase : Tuple = index.search(A_ )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
UpperCamelCase : Dict = "foo"
UpperCamelCase : Optional[Any] = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
UpperCamelCase , UpperCamelCase : str = index.search(A_ , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
UpperCamelCase : Dict = ["foo", "bar", "foobar"]
UpperCamelCase : List[Any] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
UpperCamelCase , UpperCamelCase : Optional[int] = index.search_batch(A_ )
UpperCamelCase : str = [scores[0] for scores in total_scores]
UpperCamelCase : Optional[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A_ ) , 0 )
self.assertListEqual([1, 1, 1] , A_ )
# batched queries with timeout
UpperCamelCase : int = ["foo", "bar", "foobar"]
UpperCamelCase : List[Any] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
UpperCamelCase , UpperCamelCase : Union[str, Any] = index.search_batch(A_ , request_timeout=30 )
UpperCamelCase : Union[str, Any] = [scores[0] for scores in total_scores]
UpperCamelCase : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A_ ) , 0 )
self.assertListEqual([1, 1, 1] , A_ )
| 52 |
def lowerCAmelCase ( lowerCAmelCase_ = 10**9 )-> int:
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[int] = 2
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : str = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
lowerCAmelCase_ : Any = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""") | 262 | 0 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowercase__ ( __lowercase : int , __lowercase : Optional[Any] , __lowercase : Dict , __lowercase : Optional[int]=1024 ) -> Dict:
"""simple docstring"""
__UpperCamelCase , __UpperCamelCase = [], []
__UpperCamelCase = list(zip(__lowercase , __lowercase ) )
__UpperCamelCase , __UpperCamelCase = sorted_examples[0]
def is_too_big(__lowercase : Optional[Any] ):
return tok(__lowercase , return_tensors='pt' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
__UpperCamelCase = new_src + ' ' + src
__UpperCamelCase = 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 )
__UpperCamelCase , __UpperCamelCase = src, tgt
else: # can fit, keep adding
__UpperCamelCase , __UpperCamelCase = 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 lowercase__ ( __lowercase : List[str] , __lowercase : Path , __lowercase : Dict , __lowercase : Dict ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase = Path(__lowercase )
save_path.mkdir(exist_ok=__lowercase )
for split in ["train"]:
__UpperCamelCase , __UpperCamelCase = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
__UpperCamelCase = [x.rstrip() for x in Path(__lowercase ).open().readlines()]
__UpperCamelCase = [x.rstrip() for x in Path(__lowercase ).open().readlines()]
__UpperCamelCase , __UpperCamelCase = 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"]:
__UpperCamelCase , __UpperCamelCase = 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 lowercase__ ( ) -> List[str]:
"""simple docstring"""
__UpperCamelCase = 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 )
__UpperCamelCase = parser.parse_args()
__UpperCamelCase = 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()
| 53 |
import inspect
import unittest
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def lowercase_ ( self ) -> List[str]:
import diffusers
from diffusers.dependency_versions_table import deps
lowerCAmelCase_ : Any = inspect.getmembers(__lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowerCAmelCase_ : Optional[int] = '''k-diffusion'''
elif backend == "invisible_watermark":
lowerCAmelCase_ : Dict = '''invisible-watermark'''
assert backend in deps, f"""{backend} is not in the deps table!""" | 262 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : int = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
'''tokenization_convbert''': ['''ConvBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = ['''ConvBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : int = [
'''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvBertForMaskedLM''',
'''ConvBertForMultipleChoice''',
'''ConvBertForQuestionAnswering''',
'''ConvBertForSequenceClassification''',
'''ConvBertForTokenClassification''',
'''ConvBertLayer''',
'''ConvBertModel''',
'''ConvBertPreTrainedModel''',
'''load_tf_weights_in_convbert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] = [
'''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFConvBertForMaskedLM''',
'''TFConvBertForMultipleChoice''',
'''TFConvBertForQuestionAnswering''',
'''TFConvBertForSequenceClassification''',
'''TFConvBertForTokenClassification''',
'''TFConvBertLayer''',
'''TFConvBertModel''',
'''TFConvBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
a__ : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 54 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_UpperCAmelCase : Any =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__lowercase , **__lowercase ) -> None:
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __lowercase , )
super().__init__(*__lowercase , **__lowercase ) | 262 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
a_ : List[str] = logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_ : Tuple = """cuda""" if torch.cuda.is_available() else """cpu"""
def __snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : List[Any]=100 , UpperCAmelCase_ : str=" " ):
lowerCamelCase_ = text.split(UpperCAmelCase_ )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(UpperCAmelCase_ ) , UpperCAmelCase_ )]
def __snake_case ( UpperCAmelCase_ : dict ):
lowerCamelCase_ ,lowerCamelCase_ = [], []
for title, text in zip(documents["title"] , documents["text"] ):
if text is not None:
for passage in split_text(UpperCAmelCase_ ):
titles.append(title if title is not None else "" )
texts.append(UpperCAmelCase_ )
return {"title": titles, "text": texts}
def __snake_case ( UpperCAmelCase_ : dict , UpperCAmelCase_ : DPRContextEncoder , UpperCAmelCase_ : DPRContextEncoderTokenizerFast ):
lowerCamelCase_ = ctx_tokenizer(
documents["title"] , documents["text"] , truncation=UpperCAmelCase_ , padding="longest" , return_tensors="pt" )["input_ids"]
lowerCamelCase_ = ctx_encoder(input_ids.to(device=UpperCAmelCase_ ) , return_dict=UpperCAmelCase_ ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def __snake_case ( UpperCAmelCase_ : "RagExampleArguments" , UpperCAmelCase_ : "ProcessingArguments" , UpperCAmelCase_ : "IndexHnswArguments" , ):
######################################
logger.info("Step 1 - Create the dataset" )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowerCamelCase_ = load_dataset(
"csv" , data_files=[rag_example_args.csv_path] , split="train" , delimiter="\t" , column_names=["title", "text"] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowerCamelCase_ = dataset.map(UpperCAmelCase_ , batched=UpperCAmelCase_ , num_proc=processing_args.num_proc )
# And compute the embeddings
lowerCamelCase_ = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=UpperCAmelCase_ )
lowerCamelCase_ = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowerCamelCase_ = Features(
{"text": Value("string" ), "title": Value("string" ), "embeddings": Sequence(Value("float32" ) )} ) # optional, save as float32 instead of float64 to save space
lowerCamelCase_ = dataset.map(
partial(UpperCAmelCase_ , ctx_encoder=UpperCAmelCase_ , ctx_tokenizer=UpperCAmelCase_ ) , batched=UpperCAmelCase_ , batch_size=processing_args.batch_size , features=UpperCAmelCase_ , )
# And finally save your dataset
lowerCamelCase_ = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset" )
dataset.save_to_disk(UpperCAmelCase_ )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info("Step 2 - Index the dataset" )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowerCamelCase_ = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index("embeddings" , custom_index=UpperCAmelCase_ )
# And save the index
lowerCamelCase_ = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset_hnsw_index.faiss" )
dataset.get_index("embeddings" ).save(UpperCAmelCase_ )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class snake_case :
"""simple docstring"""
_lowerCamelCase = field(
default=str(Path(lowercase ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" ) , metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} , )
_lowerCamelCase = field(
default=lowercase , metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} , )
_lowerCamelCase = field(
default="facebook/rag-sequence-nq" , metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} , )
_lowerCamelCase = field(
default="facebook/dpr-ctx_encoder-multiset-base" , metadata={
"help": (
"The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
" 'facebook/dpr-ctx_encoder-multiset-base'"
)
} , )
_lowerCamelCase = field(
default=str(Path(lowercase ).parent / "test_run" / "dummy-kb" ) , metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} , )
@dataclass
class snake_case :
"""simple docstring"""
_lowerCamelCase = field(
default=lowercase , metadata={
"help": "The number of processes to use to split the documents into passages. Default is single process."
} , )
_lowerCamelCase = field(
default=16 , metadata={
"help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
} , )
@dataclass
class snake_case :
"""simple docstring"""
_lowerCamelCase = field(
default=7_68 , metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} , )
_lowerCamelCase = field(
default=1_28 , metadata={
"help": (
"The number of bi-directional links created for every new element during the HNSW index construction."
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
a_ : Dict = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
a_ , a_ , a_ : List[str] = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
a_ : int = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 55 |
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : Optional[int] = set()
# edges = list of graph's edges
lowerCAmelCase_ : List[str] = get_edges(lowerCAmelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = edges.pop()
chosen_vertices.add(lowerCAmelCase_ )
chosen_vertices.add(lowerCAmelCase_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(lowerCAmelCase_ )
return chosen_vertices
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}") | 262 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if not all(char in '''01''' for char in bin_string ):
raise ValueError('''Non-binary value was passed to the function''' )
if not bin_string:
raise ValueError('''Empty string was passed to the function''' )
snake_case_ = ''''''
while len(__UpperCAmelCase ) % 3 != 0:
snake_case_ = '''0''' + bin_string
snake_case_ = [
bin_string[index : index + 3]
for index in range(len(__UpperCAmelCase ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
snake_case_ = 0
for index, val in enumerate(__UpperCAmelCase ):
oct_val += int(2 ** (2 - index) * int(__UpperCAmelCase ) )
oct_string += str(__UpperCAmelCase )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 56 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
lowerCAmelCase_ : Optional[int] = False
for divisor in range(2 , int(round(sqrt(lowerCAmelCase_ ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
lowerCAmelCase_ : Tuple = False
break
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'status' must been from type bool"
return status
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
lowerCAmelCase_ : Tuple = list(range(2 , n + 1 ) )
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(lowerCAmelCase_ ) ):
for j in range(i + 1 , len(lowerCAmelCase_ ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
lowerCAmelCase_ : str = 0
# filters actual prime numbers.
lowerCAmelCase_ : Optional[int] = [x for x in begin_list if x != 0]
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
lowerCAmelCase_ : List[Any] = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(lowerCAmelCase_ ):
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and number >= 0, "'number' must been an int and >= 0"
lowerCAmelCase_ : int = [] # this list will be returns of the function.
# potential prime number factors.
lowerCAmelCase_ : List[Any] = 2
lowerCAmelCase_ : Optional[int] = number
if number == 0 or number == 1:
ans.append(lowerCAmelCase_ )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(lowerCAmelCase_ ):
while quotient != 1:
if is_prime(lowerCAmelCase_ ) and (quotient % factor == 0):
ans.append(lowerCAmelCase_ )
quotient /= factor
else:
factor += 1
else:
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : Dict = 0
# prime factorization of 'number'
lowerCAmelCase_ : Any = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = max(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : List[Any] = 0
# prime factorization of 'number'
lowerCAmelCase_ : Dict = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = min(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 == 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 == 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 != 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 != 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[str]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (number > 2) and is_even(lowerCAmelCase_ )
), "'number' must been an int, even and > 2"
lowerCAmelCase_ : str = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
lowerCAmelCase_ : int = get_prime_numbers(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = len(lowerCAmelCase_ )
# run variable for while-loops.
lowerCAmelCase_ : Union[str, Any] = 0
lowerCAmelCase_ : Tuple = None
# exit variable. for break up the loops
lowerCAmelCase_ : int = True
while i < len_pn and loop:
lowerCAmelCase_ : int = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
lowerCAmelCase_ : Tuple = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (len(lowerCAmelCase_ ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : int = 0
while numbera != 0:
lowerCAmelCase_ : str = numbera % numbera
lowerCAmelCase_ : List[Any] = numbera
lowerCAmelCase_ : Any = rest
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : List[Any] = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
elif numbera == 1 or numbera == 1:
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : Union[str, Any] = max(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Dict = 0
lowerCAmelCase_ : Union[str, Any] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(max(lowerCAmelCase_ , lowerCAmelCase_ ) ):
ans *= n
else:
lowerCAmelCase_ : List[str] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'number' must been a positive int"
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : Optional[int] = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(lowerCAmelCase_ ):
ans += 1
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and is_prime(
lowerCAmelCase_ ), "'ans' must been a prime number and from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
assert (
is_prime(lowerCAmelCase_ ) and is_prime(lowerCAmelCase_ ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
lowerCAmelCase_ : Union[str, Any] = p_number_a + 1 # jump to the next number
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
while number < p_number_a:
ans.append(lowerCAmelCase_ )
number += 1
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and ans[0] != p_number_a
and ans[len(lowerCAmelCase_ ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 1), "'n' must been int and >= 1"
lowerCAmelCase_ : List[Any] = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(lowerCAmelCase_ )
# precondition
assert ans[0] == 1 and ans[len(lowerCAmelCase_ ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number > 1
), "'number' must been an int and >= 1"
lowerCAmelCase_ : Union[str, Any] = get_divisors(lowerCAmelCase_ )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (divisors[0] == 1)
and (divisors[len(lowerCAmelCase_ ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
lowerCAmelCase_ : Optional[Any] = gcd(abs(lowerCAmelCase_ ) , abs(lowerCAmelCase_ ) )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been a int and >= 0"
lowerCAmelCase_ : Any = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been an int and >= 0"
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Union[str, Any] = 1 # this will be return
for _ in range(n - 1 ):
lowerCAmelCase_ : Union[str, Any] = ans
ans += fiba
lowerCAmelCase_ : Optional[Any] = tmp
return ans | 262 | 0 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
A : str = logging.getLogger()
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = "\n".join(_UpperCamelCase )
Path(_UpperCamelCase ).open("w" ).writelines(_UpperCamelCase )
A : Union[str, Any] = "patrickvonplaten/t5-tiny-random"
A : Optional[Any] = "sshleifer/bart-tiny-random"
A : List[Any] = "sshleifer/tiny-mbart"
A : List[Any] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def snake_case ( self , __a ):
__lowerCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowerCAmelCase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowerCAmelCase = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
_dump_articles(__a , __a )
__lowerCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
__lowerCAmelCase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowerCAmelCase = f"\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n ".split()
with patch.object(__a , "argv" , __a ):
run_generate()
assert Path(__a ).exists()
# os.remove(Path(output_file_name))
def snake_case ( self ):
self.run_eval_tester(__a )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def snake_case ( self , __a ):
self.run_eval_tester(__a )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def snake_case ( self , __a ):
__lowerCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowerCAmelCase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowerCAmelCase = {
"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
"de": [
"Maschinelles Lernen ist großartig, oder?",
"Ich esse gerne Bananen",
"Morgen ist wieder ein toller Tag!",
],
}
__lowerCAmelCase = Path(self.get_auto_remove_tmp_dir() )
__lowerCAmelCase = str(tmp_dir / "scores.json" )
__lowerCAmelCase = str(tmp_dir / "val.target" )
_dump_articles(__a , text["en"] )
_dump_articles(__a , text["de"] )
__lowerCAmelCase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowerCAmelCase = f"\n run_eval_search.py\n {model}\n {str(__a )}\n {str(__a )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n ".split()
testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )
with patch.object(__a , "argv" , __a ):
with CaptureStdout() as cs:
run_search()
__lowerCAmelCase = [" num_beams | length_penalty", model, "Best score args"]
__lowerCAmelCase = ["Info"]
if "translation" in task:
expected_strings.append("bleu" )
else:
expected_strings.extend(__a )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(__a ).exists()
os.remove(Path(__a ) )
| 57 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
_UpperCAmelCase : Union[str, Any] ="""pt"""
elif is_tf_available():
_UpperCAmelCase : List[Any] ="""tf"""
else:
_UpperCAmelCase : Optional[int] ="""jax"""
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = PerceiverTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def lowercase_ ( self ) -> Optional[int]:
super().setUp()
lowerCAmelCase_ : str = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase_ ( self ) -> Any:
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def lowercase_ ( self , **__lowercase ) -> PerceiverTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowercase )
def lowercase_ ( self , __lowercase , __lowercase=False , __lowercase=2_0 , __lowercase=5 ) -> Tuple[str, list]:
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for Perceiver 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.
lowerCAmelCase_ : Optional[Any] = []
for i in range(len(__lowercase ) ):
try:
lowerCAmelCase_ : List[str] = tokenizer.decode([i] , clean_up_tokenization_spaces=__lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCAmelCase_ : List[str] = list(filter(lambda __lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , __lowercase ) )
lowerCAmelCase_ : Optional[int] = list(filter(lambda __lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=__lowercase ) , __lowercase ) )
if max_length is not None and len(__lowercase ) > max_length:
lowerCAmelCase_ : Union[str, Any] = toks[:max_length]
if min_length is not None and len(__lowercase ) < min_length and len(__lowercase ) > 0:
while len(__lowercase ) < min_length:
lowerCAmelCase_ : Union[str, Any] = toks + toks
# toks_str = [t[1] for t in toks]
lowerCAmelCase_ : List[str] = [t[0] for t in toks]
# Ensure consistency
lowerCAmelCase_ : int = tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
if " " not in output_txt and len(__lowercase ) > 1:
lowerCAmelCase_ : Optional[int] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=__lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=__lowercase )
)
if with_prefix_space:
lowerCAmelCase_ : Any = ''' ''' + output_txt
lowerCAmelCase_ : List[str] = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
return output_txt, output_ids
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : List[str] = self.perceiver_tokenizer
lowerCAmelCase_ : Any = '''Unicode €.'''
lowerCAmelCase_ : Dict = tokenizer(__lowercase )
lowerCAmelCase_ : Any = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['''input_ids'''] , __lowercase )
# decoding
lowerCAmelCase_ : str = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , '''[CLS]Unicode €.[SEP]''' )
lowerCAmelCase_ : Optional[int] = tokenizer('''e è é ê ë''' )
lowerCAmelCase_ : str = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['''input_ids'''] , __lowercase )
# decoding
lowerCAmelCase_ : int = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.perceiver_tokenizer
lowerCAmelCase_ : Dict = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
lowerCAmelCase_ : Optional[int] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCAmelCase_ : Optional[int] = tokenizer(__lowercase , padding=__lowercase , return_tensors=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
if FRAMEWORK != "jax":
lowerCAmelCase_ : str = list(batch.input_ids.numpy()[0] )
else:
lowerCAmelCase_ : Union[str, Any] = list(batch.input_ids.tolist()[0] )
self.assertListEqual(__lowercase , __lowercase )
self.assertEqual((2, 3_8) , batch.input_ids.shape )
self.assertEqual((2, 3_8) , batch.attention_mask.shape )
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : int = self.perceiver_tokenizer
lowerCAmelCase_ : Optional[Any] = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowerCAmelCase_ : List[Any] = tokenizer(__lowercase , padding=__lowercase , return_tensors=__lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , __lowercase )
self.assertIn('''attention_mask''' , __lowercase )
self.assertNotIn('''decoder_input_ids''' , __lowercase )
self.assertNotIn('''decoder_attention_mask''' , __lowercase )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = self.perceiver_tokenizer
lowerCAmelCase_ : int = [
'''Summary of the text.''',
'''Another summary.''',
]
lowerCAmelCase_ : List[str] = tokenizer(
text_target=__lowercase , max_length=3_2 , padding='''max_length''' , truncation=__lowercase , return_tensors=__lowercase )
self.assertEqual(3_2 , targets['''input_ids'''].shape[1] )
def lowercase_ ( self ) -> Optional[Any]:
# safety check on max_len default value so we are sure the test works
lowerCAmelCase_ : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
lowerCAmelCase_ : Optional[int] = 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
lowerCAmelCase_ : Union[str, Any] = tempfile.mkdtemp()
lowerCAmelCase_ : str = ''' He is very happy, UNwant\u00E9d,running'''
lowerCAmelCase_ : Optional[int] = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Any = tokenizer.__class__.from_pretrained(__lowercase )
lowerCAmelCase_ : Tuple = after_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
shutil.rmtree(__lowercase )
lowerCAmelCase_ : Optional[int] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCAmelCase_ : Optional[int] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
lowerCAmelCase_ : Any = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
lowerCAmelCase_ : str = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : str = tokenizer.__class__.from_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = after_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
lowerCAmelCase_ : str = tokenizer.__class__.from_pretrained(__lowercase , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(__lowercase )
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : List[Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__lowercase )
with open(os.path.join(__lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
lowerCAmelCase_ : Tuple = json.load(__lowercase )
with open(os.path.join(__lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
lowerCAmelCase_ : Any = json.load(__lowercase )
lowerCAmelCase_ : Optional[int] = [f"""<extra_id_{i}>""" for i in range(1_2_5 )]
lowerCAmelCase_ : Optional[Any] = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
lowerCAmelCase_ : Any = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(__lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(__lowercase , __lowercase )
with open(os.path.join(__lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(__lowercase , __lowercase )
# 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
lowerCAmelCase_ : int = tokenizer_class.from_pretrained(
__lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
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
lowerCAmelCase_ : Tuple = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=__lowercase )]
lowerCAmelCase_ : Dict = tokenizer_class.from_pretrained(
__lowercase , additional_special_tokens=__lowercase , )
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 lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ) , '''�''' )
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> Any:
pass
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> List[str]:
pass
def lowercase_ ( self ) -> Dict:
# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
# strings and special added tokens as tokens
lowerCAmelCase_ : Tuple = self.get_tokenizers(fast=__lowercase , do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
lowerCAmelCase_ : List[str] = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
lowerCAmelCase_ : Optional[int] = tokenizer.convert_tokens_to_string(__lowercase )
self.assertIsInstance(__lowercase , __lowercase ) | 262 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class a_ ( snake_case_ ):
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = None
class a_ ( snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCamelCase = 2
@register_to_config
def __init__( self , A = 0.02 , A = 100 , A = 1.007 , A = 80 , A = 0.05 , A = 50 , ) -> Optional[Any]:
# standard deviation of the initial noise distribution
_SCREAMING_SNAKE_CASE = sigma_max
# setable values
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None # sigma(t_i)
def snake_case_( self , A , A = None ) -> torch.FloatTensor:
return sample
def snake_case_( self , A , A = None ) -> List[Any]:
_SCREAMING_SNAKE_CASE = num_inference_steps
_SCREAMING_SNAKE_CASE = np.arange(0 , self.num_inference_steps )[::-1].copy()
_SCREAMING_SNAKE_CASE = torch.from_numpy(A ).to(A )
_SCREAMING_SNAKE_CASE = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
_SCREAMING_SNAKE_CASE = torch.tensor(A , dtype=torch.floataa , device=A )
def snake_case_( self , A , A , A = None ) -> Tuple[torch.FloatTensor, float]:
if self.config.s_min <= sigma <= self.config.s_max:
_SCREAMING_SNAKE_CASE = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
_SCREAMING_SNAKE_CASE = 0
# sample eps ~ N(0, S_noise^2 * I)
_SCREAMING_SNAKE_CASE = self.config.s_noise * randn_tensor(sample.shape , generator=A ).to(sample.device )
_SCREAMING_SNAKE_CASE = sigma + gamma * sigma
_SCREAMING_SNAKE_CASE = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def snake_case_( self , A , A , A , A , A = True , ) -> Union[KarrasVeOutput, Tuple]:
_SCREAMING_SNAKE_CASE = sample_hat + sigma_hat * model_output
_SCREAMING_SNAKE_CASE = (sample_hat - pred_original_sample) / sigma_hat
_SCREAMING_SNAKE_CASE = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A , derivative=A , pred_original_sample=A )
def snake_case_( self , A , A , A , A , A , A , A = True , ) -> Union[KarrasVeOutput, Tuple]:
_SCREAMING_SNAKE_CASE = sample_prev + sigma_prev * model_output
_SCREAMING_SNAKE_CASE = (sample_prev - pred_original_sample) / sigma_prev
_SCREAMING_SNAKE_CASE = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A , derivative=A , pred_original_sample=A )
def snake_case_( self , A , A , A ) -> List[Any]:
raise NotImplementedError()
| 58 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class snake_case__:
'''simple docstring'''
@staticmethod
def lowercase_ ( *__lowercase , **__lowercase ) -> Union[str, Any]:
pass
@is_pipeline_test
@require_vision
@require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase_ ( self , __lowercase , __lowercase , __lowercase ) -> List[str]:
lowerCAmelCase_ : Union[str, Any] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCAmelCase_ : str = [
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase_ ( self , __lowercase , __lowercase ) -> str:
lowerCAmelCase_ : Tuple = object_detector(examples[0] , threshold=0.0 )
lowerCAmelCase_ : Dict = len(__lowercase )
self.assertGreater(__lowercase , 0 )
self.assertEqual(
__lowercase , [
{
'''score''': ANY(__lowercase ),
'''label''': ANY(__lowercase ),
'''box''': {'''xmin''': ANY(__lowercase ), '''ymin''': ANY(__lowercase ), '''xmax''': ANY(__lowercase ), '''ymax''': ANY(__lowercase )},
}
for i in range(__lowercase )
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase_ ( self ) -> List[str]:
pass
@require_torch
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Union[str, Any] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCAmelCase_ : Union[str, Any] = object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
] , )
lowerCAmelCase_ : Union[str, Any] = object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
[
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
]
] , )
@require_torch
@slow
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Any = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Dict = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
] , )
lowerCAmelCase_ : Tuple = object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
] , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase_ ( self ) -> List[str]:
pass
@require_torch
@slow
def lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Any = 0.2
lowerCAmelCase_ : List[Any] = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Optional[Any] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=__lowercase , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
] , )
@require_torch
@slow
def lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Dict = 2
lowerCAmelCase_ : Union[str, Any] = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Optional[Any] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , top_k=__lowercase , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
] , ) | 262 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
__lowerCamelCase = None
__lowerCamelCase = """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowerCamelCase = [
np.dtype("""|b1"""),
np.dtype("""|u1"""),
np.dtype("""<u2"""),
np.dtype(""">u2"""),
np.dtype("""<i2"""),
np.dtype(""">i2"""),
np.dtype("""<u4"""),
np.dtype(""">u4"""),
np.dtype("""<i4"""),
np.dtype(""">i4"""),
np.dtype("""<f4"""),
np.dtype(""">f4"""),
np.dtype("""<f8"""),
np.dtype(""">f8"""),
]
@dataclass
class UpperCAmelCase :
A__ : bool = True
A__ : Optional[str] = None
# Automatically constructed
A__ : ClassVar[str] = "PIL.Image.Image"
A__ : ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
A__ : str = field(default="Image" ,init=A_ ,repr=A_ )
def __call__(self : Any ) -> List[str]:
'''simple docstring'''
return self.pa_type
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ) -> dict:
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
if isinstance(snake_case__ , snake_case__ ):
snake_case : Optional[Any] = np.array(snake_case__ )
if isinstance(snake_case__ , snake_case__ ):
return {"path": value, "bytes": None}
elif isinstance(snake_case__ , snake_case__ ):
return {"path": None, "bytes": value}
elif isinstance(snake_case__ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(snake_case__ )
elif isinstance(snake_case__ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(snake_case__ )
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] , snake_case__ : dict , snake_case__ : Any=None ) -> "PIL.Image.Image":
'''simple docstring'''
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead." )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support decoding images, please install 'Pillow'." )
if token_per_repo_id is None:
snake_case : List[Any] = {}
snake_case , snake_case : List[Any] = value["path"], value["bytes"]
if bytes_ is None:
if path is None:
raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
else:
if is_local_path(snake_case__ ):
snake_case : Optional[Any] = PIL.Image.open(snake_case__ )
else:
snake_case : Any = path.split("::" )[-1]
try:
snake_case : int = string_to_dict(snake_case__ , config.HUB_DATASETS_URL )["repo_id"]
snake_case : Optional[int] = token_per_repo_id.get(snake_case__ )
except ValueError:
snake_case : Dict = None
with xopen(snake_case__ , "rb" , use_auth_token=snake_case__ ) as f:
snake_case : Union[str, Any] = BytesIO(f.read() )
snake_case : Tuple = PIL.Image.open(bytes_ )
else:
snake_case : int = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
'''simple docstring'''
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("binary" ),
"path": Value("string" ),
}
)
def _SCREAMING_SNAKE_CASE (self : Dict , snake_case__ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ) -> pa.StructArray:
'''simple docstring'''
if pa.types.is_string(storage.type ):
snake_case : str = pa.array([None] * len(snake_case__ ) , type=pa.binary() )
snake_case : Tuple = pa.StructArray.from_arrays([bytes_array, storage] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
snake_case : Dict = pa.array([None] * len(snake_case__ ) , type=pa.string() )
snake_case : Tuple = pa.StructArray.from_arrays([storage, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
snake_case : str = storage.field("bytes" )
else:
snake_case : Union[str, Any] = pa.array([None] * len(snake_case__ ) , type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
snake_case : str = storage.field("path" )
else:
snake_case : int = pa.array([None] * len(snake_case__ ) , type=pa.string() )
snake_case : int = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
snake_case : Optional[Any] = pa.array(
[encode_np_array(np.array(snake_case__ ) )["bytes"] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
snake_case : str = pa.array([None] * len(snake_case__ ) , type=pa.string() )
snake_case : Any = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(snake_case__ , self.pa_type )
def _SCREAMING_SNAKE_CASE (self : int , snake_case__ : pa.StructArray ) -> pa.StructArray:
'''simple docstring'''
@no_op_if_value_is_null
def path_to_bytes(snake_case__ : List[str] ):
with xopen(snake_case__ , "rb" ) as f:
snake_case : int = f.read()
return bytes_
snake_case : Tuple = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
snake_case : Dict = pa.array(
[os.path.basename(snake_case__ ) if path is not None else None for path in storage.field("path" ).to_pylist()] , type=pa.string() , )
snake_case : int = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(snake_case__ , self.pa_type )
def UpperCamelCase ( ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
snake_case : Tuple = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def UpperCamelCase ( __lowerCamelCase : "PIL.Image.Image" ):
snake_case : Dict = BytesIO()
if image.format in list_image_compression_formats():
snake_case : Optional[Any] = image.format
else:
snake_case : List[Any] = "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
image.save(__lowerCamelCase , format=__lowerCamelCase )
return buffer.getvalue()
def UpperCamelCase ( __lowerCamelCase : "PIL.Image.Image" ):
if hasattr(__lowerCamelCase , "filename" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(__lowerCamelCase )}
def UpperCamelCase ( __lowerCamelCase : np.ndarray ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
snake_case : List[str] = array.dtype
snake_case : Dict = dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
snake_case : Dict = dtype.kind
snake_case : int = dtype.itemsize
snake_case : Tuple = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
snake_case : Any = np.dtype("|u1" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
snake_case : Optional[int] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
snake_case : List[str] = dtype_byteorder + dtype_kind + str(__lowerCamelCase )
snake_case : Dict = np.dtype(__lowerCamelCase )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
snake_case : List[str] = PIL.Image.fromarray(array.astype(__lowerCamelCase ) )
return {"path": None, "bytes": image_to_bytes(__lowerCamelCase )}
def UpperCamelCase ( __lowerCamelCase : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
if objs:
snake_case , snake_case : str = first_non_null_value(__lowerCamelCase )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(__lowerCamelCase , np.ndarray ):
snake_case : Any = no_op_if_value_is_null(__lowerCamelCase )
return [obj_to_image_dict_func(__lowerCamelCase ) for obj in objs]
elif isinstance(__lowerCamelCase , PIL.Image.Image ):
snake_case : Optional[int] = no_op_if_value_is_null(__lowerCamelCase )
return [obj_to_image_dict_func(__lowerCamelCase ) for obj in objs]
else:
return objs
else:
return objs
| 59 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels | 262 | 0 |
"""simple docstring"""
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 snake_case_( unittest.TestCase ):
def lowerCamelCase__ ( self : int ):
lowerCAmelCase : Tuple = inspect.getfile(accelerate.test_utils )
lowerCAmelCase : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
lowerCAmelCase : Optional[int] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
lowerCAmelCase : Union[str, Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )
@require_multi_gpu
def lowerCamelCase__ ( self : int ):
print(F'''Found {torch.cuda.device_count()} devices.''' )
lowerCAmelCase : str = ['''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 lowerCamelCase__ ( self : List[Any] ):
print(F'''Found {torch.cuda.device_count()} devices.''' )
lowerCAmelCase : 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 lowerCamelCase__ ( self : Dict ):
lowerCAmelCase : Optional[int] = ['''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 lowerCamelCase__ ( self : Dict ):
print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
lowerCAmelCase : Tuple = ['''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__ : List[str] = Accelerator()
snake_case__ : Dict = (accelerator.state.process_index + 2, 10)
snake_case__ : Dict = torch.randint(0, 10, shape).to(accelerator.device)
snake_case__ : Any = ''''''
snake_case__ : Tuple = 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__ : Dict = 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__ : int = 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)
| 60 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Dict ={
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = """ernie_m"""
SCREAMING_SNAKE_CASE__ : Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __lowercase = 2_5_0_0_0_2 , __lowercase = 7_6_8 , __lowercase = 1_2 , __lowercase = 1_2 , __lowercase = 3_0_7_2 , __lowercase = "gelu" , __lowercase = 0.1 , __lowercase = 0.1 , __lowercase = 5_1_4 , __lowercase = 0.02 , __lowercase = 1 , __lowercase = 1e-05 , __lowercase=None , __lowercase=False , __lowercase=0.0 , **__lowercase , ) -> Tuple:
super().__init__(pad_token_id=__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = vocab_size
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Tuple = num_hidden_layers
lowerCAmelCase_ : int = num_attention_heads
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : int = hidden_act
lowerCAmelCase_ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = max_position_embeddings
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : List[Any] = classifier_dropout
lowerCAmelCase_ : Any = is_decoder
lowerCAmelCase_ : List[Any] = act_dropout | 262 | 0 |
"""simple docstring"""
def __a ( __lowerCamelCase ):
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
UpperCAmelCase_ : str = sorted(string.lower() )
return len(__lowerCamelCase ) == len(set(__lowerCamelCase ) )
if __name__ == "__main__":
_a = input('Enter a string ').strip()
_a = is_isogram(input_str)
print(f"""{input_str} is {'an' if isogram else 'not an'} isogram.""")
| 61 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase=7 , __lowercase=3 , __lowercase=1_8 , __lowercase=3_0 , __lowercase=4_0_0 , __lowercase=True , __lowercase=None , __lowercase=True , __lowercase=None , ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = size if size is not None else {'''shortest_edge''': 2_0}
lowerCAmelCase_ : Any = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
lowerCAmelCase_ : Any = parent
lowerCAmelCase_ : Any = batch_size
lowerCAmelCase_ : Optional[int] = num_channels
lowerCAmelCase_ : Tuple = image_size
lowerCAmelCase_ : List[str] = min_resolution
lowerCAmelCase_ : Dict = max_resolution
lowerCAmelCase_ : Tuple = do_resize
lowerCAmelCase_ : Optional[Any] = size
lowerCAmelCase_ : Union[str, Any] = do_center_crop
lowerCAmelCase_ : Optional[Any] = crop_size
def lowercase_ ( self ) -> Union[str, Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = MobileNetVaImageProcessor if is_vision_available() else None
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Union[str, Any] = MobileNetVaImageProcessingTester(self )
@property
def lowercase_ ( self ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowercase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowercase , '''size''' ) )
self.assertTrue(hasattr(__lowercase , '''do_center_crop''' ) )
self.assertTrue(hasattr(__lowercase , '''crop_size''' ) )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 2_0} )
self.assertEqual(image_processor.crop_size , {'''height''': 1_8, '''width''': 1_8} )
lowerCAmelCase_ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2} )
self.assertEqual(image_processor.crop_size , {'''height''': 8_4, '''width''': 8_4} )
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> Union[str, Any]:
# Initialize image_processing
lowerCAmelCase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , Image.Image )
# Test not batched input
lowerCAmelCase_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Tuple = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self ) -> Optional[int]:
# Initialize image_processing
lowerCAmelCase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , np.ndarray )
# Test not batched input
lowerCAmelCase_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Tuple = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self ) -> Any:
# Initialize image_processing
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , torch.Tensor )
# Test not batched input
lowerCAmelCase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Dict = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , ) | 262 | 0 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_A = logging.get_logger(__name__)
_A = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
_A = {
'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
'tokenizer_config_file': {
'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
},
}
_A = {'facebook/blenderbot-3B': 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCAmelCase ( ):
__UpperCamelCase =(
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
__UpperCamelCase =bs[:]
__UpperCamelCase =0
for b in range(2**8 ):
if b not in bs:
bs.append(SCREAMING_SNAKE_CASE__ )
cs.append(2**8 + n )
n += 1
__UpperCamelCase =[chr(SCREAMING_SNAKE_CASE__ ) for n in cs]
return dict(zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Tuple ):
__UpperCamelCase =set()
__UpperCamelCase =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__UpperCamelCase =char
return pairs
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = VOCAB_FILES_NAMES
UpperCAmelCase__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ : Dict = ["input_ids", "attention_mask"]
def __init__( self , A_ , A_ , A_="replace" , A_="<s>" , A_="</s>" , A_="</s>" , A_="<s>" , A_="<unk>" , A_="<pad>" , A_="<mask>" , A_=False , **A_ , ) -> Union[str, Any]:
__UpperCamelCase =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else bos_token
__UpperCamelCase =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else eos_token
__UpperCamelCase =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else sep_token
__UpperCamelCase =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else cls_token
__UpperCamelCase =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else unk_token
__UpperCamelCase =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__UpperCamelCase =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else mask_token
super().__init__(
errors=A_ , bos_token=A_ , eos_token=A_ , unk_token=A_ , sep_token=A_ , cls_token=A_ , pad_token=A_ , mask_token=A_ , add_prefix_space=A_ , **A_ , )
with open(A_ , encoding='utf-8' ) as vocab_handle:
__UpperCamelCase =json.load(A_ )
__UpperCamelCase ={v: k for k, v in self.encoder.items()}
__UpperCamelCase =errors # how to handle errors in decoding
__UpperCamelCase =bytes_to_unicode()
__UpperCamelCase ={v: k for k, v in self.byte_encoder.items()}
with open(A_ , encoding='utf-8' ) as merges_handle:
__UpperCamelCase =merges_handle.read().split('\n' )[1:-1]
__UpperCamelCase =[tuple(merge.split() ) for merge in bpe_merges]
__UpperCamelCase =dict(zip(A_ , range(len(A_ ) ) ) )
__UpperCamelCase ={}
__UpperCamelCase =add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__UpperCamelCase =re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _a ( self ) -> Union[str, Any]:
return len(self.encoder )
def _a ( self ) -> Union[str, Any]:
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self , A_ ) -> List[str]:
if token in self.cache:
return self.cache[token]
__UpperCamelCase =tuple(A_ )
__UpperCamelCase =get_pairs(A_ )
if not pairs:
return token
while True:
__UpperCamelCase =min(A_ , key=lambda A_ : self.bpe_ranks.get(A_ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
__UpperCamelCase , __UpperCamelCase =bigram
__UpperCamelCase =[]
__UpperCamelCase =0
while i < len(A_ ):
try:
__UpperCamelCase =word.index(A_ , A_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__UpperCamelCase =j
if word[i] == first and i < len(A_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__UpperCamelCase =tuple(A_ )
__UpperCamelCase =new_word
if len(A_ ) == 1:
break
else:
__UpperCamelCase =get_pairs(A_ )
__UpperCamelCase =' '.join(A_ )
__UpperCamelCase =word
return word
def _a ( self , A_ ) -> Tuple:
__UpperCamelCase =[]
for token in re.findall(self.pat , A_ ):
__UpperCamelCase =''.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(A_ ).split(' ' ) )
return bpe_tokens
def _a ( self , A_ ) -> Dict:
return self.encoder.get(A_ , self.encoder.get(self.unk_token ) )
def _a ( self , A_ ) -> Optional[Any]:
return self.decoder.get(A_ )
def _a ( self , A_ ) -> int:
__UpperCamelCase =''.join(A_ )
__UpperCamelCase =bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def _a ( self , A_ , A_ = None ) -> Tuple[str]:
if not os.path.isdir(A_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
__UpperCamelCase =os.path.join(
A_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
__UpperCamelCase =os.path.join(
A_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(A_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=A_ , ensure_ascii=A_ ) + '\n' )
__UpperCamelCase =0
with open(A_ , '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 A_ : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
' Please check that the tokenizer is not corrupted!' )
__UpperCamelCase =token_index
writer.write(' '.join(A_ ) + '\n' )
index += 1
return vocab_file, merge_file
def _a ( self , A_ , A_ = None , A_ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
if token_ids_a is None:
return [1] + ([0] * len(A_ )) + [1]
return [1] + ([0] * len(A_ )) + [1, 1] + ([0] * len(A_ )) + [1]
def _a ( self , A_ , A_ = None ) -> List[int]:
__UpperCamelCase =[self.sep_token_id]
__UpperCamelCase =[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 , A_ , A_=False , **A_ ) -> Tuple:
__UpperCamelCase =kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(A_ ) > 0 and not text[0].isspace()):
__UpperCamelCase =' ' + text
return (text, kwargs)
def _a ( self , A_ , A_ = None ) -> Tuple:
return token_ids_a + [self.eos_token_id]
def _a ( self , A_ ) -> List[int]:
__UpperCamelCase =[]
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(' ' + text )
else:
# Generated responses should contain them already.
inputs.append(A_ )
__UpperCamelCase =' '.join(A_ )
__UpperCamelCase =self.encode(A_ )
if len(A_ ) > self.model_max_length:
__UpperCamelCase =input_ids[-self.model_max_length :]
logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.' )
return input_ids
| 62 |
from __future__ import annotations
import math
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> None:
lowerCAmelCase_ : str = size
# approximate the overall size of segment tree with given value
lowerCAmelCase_ : Dict = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
lowerCAmelCase_ : Dict = [0 for i in range(0 , 4 * size )]
lowerCAmelCase_ : Optional[int] = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowercase_ ( self , __lowercase ) -> int:
return idx * 2
def lowercase_ ( self , __lowercase ) -> int:
return idx * 2 + 1
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase ) -> None:
if left_element == right_element:
lowerCAmelCase_ : Tuple = a[left_element - 1]
else:
lowerCAmelCase_ : int = (left_element + right_element) // 2
self.build(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase )
self.build(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase )
lowerCAmelCase_ : Any = max(
self.segment_tree[self.left(__lowercase )] , self.segment_tree[self.right(__lowercase )] )
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> bool:
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Union[str, Any] = False
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Any = self.lazy[idx]
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : Optional[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
lowerCAmelCase_ : Dict = val
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = val
lowerCAmelCase_ : List[Any] = val
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : List[str] = True
return True
lowerCAmelCase_ : Optional[Any] = (left_element + right_element) // 2
self.update(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
self.update(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase , __lowercase , __lowercase )
lowerCAmelCase_ : int = max(
self.segment_tree[self.left(__lowercase )] , self.segment_tree[self.right(__lowercase )] )
return True
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> int | float:
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Optional[Any] = False
if left_element != right_element:
lowerCAmelCase_ : List[Any] = self.lazy[idx]
lowerCAmelCase_ : Dict = self.lazy[idx]
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : Optional[int] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
lowerCAmelCase_ : List[Any] = (left_element + right_element) // 2
lowerCAmelCase_ : Tuple = self.query(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase , __lowercase )
lowerCAmelCase_ : List[Any] = self.query(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase , __lowercase )
return max(__lowercase , __lowercase )
def __str__( self ) -> str:
return str([self.query(1 , 1 , self.size , __lowercase , __lowercase ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
_UpperCAmelCase : str =[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
_UpperCAmelCase : List[str] =15
_UpperCAmelCase : Any =SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 262 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : list[int] , lowercase : list[int] ) -> None:
_a = len(lowercase )
print("The following activities are selected:" )
# The first activity is always selected
_a = 0
print(lowercase , end="," )
# Consider rest of the activities
for j in range(lowercase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(lowercase , end="," )
_a = j
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase_ : Any = [1, 3, 0, 5, 8, 5]
lowerCAmelCase_ : Optional[int] = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 63 |
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
_UpperCAmelCase : Optional[int] ="""src/transformers"""
_UpperCAmelCase : str ="""docs/source/en"""
_UpperCAmelCase : Optional[int] ="""."""
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
with open(lowerCAmelCase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCAmelCase_ : int = f.readlines()
# Find the start prompt.
lowerCAmelCase_ : List[Any] = 0
while not lines[start_index].startswith(lowerCAmelCase_ ):
start_index += 1
start_index += 1
lowerCAmelCase_ : List[str] = start_index
while not lines[end_index].startswith(lowerCAmelCase_ ):
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 |
_UpperCAmelCase : Optional[Any] ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase : Optional[int] =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase : Dict =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.
_UpperCAmelCase : Optional[Any] =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase : Optional[int] =direct_transformers_import(TRANSFORMERS_PATH)
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : str = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , lowerCAmelCase_ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : Tuple = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_ )
lowerCAmelCase_ : int = (width - text_length) // 2
lowerCAmelCase_ : Union[str, Any] = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCAmelCase ( )-> str:
lowerCAmelCase_ : Any = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowerCAmelCase_ : Dict = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowerCAmelCase_ : List[Any] = {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.
lowerCAmelCase_ : Tuple = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[Any] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowerCAmelCase_ ):
lowerCAmelCase_ : Optional[int] = None
if attr_name.endswith('''Tokenizer''' ):
lowerCAmelCase_ : Union[str, Any] = slow_tokenizers
lowerCAmelCase_ : List[str] = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
lowerCAmelCase_ : int = fast_tokenizers
lowerCAmelCase_ : Union[str, Any] = attr_name[:-13]
elif _re_tf_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = tf_models
lowerCAmelCase_ : str = _re_tf_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_flax_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = flax_models
lowerCAmelCase_ : Union[str, Any] = _re_flax_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_pt_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Any = pt_models
lowerCAmelCase_ : List[Any] = _re_pt_models.match(lowerCAmelCase_ ).groups()[0]
if lookup_dict is not None:
while len(lowerCAmelCase_ ) > 0:
if attr_name in model_name_to_prefix.values():
lowerCAmelCase_ : Union[str, Any] = True
break
# Try again after removing the last word in the name
lowerCAmelCase_ : Any = ''''''.join(camel_case_split(lowerCAmelCase_ )[:-1] )
# Let's build that table!
lowerCAmelCase_ : int = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowerCAmelCase_ : Tuple = ['''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).
lowerCAmelCase_ : Union[str, Any] = [len(lowerCAmelCase_ ) + 2 for c in columns]
lowerCAmelCase_ : Optional[Any] = max([len(lowerCAmelCase_ ) for name in model_names] ) + 2
# Build the table per se
lowerCAmelCase_ : Dict = '''|''' + '''|'''.join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for c, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
lowerCAmelCase_ : List[str] = {True: '''✅''', False: '''❌'''}
for name in model_names:
lowerCAmelCase_ : List[Any] = model_name_to_prefix[name]
lowerCAmelCase_ : Union[str, Any] = [
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(lowerCAmelCase_ , lowerCAmelCase_ ) for l, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + "|\n"
return table
def lowerCAmelCase ( lowerCAmelCase_=False )-> Tuple:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = _find_text_in_file(
filename=os.path.join(lowerCAmelCase_ , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
lowerCAmelCase_ : Tuple = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowerCAmelCase_ , '''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__":
_UpperCAmelCase : List[Any] =argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_UpperCAmelCase : Tuple =parser.parse_args()
check_model_table(args.fix_and_overwrite) | 262 | 0 |
"""simple docstring"""
# 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 ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 64 |
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 ( )-> int:
lowerCAmelCase_ : int = ArgumentParser('''Transformers CLI tool''' , usage='''transformers-cli <command> [<args>]''' )
lowerCAmelCase_ : Dict = parser.add_subparsers(help='''transformers-cli command helpers''' )
# Register commands
ConvertCommand.register_subcommand(lowerCAmelCase_ )
DownloadCommand.register_subcommand(lowerCAmelCase_ )
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
RunCommand.register_subcommand(lowerCAmelCase_ )
ServeCommand.register_subcommand(lowerCAmelCase_ )
UserCommands.register_subcommand(lowerCAmelCase_ )
AddNewModelCommand.register_subcommand(lowerCAmelCase_ )
AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_ )
LfsCommands.register_subcommand(lowerCAmelCase_ )
PTtoTFCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
lowerCAmelCase_ : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , '''func''' ):
parser.print_help()
exit(1 )
# Run
lowerCAmelCase_ : List[Any] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main() | 262 | 0 |
from statistics import mean
import numpy as np
def lowerCAmelCase_ ( __A, __A, __A, __A ) -> list:
'''simple docstring'''
UpperCAmelCase__ = 0
# Number of processes finished
UpperCAmelCase__ = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
UpperCAmelCase__ = [0] * no_of_process
# List to include calculation results
UpperCAmelCase__ = [0] * no_of_process
# Sort by arrival time.
UpperCAmelCase__ = [burst_time[i] for i in np.argsort(__A )]
UpperCAmelCase__ = [process_name[i] for i in np.argsort(__A )]
arrival_time.sort()
while no_of_process > finished_process_count:
UpperCAmelCase__ = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
UpperCAmelCase__ = arrival_time[i]
UpperCAmelCase__ = 0
# Index showing the location of the process being performed
UpperCAmelCase__ = 0
# Saves the current response ratio.
UpperCAmelCase__ = 0
for i in range(0, __A ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
UpperCAmelCase__ = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
UpperCAmelCase__ = temp
UpperCAmelCase__ = i
# Calculate the turn around time
UpperCAmelCase__ = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
UpperCAmelCase__ = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def lowerCAmelCase_ ( __A, __A, __A, __A ) -> list:
'''simple docstring'''
UpperCAmelCase__ = [0] * no_of_process
for i in range(0, __A ):
UpperCAmelCase__ = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
UpperCamelCase__ = 5
UpperCamelCase__ = ['A', 'B', 'C', 'D', 'E']
UpperCamelCase__ = [1, 2, 3, 4, 5]
UpperCamelCase__ = [1, 2, 3, 4, 5]
UpperCamelCase__ = calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
UpperCamelCase__ = calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
for i in range(0, no_of_process):
print(
f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
)
print(f'''average waiting time : {mean(waiting_time):.5f}''')
print(f'''average turn around time : {mean(turn_around_time):.5f}''')
| 65 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
_UpperCAmelCase : Tuple =None
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : Dict ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
_UpperCAmelCase : Any ={
"""vocab_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
),
},
}
_UpperCAmelCase : int ={
"""facebook/nllb-large-en-ro""": 1024,
"""facebook/nllb-200-distilled-600M""": 1024,
}
# fmt: off
_UpperCAmelCase : Any =["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Dict = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""input_ids""", """attention_mask"""]
SCREAMING_SNAKE_CASE__ : int = NllbTokenizer
SCREAMING_SNAKE_CASE__ : List[int] = []
SCREAMING_SNAKE_CASE__ : List[int] = []
def __init__( self , __lowercase=None , __lowercase=None , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=False , **__lowercase , ) -> int:
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ : int = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
lowerCAmelCase_ : List[Any] = legacy_behaviour
super().__init__(
vocab_file=__lowercase , tokenizer_file=__lowercase , bos_token=__lowercase , eos_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , src_lang=__lowercase , tgt_lang=__lowercase , additional_special_tokens=__lowercase , legacy_behaviour=__lowercase , **__lowercase , )
lowerCAmelCase_ : Any = vocab_file
lowerCAmelCase_ : List[Any] = False if not self.vocab_file else True
lowerCAmelCase_ : Optional[Any] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
lowerCAmelCase_ : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(__lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowerCAmelCase_ : Any = src_lang if src_lang is not None else '''eng_Latn'''
lowerCAmelCase_ : str = self.convert_tokens_to_ids(self._src_lang )
lowerCAmelCase_ : Optional[int] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def lowercase_ ( self ) -> str:
return self._src_lang
@src_lang.setter
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : Any = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowercase_ ( self , __lowercase , __lowercase = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowercase_ ( self , __lowercase , __lowercase = None ) -> List[int]:
lowerCAmelCase_ : Optional[Any] = [self.sep_token_id]
lowerCAmelCase_ : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , **__lowercase ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : int = self(__lowercase , add_special_tokens=__lowercase , return_tensors=__lowercase , **__lowercase )
lowerCAmelCase_ : Dict = self.convert_tokens_to_ids(__lowercase )
lowerCAmelCase_ : List[Any] = tgt_lang_id
return inputs
def lowercase_ ( self , __lowercase , __lowercase = "eng_Latn" , __lowercase = None , __lowercase = "fra_Latn" , **__lowercase , ) -> BatchEncoding:
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : List[str] = tgt_lang
return super().prepare_seqaseq_batch(__lowercase , __lowercase , **__lowercase )
def lowercase_ ( self ) -> List[Any]:
return self.set_src_lang_special_tokens(self.src_lang )
def lowercase_ ( self ) -> str:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : List[str] = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
lowerCAmelCase_ : Any = []
lowerCAmelCase_ : List[str] = [self.eos_token_id, self.cur_lang_code]
else:
lowerCAmelCase_ : Optional[int] = [self.cur_lang_code]
lowerCAmelCase_ : List[Any] = [self.eos_token_id]
lowerCAmelCase_ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ : List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ : Any = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : Dict = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
lowerCAmelCase_ : List[Any] = []
lowerCAmelCase_ : Any = [self.eos_token_id, self.cur_lang_code]
else:
lowerCAmelCase_ : Any = [self.cur_lang_code]
lowerCAmelCase_ : Any = [self.eos_token_id]
lowerCAmelCase_ : List[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ : Tuple = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ : Optional[Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowercase_ ( self , __lowercase , __lowercase = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__lowercase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
return
lowerCAmelCase_ : Any = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
copyfile(self.vocab_file , __lowercase )
return (out_vocab_file,) | 262 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
__a = "hf-internal-testing/tiny-random-bert"
__a = os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
__a = "9b8c223d42b2188cb49d29af482996f9d0f3e5a6"
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self: Optional[int] ) -> Tuple:
snake_case_ :Tuple = cached_file(snake_case , snake_case )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(snake_case ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(snake_case , snake_case ) ) )
with open(os.path.join(snake_case , """refs""" , """main""" ) ) as f:
snake_case_ :List[str] = f.read()
self.assertEqual(snake_case , os.path.join(snake_case , """snapshots""" , snake_case , snake_case ) )
self.assertTrue(os.path.isfile(snake_case ) )
# File is cached at the same place the second time.
snake_case_ :Tuple = cached_file(snake_case , snake_case )
self.assertEqual(snake_case , snake_case )
# Using a specific revision to test the full commit hash.
snake_case_ :List[str] = cached_file(snake_case , snake_case , revision="""9b8c223""" )
self.assertEqual(snake_case , os.path.join(snake_case , """snapshots""" , snake_case , snake_case ) )
def lowerCAmelCase_ ( self: List[str] ) -> List[Any]:
with self.assertRaisesRegex(snake_case , """is not a valid model identifier""" ):
snake_case_ :int = cached_file("""tiny-random-bert""" , snake_case )
with self.assertRaisesRegex(snake_case , """is not a valid git identifier""" ):
snake_case_ :str = cached_file(snake_case , snake_case , revision="""aaaa""" )
with self.assertRaisesRegex(snake_case , """does not appear to have a file named""" ):
snake_case_ :Tuple = cached_file(snake_case , """conf""" )
def lowerCAmelCase_ ( self: int ) -> List[str]:
with self.assertRaisesRegex(snake_case , """does not appear to have a file named""" ):
snake_case_ :Any = cached_file(snake_case , """conf""" )
with open(os.path.join(snake_case , """refs""" , """main""" ) ) as f:
snake_case_ :Optional[Any] = f.read()
self.assertTrue(os.path.isfile(os.path.join(snake_case , """.no_exist""" , snake_case , """conf""" ) ) )
snake_case_ :List[str] = cached_file(snake_case , """conf""" , _raise_exceptions_for_missing_entries=snake_case )
self.assertIsNone(snake_case )
snake_case_ :int = cached_file(snake_case , """conf""" , local_files_only=snake_case , _raise_exceptions_for_missing_entries=snake_case )
self.assertIsNone(snake_case )
snake_case_ :Optional[int] = mock.Mock()
snake_case_ :List[Any] = 500
snake_case_ :List[str] = {}
snake_case_ :Dict = HTTPError
snake_case_ :Optional[Any] = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("""requests.Session.request""" , return_value=snake_case ) as mock_head:
snake_case_ :Tuple = cached_file(snake_case , """conf""" , _raise_exceptions_for_connection_errors=snake_case )
self.assertIsNone(snake_case )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase_ ( self: str ) -> Tuple:
self.assertTrue(has_file("""hf-internal-testing/tiny-bert-pt-only""" , snake_case ) )
self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only""" , snake_case ) )
self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only""" , snake_case ) )
def lowerCAmelCase_ ( self: List[Any] ) -> List[str]:
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo("""bert-base-cased""" , """ahah.txt""" ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(snake_case , """is not a valid model identifier""" ):
get_file_from_repo("""bert-base-case""" , snake_case )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(snake_case , """is not a valid git identifier""" ):
get_file_from_repo("""bert-base-cased""" , snake_case , revision="""ahaha""" )
snake_case_ :Optional[Any] = get_file_from_repo("""bert-base-cased""" , snake_case )
# The name is the cached name which is not very easy to test, so instead we load the content.
snake_case_ :int = json.loads(open(snake_case , """r""" ).read() )
self.assertEqual(config["""hidden_size"""] , 768 )
def lowerCAmelCase_ ( self: List[Any] ) -> str:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ :Union[str, Any] = Path(snake_case ) / """a.txt"""
filename.touch()
self.assertEqual(get_file_from_repo(snake_case , """a.txt""" ) , str(snake_case ) )
self.assertIsNone(get_file_from_repo(snake_case , """b.txt""" ) )
| 66 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
_UpperCAmelCase : Optional[Any] =NewType("""DataClass""", Any)
_UpperCAmelCase : Dict =NewType("""DataClassType""", Any)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def lowerCAmelCase ( lowerCAmelCase_ )-> Callable[[str], Any]:
lowerCAmelCase_ : str = {str(lowerCAmelCase_ ): choice for choice in choices}
return lambda lowerCAmelCase_ : str_to_choice.get(lowerCAmelCase_ , lowerCAmelCase_ )
def lowerCAmelCase ( *,
lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = dataclasses.MISSING , lowerCAmelCase_ = dataclasses.MISSING , lowerCAmelCase_ = None , **lowerCAmelCase_ , )-> dataclasses.Field:
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
lowerCAmelCase_ : Dict = {}
if aliases is not None:
lowerCAmelCase_ : str = aliases
if help is not None:
lowerCAmelCase_ : Tuple = help
return dataclasses.field(metadata=lowerCAmelCase_ , default=lowerCAmelCase_ , default_factory=lowerCAmelCase_ , **lowerCAmelCase_ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Iterable[DataClassType]
def __init__( self , __lowercase , **__lowercase ) -> List[str]:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
lowerCAmelCase_ : Optional[int] = ArgumentDefaultsHelpFormatter
super().__init__(**__lowercase )
if dataclasses.is_dataclass(__lowercase ):
lowerCAmelCase_ : Union[str, Any] = [dataclass_types]
lowerCAmelCase_ : List[Any] = list(__lowercase )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__lowercase )
@staticmethod
def lowercase_ ( __lowercase , __lowercase ) -> Union[str, Any]:
lowerCAmelCase_ : Optional[Any] = f"""--{field.name}"""
lowerCAmelCase_ : Tuple = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __lowercase ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
lowerCAmelCase_ : List[str] = kwargs.pop('''aliases''' , [] )
if isinstance(__lowercase , __lowercase ):
lowerCAmelCase_ : Optional[Any] = [aliases]
lowerCAmelCase_ : Any = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__lowercase , '''UnionType''' ) and isinstance(__lowercase , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__lowercase ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__lowercase ) not in field.type.__args__:
# filter `str` in Union
lowerCAmelCase_ : List[Any] = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
lowerCAmelCase_ : Dict = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
lowerCAmelCase_ : str = (
field.type.__args__[0] if isinstance(__lowercase , field.type.__args__[1] ) else field.type.__args__[1]
)
lowerCAmelCase_ : List[Any] = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
lowerCAmelCase_ : Dict = {}
if origin_type is Literal or (isinstance(field.type , __lowercase ) and issubclass(field.type , __lowercase )):
if origin_type is Literal:
lowerCAmelCase_ : Optional[Any] = field.type.__args__
else:
lowerCAmelCase_ : int = [x.value for x in field.type]
lowerCAmelCase_ : str = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
lowerCAmelCase_ : str = field.default
else:
lowerCAmelCase_ : Tuple = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
lowerCAmelCase_ : Tuple = copy(__lowercase )
# Hack because type=bool in argparse does not behave as we want.
lowerCAmelCase_ : Dict = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
lowerCAmelCase_ : Union[str, Any] = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
lowerCAmelCase_ : List[str] = default
# This tells argparse we accept 0 or 1 value after --field_name
lowerCAmelCase_ : int = '''?'''
# This is the value that will get picked if we do --field_name (without value)
lowerCAmelCase_ : List[Any] = True
elif isclass(__lowercase ) and issubclass(__lowercase , __lowercase ):
lowerCAmelCase_ : Union[str, Any] = field.type.__args__[0]
lowerCAmelCase_ : Dict = '''+'''
if field.default_factory is not dataclasses.MISSING:
lowerCAmelCase_ : Any = field.default_factory()
elif field.default is dataclasses.MISSING:
lowerCAmelCase_ : Optional[int] = True
else:
lowerCAmelCase_ : List[Any] = field.type
if field.default is not dataclasses.MISSING:
lowerCAmelCase_ : Dict = field.default
elif field.default_factory is not dataclasses.MISSING:
lowerCAmelCase_ : List[Any] = field.default_factory()
else:
lowerCAmelCase_ : int = True
parser.add_argument(__lowercase , *__lowercase , **__lowercase )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
lowerCAmelCase_ : Any = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__lowercase )
def lowercase_ ( self , __lowercase ) -> List[Any]:
if hasattr(__lowercase , '''_argument_group_name''' ):
lowerCAmelCase_ : str = self.add_argument_group(dtype._argument_group_name )
else:
lowerCAmelCase_ : Dict = self
try:
lowerCAmelCase_ : Dict[str, type] = get_type_hints(__lowercase )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 1_0) and "unsupported operand type(s) for |" in str(__lowercase ):
lowerCAmelCase_ : Any = '''.'''.join(map(__lowercase , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__lowercase ):
if not field.init:
continue
lowerCAmelCase_ : Optional[int] = type_hints[field.name]
self._parse_dataclass_field(__lowercase , __lowercase )
def lowercase_ ( self , __lowercase=None , __lowercase=False , __lowercase=True , __lowercase=None , __lowercase=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
lowerCAmelCase_ : str = []
if args_filename:
args_files.append(Path(__lowercase ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
lowerCAmelCase_ : str = ArgumentParser()
args_file_parser.add_argument(__lowercase , type=__lowercase , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = args_file_parser.parse_known_args(args=__lowercase )
lowerCAmelCase_ : int = vars(__lowercase ).get(args_file_flag.lstrip('''-''' ) , __lowercase )
if cmd_args_file_paths:
args_files.extend([Path(__lowercase ) for p in cmd_args_file_paths] )
lowerCAmelCase_ : Dict = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
lowerCAmelCase_ : Any = file_args + args if args is not None else file_args + sys.argv[1:]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = self.parse_known_args(args=__lowercase )
lowerCAmelCase_ : Any = []
for dtype in self.dataclass_types:
lowerCAmelCase_ : str = {f.name for f in dataclasses.fields(__lowercase ) if f.init}
lowerCAmelCase_ : str = {k: v for k, v in vars(__lowercase ).items() if k in keys}
for k in keys:
delattr(__lowercase , __lowercase )
lowerCAmelCase_ : Optional[int] = dtype(**__lowercase )
outputs.append(__lowercase )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__lowercase )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
lowerCAmelCase_ : int = set(args.keys() )
lowerCAmelCase_ : str = []
for dtype in self.dataclass_types:
lowerCAmelCase_ : int = {f.name for f in dataclasses.fields(__lowercase ) if f.init}
lowerCAmelCase_ : List[str] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
lowerCAmelCase_ : List[str] = dtype(**__lowercase )
outputs.append(__lowercase )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__lowercase )}""" )
return tuple(__lowercase )
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
with open(Path(__lowercase ) , encoding='''utf-8''' ) as open_json_file:
lowerCAmelCase_ : Dict = json.loads(open_json_file.read() )
lowerCAmelCase_ : str = self.parse_dict(__lowercase , allow_extra_keys=__lowercase )
return tuple(__lowercase )
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
lowerCAmelCase_ : Optional[Any] = self.parse_dict(yaml.safe_load(Path(__lowercase ).read_text() ) , allow_extra_keys=__lowercase )
return tuple(__lowercase ) | 262 | 0 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
__UpperCAmelCase =logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__ )
class a__ ( UpperCAmelCase__ ):
def __init__( self : Optional[Any] , *a : Union[str, Any] , **a : Tuple ):
"""simple docstring"""
super().__init__(*a , **a )
requires_backends(self , '''decord''' )
self.check_model_type(a )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , a : Optional[int]=None , a : str=None , a : List[str]=None ):
"""simple docstring"""
__lowerCamelCase = {}
if frame_sampling_rate is not None:
__lowerCamelCase = frame_sampling_rate
if num_frames is not None:
__lowerCamelCase = num_frames
__lowerCamelCase = {}
if top_k is not None:
__lowerCamelCase = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : Optional[Any] , a : Union[str, List[str]] , **a : Union[str, Any] ):
"""simple docstring"""
return super().__call__(a , **a )
def SCREAMING_SNAKE_CASE__ ( self : int , a : List[Any] , a : Any=None , a : Optional[int]=1 ):
"""simple docstring"""
if num_frames is None:
__lowerCamelCase = self.model.config.num_frames
if video.startswith('''http://''' ) or video.startswith('''https://''' ):
__lowerCamelCase = BytesIO(requests.get(a ).content )
__lowerCamelCase = VideoReader(a )
videoreader.seek(0 )
__lowerCamelCase = 0
__lowerCamelCase = num_frames * frame_sampling_rate - 1
__lowerCamelCase = np.linspace(a , a , num=a , dtype=np.intaa )
__lowerCamelCase = videoreader.get_batch(a ).asnumpy()
__lowerCamelCase = list(a )
__lowerCamelCase = self.image_processor(a , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE__ ( self : List[str] , a : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase = self.model(**a )
return model_outputs
def SCREAMING_SNAKE_CASE__ ( self : Dict , a : Any , a : List[str]=5 ):
"""simple docstring"""
if top_k > self.model.config.num_labels:
__lowerCamelCase = self.model.config.num_labels
if self.framework == "pt":
__lowerCamelCase = model_outputs.logits.softmax(-1 )[0]
__lowerCamelCase , __lowerCamelCase = probs.topk(a )
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
__lowerCamelCase = scores.tolist()
__lowerCamelCase = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a , a )]
| 67 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCAmelCase ( lowerCAmelCase_ )-> Union[str, Any]:
return EnvironmentCommand()
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def lowercase_ ( __lowercase ) -> List[Any]:
lowerCAmelCase_ : List[str] = parser.add_parser('''env''' )
download_parser.set_defaults(func=__lowercase )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Optional[Any] = huggingface_hub.__version__
lowerCAmelCase_ : str = '''not installed'''
lowerCAmelCase_ : str = '''NA'''
if is_torch_available():
import torch
lowerCAmelCase_ : Any = torch.__version__
lowerCAmelCase_ : str = torch.cuda.is_available()
lowerCAmelCase_ : List[str] = '''not installed'''
if is_transformers_available():
import transformers
lowerCAmelCase_ : Any = transformers.__version__
lowerCAmelCase_ : Optional[Any] = '''not installed'''
if is_accelerate_available():
import accelerate
lowerCAmelCase_ : List[Any] = accelerate.__version__
lowerCAmelCase_ : List[str] = '''not installed'''
if is_xformers_available():
import xformers
lowerCAmelCase_ : Optional[Any] = xformers.__version__
lowerCAmelCase_ : int = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(__lowercase ) )
return info
@staticmethod
def lowercase_ ( __lowercase ) -> str:
return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n" | 262 | 0 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 42
__lowerCamelCase = None
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[Any]=0.999 , SCREAMING_SNAKE_CASE_: str="cosine" , ) -> Optional[int]:
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(SCREAMING_SNAKE_CASE_: Optional[int] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(SCREAMING_SNAKE_CASE_: List[Any] ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
A__ = []
for i in range(SCREAMING_SNAKE_CASE_ ):
A__ = i / num_diffusion_timesteps
A__ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_ ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) )
return torch.tensor(SCREAMING_SNAKE_CASE_ , dtype=torch.floataa )
class a__ ( snake_case , snake_case ):
"""simple docstring"""
__lowerCamelCase = 1
@register_to_config
def __init__( self , lowercase = 1000 , lowercase = 0.0001 , lowercase = 0.02 , lowercase = "linear" , lowercase = None , lowercase = True , lowercase = True , lowercase = 0 , lowercase = "epsilon" , lowercase = 1.0 , **lowercase , ) -> Union[str, Any]:
'''simple docstring'''
if kwargs.get("set_alpha_to_one" , lowercase ) is not None:
A__ = (
"The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
)
deprecate("set_alpha_to_one" , "1.0.0" , lowercase , standard_warn=lowercase )
A__ = kwargs["set_alpha_to_one"]
if trained_betas is not None:
A__ = torch.tensor(lowercase , dtype=torch.floataa )
elif beta_schedule == "linear":
A__ = torch.linspace(lowercase , lowercase , lowercase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
A__ = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowercase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
A__ = betas_for_alpha_bar(lowercase )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
A__ = 1.0 - self.betas
A__ = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
A__ = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
A__ = 1.0
# setable values
A__ = None
A__ = torch.from_numpy(np.arange(0 , lowercase ).copy().astype(np.intaa ) )
def UpperCamelCase ( self , lowercase , lowercase = None ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def UpperCamelCase ( self , lowercase , lowercase = None ) -> Optional[int]:
'''simple docstring'''
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
F' maximal {self.config.num_train_timesteps} timesteps.' )
A__ = num_inference_steps
A__ = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
A__ = (np.arange(0 , lowercase ) * step_ratio).round().copy().astype(np.intaa )
A__ = torch.from_numpy(lowercase ).to(lowercase )
self.timesteps += self.config.steps_offset
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase = 0.0 , lowercase = False , lowercase = None , lowercase = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
'''simple docstring'''
A__ = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
A__ = self.alphas_cumprod[timestep]
A__ = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
A__ = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
A__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
A__ = model_output
elif self.config.prediction_type == "sample":
A__ = model_output
A__ = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
A__ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
A__ = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
" `v_prediction`" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
A__ = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
A__ = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
A__ = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=lowercase , pred_original_sample=lowercase )
def __len__( self ) -> Optional[Any]:
'''simple docstring'''
return self.config.num_train_timesteps
| 68 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = JukeboxTokenizer
SCREAMING_SNAKE_CASE__ : int = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
import torch
lowerCAmelCase_ : Union[str, Any] = JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
lowerCAmelCase_ : Any = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : List[str] = [
torch.tensor([[
0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
7_6, 7_6]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def lowercase_ ( self ) -> List[Any]:
import torch
lowerCAmelCase_ : Any = JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
lowerCAmelCase_ : str = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : Tuple = [
torch.tensor([[
0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 7_7, 7_7]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) ) | 262 | 0 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def UpperCAmelCase ( ) -> List[str]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(UpperCAmelCase ):
requests.request('GET' , 'https://huggingface.co' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('GET' , 'https://huggingface.co' , timeout=1.0 )
@pytest.mark.integration
def UpperCAmelCase ( ) -> Dict:
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('GET' , 'https://huggingface.co' )
def UpperCAmelCase ( ) -> List[Any]:
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(UpperCAmelCase ):
http_head('https://huggingface.co' )
| 69 |
from __future__ import annotations
import requests
def lowerCAmelCase ( lowerCAmelCase_ )-> dict:
lowerCAmelCase_ : List[Any] = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(lowerCAmelCase_ ).json()
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> list[dict]:
lowerCAmelCase_ : List[Any] = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
lowerCAmelCase_ : Tuple = requests.get(lowerCAmelCase_ ).json()[:max_stories]
return [get_hackernews_story(lowerCAmelCase_ ) for story_id in story_ids]
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> str:
lowerCAmelCase_ : Optional[Any] = hackernews_top_stories(lowerCAmelCase_ )
return "\n".join('''* [{title}]({url})'''.format(**lowerCAmelCase_ ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown()) | 262 | 0 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A__ : Optional[Any] =np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
A__ : Optional[int] =[0, 25, 50]
A__ : Dict =[25, 50, 75]
A__ : Optional[int] =fuzz.membership.trimf(X, abca)
A__ : List[Any] =fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
A__ : Tuple =np.ones(75)
A__ : Dict =np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
A__ : Optional[int] =fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
A__ : str =fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
A__ : Any =fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
A__ : Dict =fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
A__ : List[Any] =young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
A__ : Optional[Any] =young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
A__ : Dict =fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
A__ : Optional[Any] =fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('''Young''')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('''Middle aged''')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('''union''')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('''intersection''')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('''complement_a''')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('''difference a/b''')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('''alg_sum''')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('''alg_product''')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('''bdd_sum''')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('''bdd_difference''')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 70 |
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
_UpperCAmelCase : List[str] =get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_UpperCAmelCase : Optional[int] =25_0004
_UpperCAmelCase : Tuple =25_0020
@require_sentencepiece
@require_tokenizers
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = MBartTokenizer
SCREAMING_SNAKE_CASE__ : Dict = MBartTokenizerFast
SCREAMING_SNAKE_CASE__ : Tuple = True
SCREAMING_SNAKE_CASE__ : List[str] = True
def lowercase_ ( self ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase_ : str = MBartTokenizer(__lowercase , keep_accents=__lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[int] = MBartTokenizer(__lowercase , keep_accents=__lowercase )
lowerCAmelCase_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowerCAmelCase_ : Optional[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [
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''',
'''.''',
] , )
lowerCAmelCase_ : Dict = tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(
__lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCAmelCase_ : Union[str, Any] = tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [
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 lowercase_ ( 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
lowerCAmelCase_ : int = (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})""" ):
lowerCAmelCase_ : Optional[int] = self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : int = self.tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = tempfile.mkdtemp()
lowerCAmelCase_ : Union[str, Any] = tokenizer_r.save_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.save_pretrained(__lowercase )
# 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 ) )
lowerCAmelCase_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Tuple = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=True
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : int = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Tuple = tokenizer_p.save_pretrained(__lowercase )
# Checks it save with the same files
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Optional[int] = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=False
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Optional[int] = tokenizer_p.save_pretrained(__lowercase )
# 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
lowerCAmelCase_ : Dict = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = """facebook/mbart-large-en-ro"""
SCREAMING_SNAKE_CASE__ : int = [
""" 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.""",
]
SCREAMING_SNAKE_CASE__ : Optional[int] = [
"""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.""",
]
SCREAMING_SNAKE_CASE__ : str = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def lowercase_ ( cls ) -> Optional[int]:
lowerCAmelCase_ : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
lowerCAmelCase_ : Optional[Any] = 1
return cls
def lowercase_ ( self ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 2_5_0_0_2_0 )
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
def lowercase_ ( self ) -> Any:
self.assertIn(__lowercase , self.tokenizer.all_special_ids )
lowerCAmelCase_ : Union[str, Any] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
lowerCAmelCase_ : Tuple = self.tokenizer.decode(__lowercase , skip_special_tokens=__lowercase )
lowerCAmelCase_ : List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertNotIn(self.tokenizer.eos_token , __lowercase )
def lowercase_ ( self ) -> Any:
lowerCAmelCase_ : Union[str, Any] = ['''this is gunna be a long sentence ''' * 2_0]
assert isinstance(src_text[0] , __lowercase )
lowerCAmelCase_ : str = 1_0
lowerCAmelCase_ : Tuple = self.tokenizer(__lowercase , max_length=__lowercase , truncation=__lowercase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __lowercase )
self.assertEqual(len(__lowercase ) , __lowercase )
def lowercase_ ( self ) -> int:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = tempfile.mkdtemp()
lowerCAmelCase_ : int = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = MBartTokenizer.from_pretrained(__lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __lowercase )
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Tuple = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowercase , return_tensors='''pt''' )
lowerCAmelCase_ : Tuple = 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 lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
lowerCAmelCase_ : int = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
lowerCAmelCase_ : str = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
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 lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = self.tokenizer(self.src_text , padding=__lowercase , truncation=__lowercase , max_length=3 , return_tensors='''pt''' )
lowerCAmelCase_ : Any = self.tokenizer(
text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=1_0 , return_tensors='''pt''' )
lowerCAmelCase_ : int = targets['''input_ids''']
lowerCAmelCase_ : Optional[Any] = shift_tokens_right(__lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(__lowercase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 2_5_0_0_0_1,
} , ) | 262 | 0 |
def A ( a_ ,a_ ,a_ ) -> Tuple:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a_ ,n - 1 ,a_ ) * a) % mod
else:
__UpperCamelCase : Dict =binary_exponentiation(a_ ,n / 2 ,a_ )
return (b * b) % mod
# a prime number
A_ :str = 701
A_ :Optional[Any] = 1000000000
A_ :int = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 71 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = "cpu" , lowerCAmelCase_ = None )-> None:
lowerCAmelCase_ : str = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase_ , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowerCAmelCase_ : int = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Tuple = src_path
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
fire.Fire(convert) | 262 | 0 |
"""simple docstring"""
lowerCAmelCase__ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowerCAmelCase__ = {value: key for key, value in MORSE_CODE_DICT.items()}
def snake_case_ ( A_ : str ):
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def snake_case_ ( A_ : str ):
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = '''Morse code here!'''
print(A_ )
_lowerCamelCase : List[str] = encrypt(A_ )
print(A_ )
_lowerCamelCase : Any = decrypt(A_ )
print(A_ )
if __name__ == "__main__":
main()
| 72 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel | 262 | 0 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Union[str, Any] = ['''torch''', '''torchsde''']
def __init__( self : Union[str, Any] ,*SCREAMING_SNAKE_CASE__ : int ,**SCREAMING_SNAKE_CASE__ : Optional[int]):
requires_backends(self ,['torch', 'torchsde'])
@classmethod
def lowerCAmelCase ( cls : Dict ,*SCREAMING_SNAKE_CASE__ : Optional[Any] ,**SCREAMING_SNAKE_CASE__ : Optional[int]):
requires_backends(cls ,['torch', 'torchsde'])
@classmethod
def lowerCAmelCase ( cls : Any ,*SCREAMING_SNAKE_CASE__ : str ,**SCREAMING_SNAKE_CASE__ : List[Any]):
requires_backends(cls ,['torch', 'torchsde'])
| 73 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : str ={
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = """vit_mae"""
def __init__( self , __lowercase=7_6_8 , __lowercase=1_2 , __lowercase=1_2 , __lowercase=3_0_7_2 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1e-12 , __lowercase=2_2_4 , __lowercase=1_6 , __lowercase=3 , __lowercase=True , __lowercase=1_6 , __lowercase=5_1_2 , __lowercase=8 , __lowercase=2_0_4_8 , __lowercase=0.75 , __lowercase=False , **__lowercase , ) -> str:
super().__init__(**__lowercase )
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Any = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : int = intermediate_size
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : int = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : Union[str, Any] = image_size
lowerCAmelCase_ : Optional[int] = patch_size
lowerCAmelCase_ : Tuple = num_channels
lowerCAmelCase_ : List[str] = qkv_bias
lowerCAmelCase_ : List[Any] = decoder_num_attention_heads
lowerCAmelCase_ : int = decoder_hidden_size
lowerCAmelCase_ : Optional[int] = decoder_num_hidden_layers
lowerCAmelCase_ : Tuple = decoder_intermediate_size
lowerCAmelCase_ : Tuple = mask_ratio
lowerCAmelCase_ : Any = norm_pix_loss | 262 | 0 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'''
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''spm_file''': '''sentencepiece.bpe.model''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
),
},
'''spm_file''': {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
)
},
}
_lowercase = {
'''facebook/s2t-small-librispeech-asr''': 10_24,
}
_lowercase = ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']
_lowercase = {'''mustc''': MUSTC_LANGS}
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: int = VOCAB_FILES_NAMES
_lowerCamelCase: List[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase: Any = MAX_MODEL_INPUT_SIZES
_lowerCamelCase: Optional[Any] = ['''input_ids''', '''attention_mask''']
_lowerCamelCase: List[int] = []
def __init__( self : Optional[Any] ,A_ : int ,A_ : Optional[Any] ,A_ : List[str]="<s>" ,A_ : Union[str, Any]="</s>" ,A_ : Dict="<pad>" ,A_ : Dict="<unk>" ,A_ : List[str]=False ,A_ : Tuple=False ,A_ : Optional[int]=None ,A_ : Dict=None ,A_ : Optional[Dict[str, Any]] = None ,**A_ : Union[str, Any] ,) -> None:
A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A_ ,eos_token=A_ ,unk_token=A_ ,pad_token=A_ ,do_upper_case=A_ ,do_lower_case=A_ ,tgt_lang=A_ ,lang_codes=A_ ,sp_model_kwargs=self.sp_model_kwargs ,**A_ ,)
A = do_upper_case
A = do_lower_case
A = load_json(A_ )
A = {v: k for k, v in self.encoder.items()}
A = spm_file
A = load_spm(A_ ,self.sp_model_kwargs )
if lang_codes is not None:
A = lang_codes
A = LANGUAGES[lang_codes]
A = [F'<lang:{lang}>' for lang in self.langs]
A = {lang: self.sp_model.PieceToId(F'<lang:{lang}>' ) for lang in self.langs}
A = self.lang_tokens
A = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
A = {}
@property
def _SCREAMING_SNAKE_CASE ( self : Any ) -> int:
return len(self.encoder )
@property
def _SCREAMING_SNAKE_CASE ( self : int ) -> str:
return self._tgt_lang
@tgt_lang.setter
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : List[str] ) -> None:
A = new_tgt_lang
self.set_tgt_lang_special_tokens(A_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : str ) -> None:
A = self.lang_code_to_id[tgt_lang]
A = [lang_code_id]
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : str ) -> List[str]:
return self.sp_model.encode(A_ ,out_type=A_ )
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : List[str] ) -> Union[str, Any]:
return self.encoder.get(A_ ,self.encoder[self.unk_token] )
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : int ) -> str:
return self.decoder.get(A_ ,self.unk_token )
def _SCREAMING_SNAKE_CASE ( self : str ,A_ : List[str] ) -> str:
A = []
A = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
A = self.sp_model.decode(A_ )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
A = []
else:
current_sub_tokens.append(A_ )
A = self.sp_model.decode(A_ )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : List[str] ,A_ : Optional[Any]=None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : List[int] ,A_ : Optional[List[int]] = None ,A_ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ ,token_ids_a=A_ ,already_has_special_tokens=A_ )
A = [1] * len(self.prefix_tokens )
A = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(A_ )) + suffix_ones
return prefix_ones + ([0] * len(A_ )) + ([0] * len(A_ )) + suffix_ones
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Dict:
A = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : int ) -> Dict:
A = self.__dict__.copy()
A = None
return state
def __setstate__( self : List[str] ,A_ : Dict ) -> None:
A = d
# for backward compatibility
if not hasattr(self ,'sp_model_kwargs' ):
A = {}
A = load_spm(self.spm_file ,self.sp_model_kwargs )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : str ,A_ : Optional[str] = None ) -> Tuple[str]:
A = Path(A_ )
assert save_dir.is_dir(), F'{save_directory} should be a directory'
A = save_dir / (
(filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
)
A = save_dir / (
(filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
)
save_json(self.encoder ,A_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(A_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file ,A_ )
elif not os.path.isfile(self.spm_file ):
with open(A_ ,'wb' ) as fi:
A = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (str(A_ ), str(A_ ))
def _snake_case ( snake_case__ : str , snake_case__ : Dict[str, Any] ):
A = sentencepiece.SentencePieceProcessor(**snake_case__ )
spm.Load(str(snake_case__ ) )
return spm
def _snake_case ( snake_case__ : str ):
with open(snake_case__ , 'r' ) as f:
return json.load(snake_case__ )
def _snake_case ( snake_case__ : int , snake_case__ : str ):
with open(snake_case__ , 'w' ) as f:
json.dump(snake_case__ , snake_case__ , indent=2 ) | 74 |
def lowerCAmelCase ( lowerCAmelCase_ = 10**9 )-> int:
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[int] = 2
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : str = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
lowerCAmelCase_ : Any = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""") | 262 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ : int = logging.get_logger(__name__)
a_ : Tuple = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : int ='table-transformer'
lowercase : Union[str, Any] =['past_key_values']
lowercase : str ={
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self, lowerCAmelCase=True, lowerCAmelCase=None, lowerCAmelCase=3, lowerCAmelCase=100, lowerCAmelCase=6, lowerCAmelCase=2_048, lowerCAmelCase=8, lowerCAmelCase=6, lowerCAmelCase=2_048, lowerCAmelCase=8, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=True, lowerCAmelCase="relu", lowerCAmelCase=256, lowerCAmelCase=0.1, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=0.0_2, lowerCAmelCase=1.0, lowerCAmelCase=False, lowerCAmelCase="sine", lowerCAmelCase="resnet50", lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase=1, lowerCAmelCase=5, lowerCAmelCase=2, lowerCAmelCase=1, lowerCAmelCase=1, lowerCAmelCase=5, lowerCAmelCase=2, lowerCAmelCase=0.1, **lowerCAmelCase, ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowerCamelCase_ =CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =backbone_config.get('''model_type''' )
lowerCamelCase_ =CONFIG_MAPPING[backbone_model_type]
lowerCamelCase_ =config_class.from_dict(lowerCAmelCase )
# set timm attributes to None
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =None, None, None
lowerCamelCase_ =use_timm_backbone
lowerCamelCase_ =backbone_config
lowerCamelCase_ =num_channels
lowerCamelCase_ =num_queries
lowerCamelCase_ =d_model
lowerCamelCase_ =encoder_ffn_dim
lowerCamelCase_ =encoder_layers
lowerCamelCase_ =encoder_attention_heads
lowerCamelCase_ =decoder_ffn_dim
lowerCamelCase_ =decoder_layers
lowerCamelCase_ =decoder_attention_heads
lowerCamelCase_ =dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =activation_function
lowerCamelCase_ =init_std
lowerCamelCase_ =init_xavier_std
lowerCamelCase_ =encoder_layerdrop
lowerCamelCase_ =decoder_layerdrop
lowerCamelCase_ =encoder_layers
lowerCamelCase_ =auxiliary_loss
lowerCamelCase_ =position_embedding_type
lowerCamelCase_ =backbone
lowerCamelCase_ =use_pretrained_backbone
lowerCamelCase_ =dilation
# Hungarian matcher
lowerCamelCase_ =class_cost
lowerCamelCase_ =bbox_cost
lowerCamelCase_ =giou_cost
# Loss coefficients
lowerCamelCase_ =mask_loss_coefficient
lowerCamelCase_ =dice_loss_coefficient
lowerCamelCase_ =bbox_loss_coefficient
lowerCamelCase_ =giou_loss_coefficient
lowerCamelCase_ =eos_coefficient
super().__init__(is_encoder_decoder=lowerCAmelCase, **lowerCAmelCase )
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.d_model
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Tuple =version.parse('1.11' )
@property
def lowercase__ ( self ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def lowercase__ ( self ):
"""simple docstring"""
return 1e-5
@property
def lowercase__ ( self ):
"""simple docstring"""
return 12
| 75 |
import inspect
import unittest
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def lowercase_ ( self ) -> List[str]:
import diffusers
from diffusers.dependency_versions_table import deps
lowerCAmelCase_ : Any = inspect.getmembers(__lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowerCAmelCase_ : Optional[int] = '''k-diffusion'''
elif backend == "invisible_watermark":
lowerCAmelCase_ : Dict = '''invisible-watermark'''
assert backend in deps, f"""{backend} is not in the deps table!""" | 262 | 0 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[str] = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
SCREAMING_SNAKE_CASE : Dict = 128
elif "12-12" in model_name:
SCREAMING_SNAKE_CASE : List[str] = 12
SCREAMING_SNAKE_CASE : Optional[int] = 12
elif "14-14" in model_name:
SCREAMING_SNAKE_CASE : Union[str, Any] = 14
SCREAMING_SNAKE_CASE : Tuple = 14
elif "16-16" in model_name:
SCREAMING_SNAKE_CASE : Optional[Any] = 16
SCREAMING_SNAKE_CASE : str = 16
else:
raise ValueError("Model not supported")
SCREAMING_SNAKE_CASE : Optional[Any] = "huggingface/label-files"
if "speech-commands" in model_name:
SCREAMING_SNAKE_CASE : Union[str, Any] = 35
SCREAMING_SNAKE_CASE : Tuple = "speech-commands-v2-id2label.json"
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = 527
SCREAMING_SNAKE_CASE : str = "audioset-id2label.json"
SCREAMING_SNAKE_CASE : List[str] = json.load(open(hf_hub_download(_a , _a , repo_type="dataset") , "r"))
SCREAMING_SNAKE_CASE : Union[str, Any] = {int(_a): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : Optional[Any] = idalabel
SCREAMING_SNAKE_CASE : Tuple = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase__ ( _a):
if "module.v" in name:
SCREAMING_SNAKE_CASE : int = name.replace("module.v" , "audio_spectrogram_transformer")
if "cls_token" in name:
SCREAMING_SNAKE_CASE : List[Any] = name.replace("cls_token" , "embeddings.cls_token")
if "dist_token" in name:
SCREAMING_SNAKE_CASE : List[str] = name.replace("dist_token" , "embeddings.distillation_token")
if "pos_embed" in name:
SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("pos_embed" , "embeddings.position_embeddings")
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE : str = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection")
# transformer blocks
if "blocks" in name:
SCREAMING_SNAKE_CASE : List[str] = name.replace("blocks" , "encoder.layer")
if "attn.proj" in name:
SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("attn.proj" , "attention.output.dense")
if "attn" in name:
SCREAMING_SNAKE_CASE : List[Any] = name.replace("attn" , "attention.self")
if "norm1" in name:
SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("norm1" , "layernorm_before")
if "norm2" in name:
SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("norm2" , "layernorm_after")
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE : List[str] = name.replace("mlp.fc1" , "intermediate.dense")
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE : Tuple = name.replace("mlp.fc2" , "output.dense")
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("audio_spectrogram_transformer.norm" , "audio_spectrogram_transformer.layernorm")
# classifier head
if "module.mlp_head.0" in name:
SCREAMING_SNAKE_CASE : str = name.replace("module.mlp_head.0" , "classifier.layernorm")
if "module.mlp_head.1" in name:
SCREAMING_SNAKE_CASE : Dict = name.replace("module.mlp_head.1" , "classifier.dense")
return name
def lowerCamelCase__ ( _a , _a):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE : List[Any] = orig_state_dict.pop(_a)
if "qkv" in key:
SCREAMING_SNAKE_CASE : str = key.split(".")
SCREAMING_SNAKE_CASE : List[str] = int(key_split[3])
SCREAMING_SNAKE_CASE : Union[str, Any] = config.hidden_size
if "weight" in key:
SCREAMING_SNAKE_CASE : str = val[:dim, :]
SCREAMING_SNAKE_CASE : str = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE : List[str] = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE : Optional[int] = val[:dim]
SCREAMING_SNAKE_CASE : Any = val[dim : dim * 2]
SCREAMING_SNAKE_CASE : List[str] = val[-dim:]
else:
SCREAMING_SNAKE_CASE : Dict = val
return orig_state_dict
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[Any] = [
"module.v.head.weight",
"module.v.head.bias",
"module.v.head_dist.weight",
"module.v.head_dist.bias",
]
for k in ignore_keys:
state_dict.pop(_a , _a)
@torch.no_grad()
def lowerCamelCase__ ( _a , _a , _a=False):
SCREAMING_SNAKE_CASE : int = get_audio_spectrogram_transformer_config(_a)
SCREAMING_SNAKE_CASE : Optional[Any] = {
"ast-finetuned-audioset-10-10-0.4593": (
"https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.450": (
"https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.448": (
"https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.448-v2": (
"https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"
),
"ast-finetuned-audioset-12-12-0.447": (
"https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"
),
"ast-finetuned-audioset-14-14-0.443": (
"https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"
),
"ast-finetuned-audioset-16-16-0.442": (
"https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"
),
"ast-finetuned-speech-commands-v2": (
"https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"
),
}
# load original state_dict
SCREAMING_SNAKE_CASE : List[str] = model_name_to_url[model_name]
SCREAMING_SNAKE_CASE : List[Any] = torch.hub.load_state_dict_from_url(_a , map_location="cpu")
# remove some keys
remove_keys(_a)
# rename some keys
SCREAMING_SNAKE_CASE : List[str] = convert_state_dict(_a , _a)
# load 🤗 model
SCREAMING_SNAKE_CASE : Dict = ASTForAudioClassification(_a)
model.eval()
model.load_state_dict(_a)
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
SCREAMING_SNAKE_CASE : List[str] = -4.267_7393 if "speech-commands" not in model_name else -6.84_5978
SCREAMING_SNAKE_CASE : Tuple = 4.568_9974 if "speech-commands" not in model_name else 5.565_4526
SCREAMING_SNAKE_CASE : List[Any] = 1024 if "speech-commands" not in model_name else 128
SCREAMING_SNAKE_CASE : Optional[int] = ASTFeatureExtractor(mean=_a , std=_a , max_length=_a)
if "speech-commands" in model_name:
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset("speech_commands" , "v0.02" , split="validation")
SCREAMING_SNAKE_CASE : Any = dataset[0]["audio"]["array"]
else:
SCREAMING_SNAKE_CASE : Dict = hf_hub_download(
repo_id="nielsr/audio-spectogram-transformer-checkpoint" , filename="sample_audio.flac" , repo_type="dataset" , )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = torchaudio.load(_a)
SCREAMING_SNAKE_CASE : Any = waveform.squeeze().numpy()
SCREAMING_SNAKE_CASE : Optional[Any] = feature_extractor(_a , sampling_rate=16000 , return_tensors="pt")
# forward pass
SCREAMING_SNAKE_CASE : Optional[Any] = model(**_a)
SCREAMING_SNAKE_CASE : int = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
SCREAMING_SNAKE_CASE : Any = torch.tensor([-0.8760, -7.0042, -8.6602])
elif model_name == "ast-finetuned-audioset-10-10-0.450":
SCREAMING_SNAKE_CASE : Any = torch.tensor([-1.1986, -7.0903, -8.2718])
elif model_name == "ast-finetuned-audioset-10-10-0.448":
SCREAMING_SNAKE_CASE : str = torch.tensor([-2.6128, -8.0080, -9.4344])
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
SCREAMING_SNAKE_CASE : str = torch.tensor([-1.5080, -7.4534, -8.8917])
elif model_name == "ast-finetuned-audioset-12-12-0.447":
SCREAMING_SNAKE_CASE : str = torch.tensor([-0.5050, -6.5833, -8.0843])
elif model_name == "ast-finetuned-audioset-14-14-0.443":
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([-0.3826, -7.0336, -8.2413])
elif model_name == "ast-finetuned-audioset-16-16-0.442":
SCREAMING_SNAKE_CASE : Any = torch.tensor([-1.2113, -6.9101, -8.3470])
elif model_name == "ast-finetuned-speech-commands-v2":
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([6.1589, -8.0566, -8.7984])
else:
raise ValueError("Unknown model name")
if not torch.allclose(logits[0, :3] , _a , atol=1E-4):
raise ValueError("Logits don't match")
print("Looks ok!")
if pytorch_dump_folder_path is not None:
Path(_a).mkdir(exist_ok=_a)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(_a)
print(f"Saving feature extractor to {pytorch_dump_folder_path}")
feature_extractor.save_pretrained(_a)
if push_to_hub:
print("Pushing model and feature extractor to the hub...")
model.push_to_hub(f"MIT/{model_name}")
feature_extractor.push_to_hub(f"MIT/{model_name}")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
a_ = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 76 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_UpperCAmelCase : Any =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__lowercase , **__lowercase ) -> None:
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __lowercase , )
super().__init__(*__lowercase , **__lowercase ) | 262 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : List[str] = len(_lowerCAmelCase ) + 1
lowercase__ : Any = len(_lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
lowercase__ : List[str] = [[0 for i in range(_lowerCAmelCase )] for j in range(_lowerCAmelCase )]
# since string of zero length match pattern of zero length
lowercase__ : Any = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowerCAmelCase ):
lowercase__ : Tuple = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowerCAmelCase ):
lowercase__ : Union[str, Any] = dp[0][j - 2] if pattern[j - 1] == '*' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _lowerCAmelCase ):
for j in range(1 , _lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
lowercase__ : List[Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
lowercase__ : Union[str, Any] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
lowercase__ : Tuple = dp[i - 1][j]
else:
lowercase__ : Tuple = 0
else:
lowercase__ : List[Any] = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
_UpperCamelCase : Any = "aab"
_UpperCamelCase : int = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'''{input_string} matches the given pattern {pattern}''')
else:
print(f'''{input_string} does not match with the given pattern {pattern}''')
| 77 |
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : Optional[int] = set()
# edges = list of graph's edges
lowerCAmelCase_ : List[str] = get_edges(lowerCAmelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = edges.pop()
chosen_vertices.add(lowerCAmelCase_ )
chosen_vertices.add(lowerCAmelCase_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(lowerCAmelCase_ )
return chosen_vertices
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}") | 262 | 0 |
"""simple docstring"""
import sys
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = len(lowercase_ )
UpperCAmelCase = [[0 for x in range(lowercase_ )] for x in range(lowercase_ )]
UpperCAmelCase = [[0 for x in range(lowercase_ )] for x in range(lowercase_ )]
for chain_length in range(2 , lowercase_ ):
for a in range(1 , n - chain_length + 1 ):
UpperCAmelCase = a + chain_length - 1
UpperCAmelCase = sys.maxsize
for c in range(lowercase_ , lowercase_ ):
UpperCAmelCase = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
UpperCAmelCase = cost
UpperCAmelCase = c
return matrix, sol
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
if i == j:
print('A' + str(lowercase_ ) , end=' ' )
else:
print('(' , end=' ' )
print_optiomal_solution(lowercase_ , lowercase_ , optimal_solution[i][j] )
print_optiomal_solution(lowercase_ , optimal_solution[i][j] + 1 , lowercase_ )
print(')' , end=' ' )
def _lowerCAmelCase ( ):
UpperCAmelCase = [30, 35, 15, 5, 10, 20, 25]
UpperCAmelCase = len(lowercase_ )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
UpperCAmelCase , UpperCAmelCase = matrix_chain_order(lowercase_ )
print('No. of Operation required: ' + str(matrix[1][n - 1] ) )
print_optiomal_solution(lowercase_ , 1 , n - 1 )
if __name__ == "__main__":
main()
| 78 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
lowerCAmelCase_ : Optional[int] = False
for divisor in range(2 , int(round(sqrt(lowerCAmelCase_ ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
lowerCAmelCase_ : Tuple = False
break
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'status' must been from type bool"
return status
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
lowerCAmelCase_ : Tuple = list(range(2 , n + 1 ) )
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(lowerCAmelCase_ ) ):
for j in range(i + 1 , len(lowerCAmelCase_ ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
lowerCAmelCase_ : str = 0
# filters actual prime numbers.
lowerCAmelCase_ : Optional[int] = [x for x in begin_list if x != 0]
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
lowerCAmelCase_ : List[Any] = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(lowerCAmelCase_ ):
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and number >= 0, "'number' must been an int and >= 0"
lowerCAmelCase_ : int = [] # this list will be returns of the function.
# potential prime number factors.
lowerCAmelCase_ : List[Any] = 2
lowerCAmelCase_ : Optional[int] = number
if number == 0 or number == 1:
ans.append(lowerCAmelCase_ )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(lowerCAmelCase_ ):
while quotient != 1:
if is_prime(lowerCAmelCase_ ) and (quotient % factor == 0):
ans.append(lowerCAmelCase_ )
quotient /= factor
else:
factor += 1
else:
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : Dict = 0
# prime factorization of 'number'
lowerCAmelCase_ : Any = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = max(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : List[Any] = 0
# prime factorization of 'number'
lowerCAmelCase_ : Dict = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = min(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 == 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 == 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 != 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 != 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[str]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (number > 2) and is_even(lowerCAmelCase_ )
), "'number' must been an int, even and > 2"
lowerCAmelCase_ : str = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
lowerCAmelCase_ : int = get_prime_numbers(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = len(lowerCAmelCase_ )
# run variable for while-loops.
lowerCAmelCase_ : Union[str, Any] = 0
lowerCAmelCase_ : Tuple = None
# exit variable. for break up the loops
lowerCAmelCase_ : int = True
while i < len_pn and loop:
lowerCAmelCase_ : int = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
lowerCAmelCase_ : Tuple = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (len(lowerCAmelCase_ ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : int = 0
while numbera != 0:
lowerCAmelCase_ : str = numbera % numbera
lowerCAmelCase_ : List[Any] = numbera
lowerCAmelCase_ : Any = rest
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : List[Any] = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
elif numbera == 1 or numbera == 1:
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : Union[str, Any] = max(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Dict = 0
lowerCAmelCase_ : Union[str, Any] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(max(lowerCAmelCase_ , lowerCAmelCase_ ) ):
ans *= n
else:
lowerCAmelCase_ : List[str] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'number' must been a positive int"
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : Optional[int] = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(lowerCAmelCase_ ):
ans += 1
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and is_prime(
lowerCAmelCase_ ), "'ans' must been a prime number and from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
assert (
is_prime(lowerCAmelCase_ ) and is_prime(lowerCAmelCase_ ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
lowerCAmelCase_ : Union[str, Any] = p_number_a + 1 # jump to the next number
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
while number < p_number_a:
ans.append(lowerCAmelCase_ )
number += 1
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and ans[0] != p_number_a
and ans[len(lowerCAmelCase_ ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 1), "'n' must been int and >= 1"
lowerCAmelCase_ : List[Any] = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(lowerCAmelCase_ )
# precondition
assert ans[0] == 1 and ans[len(lowerCAmelCase_ ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number > 1
), "'number' must been an int and >= 1"
lowerCAmelCase_ : Union[str, Any] = get_divisors(lowerCAmelCase_ )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (divisors[0] == 1)
and (divisors[len(lowerCAmelCase_ ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
lowerCAmelCase_ : Optional[Any] = gcd(abs(lowerCAmelCase_ ) , abs(lowerCAmelCase_ ) )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been a int and >= 0"
lowerCAmelCase_ : Any = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been an int and >= 0"
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Union[str, Any] = 1 # this will be return
for _ in range(n - 1 ):
lowerCAmelCase_ : Union[str, Any] = ans
ans += fiba
lowerCAmelCase_ : Optional[Any] = tmp
return ans | 262 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _UpperCAmelCase :
"""simple docstring"""
@staticmethod
def lowerCAmelCase ( *__UpperCAmelCase : Optional[int] , **__UpperCAmelCase : List[Any] ):
'''simple docstring'''
pass
def __lowercase ( __lowercase ) -> Optional[Any]:
'''simple docstring'''
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowerCamelCase_ = (
'''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def lowerCAmelCase ( self : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
_A = pipeline(
"document-question-answering" , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
_A = INVOICE_URL
_A = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , "" ) ) )
_A = "What is the placebo?"
_A = [
{
"image": load_image(__UpperCAmelCase ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : str ):
'''simple docstring'''
_A = dqa_pipeline(__UpperCAmelCase , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
[
{"score": ANY(__UpperCAmelCase ), "answer": ANY(__UpperCAmelCase ), "start": ANY(__UpperCAmelCase ), "end": ANY(__UpperCAmelCase )},
{"score": ANY(__UpperCAmelCase ), "answer": ANY(__UpperCAmelCase ), "start": ANY(__UpperCAmelCase ), "end": ANY(__UpperCAmelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
_A = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
_A = INVOICE_URL
_A = "How many cats are there?"
_A = [
{"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
_A = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
_A = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , __UpperCAmelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
_A = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_A = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
# We can optionnally pass directly the words and bounding boxes
_A = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_A = []
_A = []
_A = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , words=__UpperCAmelCase , boxes=__UpperCAmelCase , top_k=2 )
self.assertEqual(__UpperCAmelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
_A = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
_A = INVOICE_URL
_A = "What is the invoice number?"
_A = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
] , )
_A = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
] , )
_A = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_A = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
_A = INVOICE_URL
_A = "What is the invoice number?"
_A = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
] , )
_A = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
] , )
_A = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
_A = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__UpperCAmelCase )
_A = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__UpperCAmelCase , revision="3dc6de3" , )
_A = INVOICE_URL
_A = "What is the invoice number?"
_A = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
_A = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
_A = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
_A = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , "" ) ) )
# This model should also work if `image` is set to None
_A = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def lowerCAmelCase ( self : int ):
'''simple docstring'''
_A = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=__UpperCAmelCase )
_A = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=__UpperCAmelCase , revision="3dc6de3" , max_seq_len=50 , )
_A = INVOICE_URL
_A = "What is the invoice number?"
_A = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
] , )
_A = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
_A = list(zip(*apply_tesseract(load_image(__UpperCAmelCase ) , __UpperCAmelCase , "" ) ) )
# This model should also work if `image` is set to None
_A = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def lowerCAmelCase ( self : int ):
'''simple docstring'''
_A = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
_A = INVOICE_URL
_A = "What is the invoice number?"
_A = dqa_pipeline(image=__UpperCAmelCase , question=__UpperCAmelCase , top_k=2 )
self.assertEqual(nested_simplify(__UpperCAmelCase , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
pass
| 79 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
_UpperCAmelCase : Union[str, Any] ="""pt"""
elif is_tf_available():
_UpperCAmelCase : List[Any] ="""tf"""
else:
_UpperCAmelCase : Optional[int] ="""jax"""
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = PerceiverTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def lowercase_ ( self ) -> Optional[int]:
super().setUp()
lowerCAmelCase_ : str = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase_ ( self ) -> Any:
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def lowercase_ ( self , **__lowercase ) -> PerceiverTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowercase )
def lowercase_ ( self , __lowercase , __lowercase=False , __lowercase=2_0 , __lowercase=5 ) -> Tuple[str, list]:
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for Perceiver 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.
lowerCAmelCase_ : Optional[Any] = []
for i in range(len(__lowercase ) ):
try:
lowerCAmelCase_ : List[str] = tokenizer.decode([i] , clean_up_tokenization_spaces=__lowercase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCAmelCase_ : List[str] = list(filter(lambda __lowercase : re.match(R'''^[ a-zA-Z]+$''' , t[1] ) , __lowercase ) )
lowerCAmelCase_ : Optional[int] = list(filter(lambda __lowercase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=__lowercase ) , __lowercase ) )
if max_length is not None and len(__lowercase ) > max_length:
lowerCAmelCase_ : Union[str, Any] = toks[:max_length]
if min_length is not None and len(__lowercase ) < min_length and len(__lowercase ) > 0:
while len(__lowercase ) < min_length:
lowerCAmelCase_ : Union[str, Any] = toks + toks
# toks_str = [t[1] for t in toks]
lowerCAmelCase_ : List[str] = [t[0] for t in toks]
# Ensure consistency
lowerCAmelCase_ : int = tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
if " " not in output_txt and len(__lowercase ) > 1:
lowerCAmelCase_ : Optional[int] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=__lowercase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=__lowercase )
)
if with_prefix_space:
lowerCAmelCase_ : Any = ''' ''' + output_txt
lowerCAmelCase_ : List[str] = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
return output_txt, output_ids
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : List[str] = self.perceiver_tokenizer
lowerCAmelCase_ : Any = '''Unicode €.'''
lowerCAmelCase_ : Dict = tokenizer(__lowercase )
lowerCAmelCase_ : Any = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['''input_ids'''] , __lowercase )
# decoding
lowerCAmelCase_ : str = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , '''[CLS]Unicode €.[SEP]''' )
lowerCAmelCase_ : Optional[int] = tokenizer('''e è é ê ë''' )
lowerCAmelCase_ : str = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['''input_ids'''] , __lowercase )
# decoding
lowerCAmelCase_ : int = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) , '''[CLS]e è é ê ë[SEP]''' )
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.perceiver_tokenizer
lowerCAmelCase_ : Dict = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
lowerCAmelCase_ : Optional[int] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCAmelCase_ : Optional[int] = tokenizer(__lowercase , padding=__lowercase , return_tensors=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
if FRAMEWORK != "jax":
lowerCAmelCase_ : str = list(batch.input_ids.numpy()[0] )
else:
lowerCAmelCase_ : Union[str, Any] = list(batch.input_ids.tolist()[0] )
self.assertListEqual(__lowercase , __lowercase )
self.assertEqual((2, 3_8) , batch.input_ids.shape )
self.assertEqual((2, 3_8) , batch.attention_mask.shape )
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : int = self.perceiver_tokenizer
lowerCAmelCase_ : Optional[Any] = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowerCAmelCase_ : List[Any] = tokenizer(__lowercase , padding=__lowercase , return_tensors=__lowercase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' , __lowercase )
self.assertIn('''attention_mask''' , __lowercase )
self.assertNotIn('''decoder_input_ids''' , __lowercase )
self.assertNotIn('''decoder_attention_mask''' , __lowercase )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = self.perceiver_tokenizer
lowerCAmelCase_ : int = [
'''Summary of the text.''',
'''Another summary.''',
]
lowerCAmelCase_ : List[str] = tokenizer(
text_target=__lowercase , max_length=3_2 , padding='''max_length''' , truncation=__lowercase , return_tensors=__lowercase )
self.assertEqual(3_2 , targets['''input_ids'''].shape[1] )
def lowercase_ ( self ) -> Optional[Any]:
# safety check on max_len default value so we are sure the test works
lowerCAmelCase_ : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
lowerCAmelCase_ : Optional[int] = 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
lowerCAmelCase_ : Union[str, Any] = tempfile.mkdtemp()
lowerCAmelCase_ : str = ''' He is very happy, UNwant\u00E9d,running'''
lowerCAmelCase_ : Optional[int] = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Any = tokenizer.__class__.from_pretrained(__lowercase )
lowerCAmelCase_ : Tuple = after_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
shutil.rmtree(__lowercase )
lowerCAmelCase_ : Optional[int] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCAmelCase_ : Optional[int] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
lowerCAmelCase_ : Any = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
lowerCAmelCase_ : str = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : str = tokenizer.__class__.from_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = after_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
lowerCAmelCase_ : str = tokenizer.__class__.from_pretrained(__lowercase , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(__lowercase )
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : List[Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__lowercase )
with open(os.path.join(__lowercase , '''special_tokens_map.json''' ) , encoding='''utf-8''' ) as json_file:
lowerCAmelCase_ : Tuple = json.load(__lowercase )
with open(os.path.join(__lowercase , '''tokenizer_config.json''' ) , encoding='''utf-8''' ) as json_file:
lowerCAmelCase_ : Any = json.load(__lowercase )
lowerCAmelCase_ : Optional[int] = [f"""<extra_id_{i}>""" for i in range(1_2_5 )]
lowerCAmelCase_ : Optional[Any] = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
lowerCAmelCase_ : Any = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(__lowercase , '''special_tokens_map.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(__lowercase , __lowercase )
with open(os.path.join(__lowercase , '''tokenizer_config.json''' ) , '''w''' , encoding='''utf-8''' ) as outfile:
json.dump(__lowercase , __lowercase )
# 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
lowerCAmelCase_ : int = tokenizer_class.from_pretrained(
__lowercase , )
self.assertIn(
'''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens )
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
lowerCAmelCase_ : Tuple = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=__lowercase )]
lowerCAmelCase_ : Dict = tokenizer_class.from_pretrained(
__lowercase , additional_special_tokens=__lowercase , )
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 lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ) , '''�''' )
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> Any:
pass
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> List[str]:
pass
def lowercase_ ( self ) -> Dict:
# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
# strings and special added tokens as tokens
lowerCAmelCase_ : Tuple = self.get_tokenizers(fast=__lowercase , do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
lowerCAmelCase_ : List[str] = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
lowerCAmelCase_ : Optional[int] = tokenizer.convert_tokens_to_string(__lowercase )
self.assertIsInstance(__lowercase , __lowercase ) | 262 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
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/update_metadata.py
a__ : List[str] = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
a__ : int = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
a__ : str = re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a__ : List[str] = 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.
a__ : Optional[int] = re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a__ : Tuple = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def _UpperCamelCase ( __A ) -> Any:
'''simple docstring'''
UpperCamelCase__ = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" , __A )
return [m.group(0 ) for m in matches]
def _UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
UpperCamelCase__ = {
config.replace("Config" , "" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
UpperCamelCase__ = collections.defaultdict(__A )
UpperCamelCase__ = collections.defaultdict(__A )
UpperCamelCase__ = collections.defaultdict(__A )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(__A ):
UpperCamelCase__ = None
if _re_tf_models.match(__A ) is not None:
UpperCamelCase__ = tf_models
UpperCamelCase__ = _re_tf_models.match(__A ).groups()[0]
elif _re_flax_models.match(__A ) is not None:
UpperCamelCase__ = flax_models
UpperCamelCase__ = _re_flax_models.match(__A ).groups()[0]
elif _re_pt_models.match(__A ) is not None:
UpperCamelCase__ = pt_models
UpperCamelCase__ = _re_pt_models.match(__A ).groups()[0]
if lookup_dict is not None:
while len(__A ) > 0:
if attr_name in model_prefix_to_model_type:
UpperCamelCase__ = True
break
# Try again after removing the last word in the name
UpperCamelCase__ = "".join(camel_case_split(__A )[:-1] )
UpperCamelCase__ = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
UpperCamelCase__ = list(__A )
all_models.sort()
UpperCamelCase__ = {"model_type": all_models}
UpperCamelCase__ = [pt_models[t] for t in all_models]
UpperCamelCase__ = [tf_models[t] for t in all_models]
UpperCamelCase__ = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
UpperCamelCase__ = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
UpperCamelCase__ = "AutoProcessor"
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
UpperCamelCase__ = "AutoTokenizer"
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
UpperCamelCase__ = "AutoFeatureExtractor"
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
UpperCamelCase__ = "AutoTokenizer"
UpperCamelCase__ = [processors[t] for t in all_models]
return pd.DataFrame(__A )
def _UpperCamelCase ( __A ) -> int:
'''simple docstring'''
UpperCamelCase__ = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
UpperCamelCase__ = [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
UpperCamelCase__ = [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
# Loop through all three frameworks
for module, cls, mapping in zip(__A , __A , __A ):
# The type of pipeline may not exist in this framework
if not hasattr(__A , __A ):
continue
# First extract all model_names
UpperCamelCase__ = []
for name in getattr(__A , __A ).values():
if isinstance(__A , __A ):
model_names.append(__A )
else:
model_names.extend(list(__A ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def _UpperCamelCase ( __A , __A ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ = get_frameworks_table()
UpperCamelCase__ = Dataset.from_pandas(__A )
UpperCamelCase__ = hf_hub_download(
"huggingface/transformers-metadata" , "pipeline_tags.json" , repo_type="dataset" , token=__A )
UpperCamelCase__ = Dataset.from_json(__A )
UpperCamelCase__ = {
tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
for i in range(len(__A ) )
}
UpperCamelCase__ = update_pipeline_and_auto_class_table(__A )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
UpperCamelCase__ = sorted(table.keys() )
UpperCamelCase__ = pd.DataFrame(
{
"model_class": model_classes,
"pipeline_tag": [table[m][0] for m in model_classes],
"auto_class": [table[m][1] for m in model_classes],
} )
UpperCamelCase__ = Dataset.from_pandas(__A )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(__A , "frameworks.json" ) )
tags_dataset.to_json(os.path.join(__A , "pipeline_tags.json" ) )
if commit_sha is not None:
UpperCamelCase__ = (
F'''Update with commit {commit_sha}\n\nSee: '''
F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
)
else:
UpperCamelCase__ = "Update"
upload_folder(
repo_id="huggingface/transformers-metadata" , folder_path=__A , repo_type="dataset" , token=__A , commit_message=__A , )
def _UpperCamelCase ( ) -> int:
'''simple docstring'''
UpperCamelCase__ = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
UpperCamelCase__ = transformers_module.pipelines.SUPPORTED_TASKS
UpperCamelCase__ = []
for key in pipeline_tasks:
if key not in in_table:
UpperCamelCase__ = pipeline_tasks[key]["pt"]
if isinstance(__A , (list, tuple) ):
UpperCamelCase__ = model[0]
UpperCamelCase__ = model.__name__
if model not in in_table.values():
missing.append(__A )
if len(__A ) > 0:
UpperCamelCase__ = ", ".join(__A )
raise ValueError(
"The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
F'''`utils/update_metadata.py`: {msg}. Please add them!''' )
if __name__ == "__main__":
a__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
a__ : List[Any] = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 80 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class snake_case__:
'''simple docstring'''
@staticmethod
def lowercase_ ( *__lowercase , **__lowercase ) -> Union[str, Any]:
pass
@is_pipeline_test
@require_vision
@require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase_ ( self , __lowercase , __lowercase , __lowercase ) -> List[str]:
lowerCAmelCase_ : Union[str, Any] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCAmelCase_ : str = [
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase_ ( self , __lowercase , __lowercase ) -> str:
lowerCAmelCase_ : Tuple = object_detector(examples[0] , threshold=0.0 )
lowerCAmelCase_ : Dict = len(__lowercase )
self.assertGreater(__lowercase , 0 )
self.assertEqual(
__lowercase , [
{
'''score''': ANY(__lowercase ),
'''label''': ANY(__lowercase ),
'''box''': {'''xmin''': ANY(__lowercase ), '''ymin''': ANY(__lowercase ), '''xmax''': ANY(__lowercase ), '''ymax''': ANY(__lowercase )},
}
for i in range(__lowercase )
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase_ ( self ) -> List[str]:
pass
@require_torch
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Union[str, Any] = pipeline(
'''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCAmelCase_ : Union[str, Any] = object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
] , )
lowerCAmelCase_ : Union[str, Any] = object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
[
{'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
{'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
{'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
{'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
{'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
]
] , )
@require_torch
@slow
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Any = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Dict = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
] , )
lowerCAmelCase_ : Tuple = object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
] , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
[
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
{'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
{'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
],
] , )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase_ ( self ) -> List[str]:
pass
@require_torch
@slow
def lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Any = 0.2
lowerCAmelCase_ : List[Any] = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Optional[Any] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=__lowercase , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
{'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
] , )
@require_torch
@slow
def lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Dict = 2
lowerCAmelCase_ : Union[str, Any] = pipeline('''zero-shot-object-detection''' )
lowerCAmelCase_ : Optional[Any] = object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , top_k=__lowercase , )
self.assertEqual(
nested_simplify(__lowercase , decimals=4 ) , [
{'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
{'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
] , ) | 262 | 0 |
"""simple docstring"""
def _A ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
a =f'''Input value of [number={number}] must be an integer'''
raise TypeError(lowercase )
if number < 1:
a =f'''Input value of [number={number}] must be > 0'''
raise ValueError(lowercase )
a =1
for i in range(1 , lowercase ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod() | 81 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels | 262 | 0 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
A__ = {
"""cola""": 2,
"""mnli""": 3,
"""mrpc""": 2,
"""sst-2""": 2,
"""sts-b""": 1,
"""qqp""": 2,
"""qnli""": 2,
"""rte""": 2,
"""wnli""": 2,
}
logging.set_verbosity_info()
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case=None ):
"""simple docstring"""
_lowerCAmelCase = XLNetConfig.from_json_file(snake_case )
_lowerCAmelCase = finetuning_task.lower() if finetuning_task is not None else """"""
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(F'Building PyTorch XLNetForSequenceClassification model from configuration: {config}' )
_lowerCAmelCase = finetuning_task
_lowerCAmelCase = GLUE_TASKS_NUM_LABELS[finetuning_task]
_lowerCAmelCase = XLNetForSequenceClassification(snake_case )
elif "squad" in finetuning_task:
_lowerCAmelCase = finetuning_task
_lowerCAmelCase = XLNetForQuestionAnswering(snake_case )
else:
_lowerCAmelCase = XLNetLMHeadModel(snake_case )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(snake_case , snake_case , snake_case )
# Save pytorch-model
_lowerCAmelCase = os.path.join(snake_case , snake_case )
_lowerCAmelCase = os.path.join(snake_case , snake_case )
print(F'Save PyTorch model to {os.path.abspath(snake_case )}' )
torch.save(model.state_dict() , snake_case )
print(F'Save configuration file to {os.path.abspath(snake_case )}' )
with open(snake_case , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--xlnet_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained XLNet model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
)
parser.add_argument(
"""--finetuning_task""",
default=None,
type=str,
help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
)
A__ = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 82 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Dict ={
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = """ernie_m"""
SCREAMING_SNAKE_CASE__ : Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __lowercase = 2_5_0_0_0_2 , __lowercase = 7_6_8 , __lowercase = 1_2 , __lowercase = 1_2 , __lowercase = 3_0_7_2 , __lowercase = "gelu" , __lowercase = 0.1 , __lowercase = 0.1 , __lowercase = 5_1_4 , __lowercase = 0.02 , __lowercase = 1 , __lowercase = 1e-05 , __lowercase=None , __lowercase=False , __lowercase=0.0 , **__lowercase , ) -> Tuple:
super().__init__(pad_token_id=__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = vocab_size
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Tuple = num_hidden_layers
lowerCAmelCase_ : int = num_attention_heads
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : int = hidden_act
lowerCAmelCase_ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = max_position_embeddings
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : List[Any] = classifier_dropout
lowerCAmelCase_ : Any = is_decoder
lowerCAmelCase_ : List[Any] = act_dropout | 262 | 0 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
snake_case_ : List[str] = logging.get_logger(__name__)
@add_end_docstrings(lowercase )
class lowercase__ ( lowercase ):
def __init__( self : Optional[int] ,**lowerCamelCase__ : Any ):
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
requires_backends(self ,'vision' )
requires_backends(self ,'torch' )
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
self.check_model_type(lowerCamelCase__ )
def UpperCamelCase_ ( self : int ,**lowerCamelCase__ : int ):
'''simple docstring'''
_UpperCamelCase : Tuple = {}
_UpperCamelCase : Tuple = {}
_UpperCamelCase : Any = {}
# preprocess args
if "points_per_batch" in kwargs:
_UpperCamelCase : str = kwargs['points_per_batch']
if "points_per_crop" in kwargs:
_UpperCamelCase : Optional[Any] = kwargs['points_per_crop']
if "crops_n_layers" in kwargs:
_UpperCamelCase : List[str] = kwargs['crops_n_layers']
if "crop_overlap_ratio" in kwargs:
_UpperCamelCase : List[str] = kwargs['crop_overlap_ratio']
if "crop_n_points_downscale_factor" in kwargs:
_UpperCamelCase : Tuple = kwargs['crop_n_points_downscale_factor']
# postprocess args
if "pred_iou_thresh" in kwargs:
_UpperCamelCase : List[str] = kwargs['pred_iou_thresh']
if "stability_score_offset" in kwargs:
_UpperCamelCase : str = kwargs['stability_score_offset']
if "mask_threshold" in kwargs:
_UpperCamelCase : Dict = kwargs['mask_threshold']
if "stability_score_thresh" in kwargs:
_UpperCamelCase : str = kwargs['stability_score_thresh']
if "crops_nms_thresh" in kwargs:
_UpperCamelCase : Dict = kwargs['crops_nms_thresh']
if "output_rle_mask" in kwargs:
_UpperCamelCase : List[str] = kwargs['output_rle_mask']
if "output_bboxes_mask" in kwargs:
_UpperCamelCase : str = kwargs['output_bboxes_mask']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self : Dict ,lowerCamelCase__ : Optional[Any] ,*lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[Any]=None ,lowerCamelCase__ : Tuple=None ,**lowerCamelCase__ : List[Any] ):
'''simple docstring'''
return super().__call__(lowerCamelCase__ ,*lowerCamelCase__ ,num_workers=lowerCamelCase__ ,batch_size=lowerCamelCase__ ,**lowerCamelCase__ )
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[Any]=64 ,lowerCamelCase__ : int = 0 ,lowerCamelCase__ : float = 512 / 1500 ,lowerCamelCase__ : Optional[int] = 32 ,lowerCamelCase__ : Optional[int] = 1 ,):
'''simple docstring'''
_UpperCamelCase : str = load_image(lowerCamelCase__ )
_UpperCamelCase : Optional[int] = self.image_processor.size['longest_edge']
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Any = self.image_processor.generate_crop_boxes(
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : List[str] = self.image_processor(images=lowerCamelCase__ ,return_tensors='pt' )
with self.device_placement():
if self.framework == "pt":
_UpperCamelCase : Optional[Any] = self.get_inference_context()
with inference_context():
_UpperCamelCase : Optional[Any] = self._ensure_tensor_on_device(lowerCamelCase__ ,device=self.device )
_UpperCamelCase : Optional[int] = self.model.get_image_embeddings(model_inputs.pop('pixel_values' ) )
_UpperCamelCase : Any = image_embeddings
_UpperCamelCase : List[Any] = grid_points.shape[1]
_UpperCamelCase : Optional[Any] = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
'To return all points at once, set points_per_batch to None' )
for i in range(0 ,lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : Optional[Any] = grid_points[:, i : i + points_per_batch, :, :]
_UpperCamelCase : Optional[int] = input_labels[:, i : i + points_per_batch]
_UpperCamelCase : int = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Tuple=0.8_8 ,lowerCamelCase__ : Optional[Any]=0.9_5 ,lowerCamelCase__ : Tuple=0 ,lowerCamelCase__ : Optional[int]=1 ,):
'''simple docstring'''
_UpperCamelCase : Optional[int] = model_inputs.pop('input_boxes' )
_UpperCamelCase : str = model_inputs.pop('is_last' )
_UpperCamelCase : int = model_inputs.pop('original_sizes' ).tolist()
_UpperCamelCase : Optional[int] = model_inputs.pop('reshaped_input_sizes' ).tolist()
_UpperCamelCase : Optional[int] = self.model(**lowerCamelCase__ )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
_UpperCamelCase : Dict = model_outputs['pred_masks']
_UpperCamelCase : List[str] = self.image_processor.post_process_masks(
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,binarize=lowerCamelCase__ )
_UpperCamelCase : Dict = model_outputs['iou_scores']
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Any = self.image_processor.filter_masks(
masks[0] ,iou_scores[0] ,original_sizes[0] ,input_boxes[0] ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,)
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def UpperCamelCase_ ( self : Optional[Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : str=False ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Optional[Any]=0.7 ,):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = []
_UpperCamelCase : List[str] = []
_UpperCamelCase : Dict = []
for model_output in model_outputs:
all_scores.append(model_output.pop('iou_scores' ) )
all_masks.extend(model_output.pop('masks' ) )
all_boxes.append(model_output.pop('boxes' ) )
_UpperCamelCase : Dict = torch.cat(lowerCamelCase__ )
_UpperCamelCase : Dict = torch.cat(lowerCamelCase__ )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Dict = self.image_processor.post_process_for_mask_generation(
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : List[Any] = defaultdict(lowerCamelCase__ )
for output in model_outputs:
for k, v in output.items():
extra[k].append(lowerCamelCase__ )
_UpperCamelCase : Dict = {}
if output_rle_mask:
_UpperCamelCase : Any = rle_mask
if output_bboxes_mask:
_UpperCamelCase : Dict = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 83 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase=7 , __lowercase=3 , __lowercase=1_8 , __lowercase=3_0 , __lowercase=4_0_0 , __lowercase=True , __lowercase=None , __lowercase=True , __lowercase=None , ) -> List[Any]:
lowerCAmelCase_ : Optional[Any] = size if size is not None else {'''shortest_edge''': 2_0}
lowerCAmelCase_ : Any = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
lowerCAmelCase_ : Any = parent
lowerCAmelCase_ : Any = batch_size
lowerCAmelCase_ : Optional[int] = num_channels
lowerCAmelCase_ : Tuple = image_size
lowerCAmelCase_ : List[str] = min_resolution
lowerCAmelCase_ : Dict = max_resolution
lowerCAmelCase_ : Tuple = do_resize
lowerCAmelCase_ : Optional[Any] = size
lowerCAmelCase_ : Union[str, Any] = do_center_crop
lowerCAmelCase_ : Optional[Any] = crop_size
def lowercase_ ( self ) -> Union[str, Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = MobileNetVaImageProcessor if is_vision_available() else None
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Union[str, Any] = MobileNetVaImageProcessingTester(self )
@property
def lowercase_ ( self ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowercase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowercase , '''size''' ) )
self.assertTrue(hasattr(__lowercase , '''do_center_crop''' ) )
self.assertTrue(hasattr(__lowercase , '''crop_size''' ) )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 2_0} )
self.assertEqual(image_processor.crop_size , {'''height''': 1_8, '''width''': 1_8} )
lowerCAmelCase_ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2} )
self.assertEqual(image_processor.crop_size , {'''height''': 8_4, '''width''': 8_4} )
def lowercase_ ( self ) -> Tuple:
pass
def lowercase_ ( self ) -> Union[str, Any]:
# Initialize image_processing
lowerCAmelCase_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , Image.Image )
# Test not batched input
lowerCAmelCase_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Tuple = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self ) -> Optional[int]:
# Initialize image_processing
lowerCAmelCase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , np.ndarray )
# Test not batched input
lowerCAmelCase_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Tuple = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self ) -> Any:
# Initialize image_processing
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , torch.Tensor )
# Test not batched input
lowerCAmelCase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowerCAmelCase_ : Dict = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , ) | 262 | 0 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Dict = "owlvit_text_model"
def __init__( self , __A=4_9408 , __A=512 , __A=2048 , __A=12 , __A=8 , __A=16 , __A="quick_gelu" , __A=1E-5 , __A=0.0 , __A=0.0_2 , __A=1.0 , __A=0 , __A=4_9406 , __A=4_9407 , **__A , ) -> Union[str, Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :List[str] = vocab_size
lowerCAmelCase_ :Dict = hidden_size
lowerCAmelCase_ :str = intermediate_size
lowerCAmelCase_ :str = num_hidden_layers
lowerCAmelCase_ :int = num_attention_heads
lowerCAmelCase_ :Any = max_position_embeddings
lowerCAmelCase_ :List[str] = hidden_act
lowerCAmelCase_ :str = layer_norm_eps
lowerCAmelCase_ :Any = attention_dropout
lowerCAmelCase_ :str = initializer_range
lowerCAmelCase_ :str = initializer_factor
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = cls.get_config_dict(__A , **__A )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
lowerCAmelCase_ :Optional[int] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__A , **__A )
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "owlvit_vision_model"
def __init__( self , __A=768 , __A=3072 , __A=12 , __A=12 , __A=3 , __A=768 , __A=32 , __A="quick_gelu" , __A=1E-5 , __A=0.0 , __A=0.0_2 , __A=1.0 , **__A , ) -> Any:
super().__init__(**__A )
lowerCAmelCase_ :int = hidden_size
lowerCAmelCase_ :Optional[int] = intermediate_size
lowerCAmelCase_ :Dict = num_hidden_layers
lowerCAmelCase_ :int = num_attention_heads
lowerCAmelCase_ :Optional[int] = num_channels
lowerCAmelCase_ :Any = image_size
lowerCAmelCase_ :Union[str, Any] = patch_size
lowerCAmelCase_ :Optional[int] = hidden_act
lowerCAmelCase_ :int = layer_norm_eps
lowerCAmelCase_ :Tuple = attention_dropout
lowerCAmelCase_ :Tuple = initializer_range
lowerCAmelCase_ :Dict = initializer_factor
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
lowerCAmelCase_ :List[Any] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__A , **__A )
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "owlvit"
UpperCAmelCase_ :int = True
def __init__( self , __A=None , __A=None , __A=512 , __A=2.6_5_9_2 , __A=True , **__A , ) -> Tuple:
super().__init__(**__A )
if text_config is None:
lowerCAmelCase_ :Optional[Any] = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
lowerCAmelCase_ :List[Any] = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
lowerCAmelCase_ :Tuple = OwlViTTextConfig(**__A )
lowerCAmelCase_ :Dict = OwlViTVisionConfig(**__A )
lowerCAmelCase_ :Any = projection_dim
lowerCAmelCase_ :int = logit_scale_init_value
lowerCAmelCase_ :List[str] = return_dict
lowerCAmelCase_ :Optional[int] = 1.0
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = cls.get_config_dict(__A , **__A )
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__A , **__A )
@classmethod
def __lowerCAmelCase ( cls , __A , __A , **__A ) -> Optional[int]:
lowerCAmelCase_ :Optional[int] = {}
lowerCAmelCase_ :int = text_config
lowerCAmelCase_ :Dict = vision_config
return cls.from_dict(__A , **__A )
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :List[Any] = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ :str = self.text_config.to_dict()
lowerCAmelCase_ :Tuple = self.vision_config.to_dict()
lowerCAmelCase_ :Optional[int] = self.__class__.model_type
return output
class _SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
return 1E-4
def __lowerCAmelCase ( self , __A , __A = -1 , __A = -1 , __A = None , ) -> Mapping[str, Any]:
lowerCAmelCase_ :Tuple = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__A , seq_length=__A , framework=__A )
lowerCAmelCase_ :Tuple = super().generate_dummy_inputs(
processor.image_processor , batch_size=__A , framework=__A )
return {**text_input_dict, **image_input_dict}
@property
def __lowerCAmelCase ( self ) -> int:
return 14
| 84 |
from __future__ import annotations
import math
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> None:
lowerCAmelCase_ : str = size
# approximate the overall size of segment tree with given value
lowerCAmelCase_ : Dict = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
lowerCAmelCase_ : Dict = [0 for i in range(0 , 4 * size )]
lowerCAmelCase_ : Optional[int] = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowercase_ ( self , __lowercase ) -> int:
return idx * 2
def lowercase_ ( self , __lowercase ) -> int:
return idx * 2 + 1
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase ) -> None:
if left_element == right_element:
lowerCAmelCase_ : Tuple = a[left_element - 1]
else:
lowerCAmelCase_ : int = (left_element + right_element) // 2
self.build(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase )
self.build(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase )
lowerCAmelCase_ : Any = max(
self.segment_tree[self.left(__lowercase )] , self.segment_tree[self.right(__lowercase )] )
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> bool:
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Union[str, Any] = False
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Any = self.lazy[idx]
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : Optional[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
lowerCAmelCase_ : Dict = val
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = val
lowerCAmelCase_ : List[Any] = val
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : List[str] = True
return True
lowerCAmelCase_ : Optional[Any] = (left_element + right_element) // 2
self.update(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
self.update(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase , __lowercase , __lowercase )
lowerCAmelCase_ : int = max(
self.segment_tree[self.left(__lowercase )] , self.segment_tree[self.right(__lowercase )] )
return True
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> int | float:
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Optional[Any] = False
if left_element != right_element:
lowerCAmelCase_ : List[Any] = self.lazy[idx]
lowerCAmelCase_ : Dict = self.lazy[idx]
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : Optional[int] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
lowerCAmelCase_ : List[Any] = (left_element + right_element) // 2
lowerCAmelCase_ : Tuple = self.query(self.left(__lowercase ) , __lowercase , __lowercase , __lowercase , __lowercase )
lowerCAmelCase_ : List[Any] = self.query(self.right(__lowercase ) , mid + 1 , __lowercase , __lowercase , __lowercase )
return max(__lowercase , __lowercase )
def __str__( self ) -> str:
return str([self.query(1 , 1 , self.size , __lowercase , __lowercase ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
_UpperCAmelCase : str =[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
_UpperCAmelCase : List[str] =15
_UpperCAmelCase : Any =SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 262 | 0 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : Dict = CLIPConfig
lowerCAmelCase_ : Dict = ["CLIPEncoderLayer"]
def __init__( self , a__ ) -> Dict:
'''simple docstring'''
super().__init__(a__ )
snake_case_ = CLIPVisionModelWithProjection(config.vision_config )
snake_case_ = nn.Linear(config.vision_config.projection_dim , 1 )
snake_case_ = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def lowerCAmelCase__ ( self , a__ , a__ , a__=0.5 , a__=0.5 ) -> Any:
'''simple docstring'''
snake_case_ = self.vision_model(a__ )[0]
snake_case_ = self.p_head(a__ )
snake_case_ = nsfw_detected.flatten()
snake_case_ = nsfw_detected > p_threshold
snake_case_ = nsfw_detected.tolist()
if any(a__ ):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed." )
for idx, nsfw_detected_ in enumerate(a__ ):
if nsfw_detected_:
snake_case_ = np.zeros(images[idx].shape )
snake_case_ = self.w_head(a__ )
snake_case_ = watermark_detected.flatten()
snake_case_ = watermark_detected > w_threshold
snake_case_ = watermark_detected.tolist()
if any(a__ ):
logger.warning(
"Potential watermarked content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed." )
for idx, watermark_detected_ in enumerate(a__ ):
if watermark_detected_:
snake_case_ = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 85 |
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
_UpperCAmelCase : Optional[int] ="""src/transformers"""
_UpperCAmelCase : str ="""docs/source/en"""
_UpperCAmelCase : Optional[int] ="""."""
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
with open(lowerCAmelCase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCAmelCase_ : int = f.readlines()
# Find the start prompt.
lowerCAmelCase_ : List[Any] = 0
while not lines[start_index].startswith(lowerCAmelCase_ ):
start_index += 1
start_index += 1
lowerCAmelCase_ : List[str] = start_index
while not lines[end_index].startswith(lowerCAmelCase_ ):
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 |
_UpperCAmelCase : Optional[Any] ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase : Optional[int] =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase : Dict =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.
_UpperCAmelCase : Optional[Any] =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase : Optional[int] =direct_transformers_import(TRANSFORMERS_PATH)
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : str = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , lowerCAmelCase_ )
return [m.group(0 ) for m in matches]
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
lowerCAmelCase_ : Tuple = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_ )
lowerCAmelCase_ : int = (width - text_length) // 2
lowerCAmelCase_ : Union[str, Any] = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowerCAmelCase ( )-> str:
lowerCAmelCase_ : Any = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowerCAmelCase_ : Dict = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowerCAmelCase_ : List[Any] = {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.
lowerCAmelCase_ : Tuple = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[Any] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = collections.defaultdict(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = collections.defaultdict(lowerCAmelCase_ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowerCAmelCase_ ):
lowerCAmelCase_ : Optional[int] = None
if attr_name.endswith('''Tokenizer''' ):
lowerCAmelCase_ : Union[str, Any] = slow_tokenizers
lowerCAmelCase_ : List[str] = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
lowerCAmelCase_ : int = fast_tokenizers
lowerCAmelCase_ : Union[str, Any] = attr_name[:-13]
elif _re_tf_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = tf_models
lowerCAmelCase_ : str = _re_tf_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_flax_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Tuple = flax_models
lowerCAmelCase_ : Union[str, Any] = _re_flax_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_pt_models.match(lowerCAmelCase_ ) is not None:
lowerCAmelCase_ : Any = pt_models
lowerCAmelCase_ : List[Any] = _re_pt_models.match(lowerCAmelCase_ ).groups()[0]
if lookup_dict is not None:
while len(lowerCAmelCase_ ) > 0:
if attr_name in model_name_to_prefix.values():
lowerCAmelCase_ : Union[str, Any] = True
break
# Try again after removing the last word in the name
lowerCAmelCase_ : Any = ''''''.join(camel_case_split(lowerCAmelCase_ )[:-1] )
# Let's build that table!
lowerCAmelCase_ : int = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowerCAmelCase_ : Tuple = ['''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).
lowerCAmelCase_ : Union[str, Any] = [len(lowerCAmelCase_ ) + 2 for c in columns]
lowerCAmelCase_ : Optional[Any] = max([len(lowerCAmelCase_ ) for name in model_names] ) + 2
# Build the table per se
lowerCAmelCase_ : Dict = '''|''' + '''|'''.join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for c, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
lowerCAmelCase_ : List[str] = {True: '''✅''', False: '''❌'''}
for name in model_names:
lowerCAmelCase_ : List[Any] = model_name_to_prefix[name]
lowerCAmelCase_ : Union[str, Any] = [
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(lowerCAmelCase_ , lowerCAmelCase_ ) for l, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + "|\n"
return table
def lowerCAmelCase ( lowerCAmelCase_=False )-> Tuple:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = _find_text_in_file(
filename=os.path.join(lowerCAmelCase_ , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
lowerCAmelCase_ : Tuple = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowerCAmelCase_ , '''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__":
_UpperCAmelCase : List[Any] =argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_UpperCAmelCase : Tuple =parser.parse_args()
check_model_table(args.fix_and_overwrite) | 262 | 0 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A__ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
A_ : List[str] = [R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 5_02_57 , _SCREAMING_SNAKE_CASE = 10_24 , _SCREAMING_SNAKE_CASE = 7_68 , _SCREAMING_SNAKE_CASE = 12 , _SCREAMING_SNAKE_CASE = 12 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "gelu_new" , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 1E-5 , _SCREAMING_SNAKE_CASE = 0.02 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , ):
super().__init__()
__lowerCAmelCase : Dict = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
f" `n_embd`: {n_embd} are not equal." )
__lowerCAmelCase : int = prefix_inner_dim
__lowerCAmelCase : Optional[Any] = prefix_hidden_dim
__lowerCAmelCase : Union[str, Any] = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
__lowerCAmelCase : Union[str, Any] = (
nn.Linear(self.prefix_hidden_dim , _SCREAMING_SNAKE_CASE ) if self.prefix_hidden_dim is not None else nn.Identity()
)
__lowerCAmelCase : Dict = GPTaConfig(
vocab_size=_SCREAMING_SNAKE_CASE , n_positions=_SCREAMING_SNAKE_CASE , n_embd=_SCREAMING_SNAKE_CASE , n_layer=_SCREAMING_SNAKE_CASE , n_head=_SCREAMING_SNAKE_CASE , n_inner=_SCREAMING_SNAKE_CASE , activation_function=_SCREAMING_SNAKE_CASE , resid_pdrop=_SCREAMING_SNAKE_CASE , embd_pdrop=_SCREAMING_SNAKE_CASE , attn_pdrop=_SCREAMING_SNAKE_CASE , layer_norm_epsilon=_SCREAMING_SNAKE_CASE , initializer_range=_SCREAMING_SNAKE_CASE , scale_attn_weights=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE , scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE , reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : str = GPTaLMHeadModel(_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , ):
__lowerCAmelCase : Optional[Any] = self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = self.encode_prefix(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = self.decode_prefix(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
__lowerCAmelCase : Dict = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
__lowerCAmelCase : Tuple = torch.cat((dummy_token, input_ids) , dim=1 )
__lowerCAmelCase : List[Any] = self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return torch.zeros(_SCREAMING_SNAKE_CASE , self.prefix_length , dtype=torch.intaa , device=_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
return self.encode_prefix(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[int] = torch.split(_SCREAMING_SNAKE_CASE , 1 , dim=0 )
__lowerCAmelCase : Dict = []
__lowerCAmelCase : Dict = []
for feature in features:
__lowerCAmelCase : Union[str, Any] = self.decode_prefix(feature.to(_SCREAMING_SNAKE_CASE ) ) # back to the clip feature
# Only support beam search for now
__lowerCAmelCase , __lowerCAmelCase : List[Any] = self.generate_beam(
input_embeds=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
__lowerCAmelCase : List[str] = torch.stack(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = torch.stack(_SCREAMING_SNAKE_CASE )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE = 5 , _SCREAMING_SNAKE_CASE = 67 , _SCREAMING_SNAKE_CASE = 1.0 , _SCREAMING_SNAKE_CASE = None , ):
__lowerCAmelCase : List[Any] = eos_token_id
__lowerCAmelCase : List[Any] = None
__lowerCAmelCase : Tuple = None
__lowerCAmelCase : Optional[int] = torch.ones(_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , dtype=torch.int )
__lowerCAmelCase : Union[str, Any] = torch.zeros(_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , dtype=torch.bool )
if input_embeds is not None:
__lowerCAmelCase : Union[str, Any] = input_embeds
else:
__lowerCAmelCase : str = self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE )
for i in range(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[int] = self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = outputs.logits
__lowerCAmelCase : Union[str, Any] = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
__lowerCAmelCase : Tuple = logits.softmax(-1 ).log()
if scores is None:
__lowerCAmelCase , __lowerCAmelCase : Tuple = logits.topk(_SCREAMING_SNAKE_CASE , -1 )
__lowerCAmelCase : Dict = generated.expand(_SCREAMING_SNAKE_CASE , *generated.shape[1:] )
__lowerCAmelCase , __lowerCAmelCase : int = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
__lowerCAmelCase : Tuple = next_tokens
else:
__lowerCAmelCase : Dict = tokens.expand(_SCREAMING_SNAKE_CASE , *tokens.shape[1:] )
__lowerCAmelCase : List[str] = torch.cat((tokens, next_tokens) , dim=1 )
else:
__lowerCAmelCase : List[str] = -float(np.inf )
__lowerCAmelCase : Union[str, Any] = 0
__lowerCAmelCase : Any = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
__lowerCAmelCase : Optional[Any] = scores_sum / seq_lengths[:, None]
__lowerCAmelCase , __lowerCAmelCase : List[Any] = scores_sum_average.view(-1 ).topk(_SCREAMING_SNAKE_CASE , -1 )
__lowerCAmelCase : List[Any] = next_tokens // scores_sum.shape[1]
__lowerCAmelCase : List[Any] = seq_lengths[next_tokens_source]
__lowerCAmelCase : Any = next_tokens % scores_sum.shape[1]
__lowerCAmelCase : Tuple = next_tokens.unsqueeze(1 )
__lowerCAmelCase : str = tokens[next_tokens_source]
__lowerCAmelCase : str = torch.cat((tokens, next_tokens) , dim=1 )
__lowerCAmelCase : List[str] = generated[next_tokens_source]
__lowerCAmelCase : Union[str, Any] = scores_sum_average * seq_lengths
__lowerCAmelCase : int = is_stopped[next_tokens_source]
__lowerCAmelCase : Optional[Any] = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
__lowerCAmelCase : int = torch.cat((generated, next_token_embed) , dim=1 )
__lowerCAmelCase : List[str] = is_stopped + next_tokens.eq(_SCREAMING_SNAKE_CASE ).squeeze()
if is_stopped.all():
break
__lowerCAmelCase : Optional[int] = scores / seq_lengths
__lowerCAmelCase : int = scores.argsort(descending=_SCREAMING_SNAKE_CASE )
# tokens tensors are already padded to max_seq_length
__lowerCAmelCase : Union[str, Any] = [tokens[i] for i in order]
__lowerCAmelCase : Union[str, Any] = torch.stack(_SCREAMING_SNAKE_CASE , dim=0 )
__lowerCAmelCase : Optional[Any] = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths | 86 |
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 ( )-> int:
lowerCAmelCase_ : int = ArgumentParser('''Transformers CLI tool''' , usage='''transformers-cli <command> [<args>]''' )
lowerCAmelCase_ : Dict = parser.add_subparsers(help='''transformers-cli command helpers''' )
# Register commands
ConvertCommand.register_subcommand(lowerCAmelCase_ )
DownloadCommand.register_subcommand(lowerCAmelCase_ )
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
RunCommand.register_subcommand(lowerCAmelCase_ )
ServeCommand.register_subcommand(lowerCAmelCase_ )
UserCommands.register_subcommand(lowerCAmelCase_ )
AddNewModelCommand.register_subcommand(lowerCAmelCase_ )
AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_ )
LfsCommands.register_subcommand(lowerCAmelCase_ )
PTtoTFCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
lowerCAmelCase_ : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , '''func''' ):
parser.print_help()
exit(1 )
# Run
lowerCAmelCase_ : List[Any] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main() | 262 | 0 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowercase_ ( _lowerCamelCase : int):
lowercase__ : int = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
))
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
))
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
))
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
))
return embed
def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : int):
lowercase__ : Optional[Any] = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
))
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
))
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight'''))
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias'''))
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight'''))
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias'''))
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight'''))
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias'''))
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight'''))
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias'''))
return attention_weights
def lowercase_ ( _lowerCamelCase : Optional[int]):
lowercase__ : Tuple = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token"))
return token
def lowercase_ ( ):
lowercase__ : List[str] = []
head.append(("layernorm.weight", "norm.weight"))
head.append(("layernorm.bias", "norm.bias"))
head.append(("classifier.weight", "head.weight"))
head.append(("classifier.bias", "head.bias"))
return head
def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Union[str, Any]):
lowercase__ : Optional[Any] = "imagenet-1k-id2label.json"
lowercase__ : List[str] = 1000
lowercase__ : Dict = "huggingface/label-files"
lowercase__ : List[Any] = num_labels
lowercase__ : Tuple = json.load(open(cached_download(hf_hub_url(_lowerCamelCase , _lowerCamelCase , repo_type="dataset")) , "r"))
lowercase__ : Tuple = {int(_lowerCamelCase): v for k, v in idalabel.items()}
lowercase__ : Any = idalabel
lowercase__ : List[Any] = {v: k for k, v in idalabel.items()}
lowercase__ : Optional[int] = CvtConfig(num_labels=_lowerCamelCase , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase)
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("/" , 1)[-1][4:6] == "13":
lowercase__ : Any = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("/" , 1)[-1][4:6] == "21":
lowercase__ : Tuple = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
lowercase__ : Union[str, Any] = [2, 2, 20]
lowercase__ : Optional[Any] = [3, 12, 16]
lowercase__ : Optional[Any] = [192, 768, 1024]
lowercase__ : Union[str, Any] = CvtForImageClassification(_lowerCamelCase)
lowercase__ : Tuple = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
lowercase__ : int = image_size
lowercase__ : Dict = torch.load(_lowerCamelCase , map_location=torch.device("cpu"))
lowercase__ : Any = OrderedDict()
lowercase__ : int = []
for idx in range(len(config.depth)):
if config.cls_token[idx]:
lowercase__ : Dict = list_of_state_dict + cls_token(_lowerCamelCase)
lowercase__ : List[str] = list_of_state_dict + embeddings(_lowerCamelCase)
for cnt in range(config.depth[idx]):
lowercase__ : Any = list_of_state_dict + attention(_lowerCamelCase , _lowerCamelCase)
lowercase__ : List[str] = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_lowerCamelCase)
for i in range(len(_lowerCamelCase)):
lowercase__ : Dict = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_lowerCamelCase)
model.save_pretrained(_lowerCamelCase)
image_processor.save_pretrained(_lowerCamelCase)
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=384,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
UpperCamelCase = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 87 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
_UpperCAmelCase : Tuple =None
_UpperCAmelCase : int =logging.get_logger(__name__)
_UpperCAmelCase : Dict ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
_UpperCAmelCase : Any ={
"""vocab_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
),
},
}
_UpperCAmelCase : int ={
"""facebook/nllb-large-en-ro""": 1024,
"""facebook/nllb-200-distilled-600M""": 1024,
}
# fmt: off
_UpperCAmelCase : Any =["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Dict = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""input_ids""", """attention_mask"""]
SCREAMING_SNAKE_CASE__ : int = NllbTokenizer
SCREAMING_SNAKE_CASE__ : List[int] = []
SCREAMING_SNAKE_CASE__ : List[int] = []
def __init__( self , __lowercase=None , __lowercase=None , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=False , **__lowercase , ) -> int:
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ : int = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
lowerCAmelCase_ : List[Any] = legacy_behaviour
super().__init__(
vocab_file=__lowercase , tokenizer_file=__lowercase , bos_token=__lowercase , eos_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , src_lang=__lowercase , tgt_lang=__lowercase , additional_special_tokens=__lowercase , legacy_behaviour=__lowercase , **__lowercase , )
lowerCAmelCase_ : Any = vocab_file
lowerCAmelCase_ : List[Any] = False if not self.vocab_file else True
lowerCAmelCase_ : Optional[Any] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
lowerCAmelCase_ : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(__lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowerCAmelCase_ : Any = src_lang if src_lang is not None else '''eng_Latn'''
lowerCAmelCase_ : str = self.convert_tokens_to_ids(self._src_lang )
lowerCAmelCase_ : Optional[int] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def lowercase_ ( self ) -> str:
return self._src_lang
@src_lang.setter
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : Any = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowercase_ ( self , __lowercase , __lowercase = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowercase_ ( self , __lowercase , __lowercase = None ) -> List[int]:
lowerCAmelCase_ : Optional[Any] = [self.sep_token_id]
lowerCAmelCase_ : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase_ ( self , __lowercase , __lowercase , __lowercase , __lowercase , **__lowercase ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : int = self(__lowercase , add_special_tokens=__lowercase , return_tensors=__lowercase , **__lowercase )
lowerCAmelCase_ : Dict = self.convert_tokens_to_ids(__lowercase )
lowerCAmelCase_ : List[Any] = tgt_lang_id
return inputs
def lowercase_ ( self , __lowercase , __lowercase = "eng_Latn" , __lowercase = None , __lowercase = "fra_Latn" , **__lowercase , ) -> BatchEncoding:
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : List[str] = tgt_lang
return super().prepare_seqaseq_batch(__lowercase , __lowercase , **__lowercase )
def lowercase_ ( self ) -> List[Any]:
return self.set_src_lang_special_tokens(self.src_lang )
def lowercase_ ( self ) -> str:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : List[str] = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
lowerCAmelCase_ : Any = []
lowerCAmelCase_ : List[str] = [self.eos_token_id, self.cur_lang_code]
else:
lowerCAmelCase_ : Optional[int] = [self.cur_lang_code]
lowerCAmelCase_ : List[Any] = [self.eos_token_id]
lowerCAmelCase_ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ : List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ : Any = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowercase_ ( self , __lowercase ) -> None:
lowerCAmelCase_ : Dict = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
lowerCAmelCase_ : List[Any] = []
lowerCAmelCase_ : Any = [self.eos_token_id, self.cur_lang_code]
else:
lowerCAmelCase_ : Any = [self.cur_lang_code]
lowerCAmelCase_ : Any = [self.eos_token_id]
lowerCAmelCase_ : List[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ : Tuple = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ : Optional[Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def lowercase_ ( self , __lowercase , __lowercase = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__lowercase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
return
lowerCAmelCase_ : Any = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
copyfile(self.vocab_file , __lowercase )
return (out_vocab_file,) | 262 | 0 |
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 UpperCAmelCase_ ( _A ):
'''simple docstring'''
def __get__( self : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any=None ) -> str:
"""simple docstring"""
if obj is None:
return self
if self.fget is None:
raise AttributeError("""unreadable attribute""" )
__magic_name__ = """__cached_""" + self.fget.__name__
__magic_name__ = getattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if cached is None:
__magic_name__ = self.fget(UpperCamelCase__ )
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return cached
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = 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 a__ ( A_ ):
'''simple docstring'''
if is_torch_fx_proxy(A_ ):
return True
if is_torch_available():
import torch
if isinstance(A_, torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(A_, tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(A_, (jnp.ndarray, Tracer) ):
return True
return isinstance(A_, np.ndarray )
def a__ ( A_ ):
'''simple docstring'''
return isinstance(A_, np.ndarray )
def a__ ( A_ ):
'''simple docstring'''
return _is_numpy(A_ )
def a__ ( A_ ):
'''simple docstring'''
import torch
return isinstance(A_, torch.Tensor )
def a__ ( A_ ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch(A_ )
def a__ ( A_ ):
'''simple docstring'''
import torch
return isinstance(A_, torch.device )
def a__ ( A_ ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(A_ )
def a__ ( A_ ):
'''simple docstring'''
import torch
if isinstance(A_, A_ ):
if hasattr(A_, A_ ):
__magic_name__ = getattr(A_, A_ )
else:
return False
return isinstance(A_, torch.dtype )
def a__ ( A_ ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(A_ )
def a__ ( A_ ):
'''simple docstring'''
import tensorflow as tf
return isinstance(A_, tf.Tensor )
def a__ ( A_ ):
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(A_ )
def a__ ( A_ ):
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(A_, """is_symbolic_tensor""" ):
return tf.is_symbolic_tensor(A_ )
return type(A_ ) == tf.Tensor
def a__ ( A_ ):
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(A_ )
def a__ ( A_ ):
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(A_, jnp.ndarray )
def a__ ( A_ ):
'''simple docstring'''
return False if not is_flax_available() else _is_jax(A_ )
def a__ ( A_ ):
'''simple docstring'''
if isinstance(A_, (dict, UserDict) ):
return {k: to_py_obj(A_ ) for k, v in obj.items()}
elif isinstance(A_, (list, tuple) ):
return [to_py_obj(A_ ) for o in obj]
elif is_tf_tensor(A_ ):
return obj.numpy().tolist()
elif is_torch_tensor(A_ ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(A_ ):
return np.asarray(A_ ).tolist()
elif isinstance(A_, (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def a__ ( A_ ):
'''simple docstring'''
if isinstance(A_, (dict, UserDict) ):
return {k: to_numpy(A_ ) for k, v in obj.items()}
elif isinstance(A_, (list, tuple) ):
return np.array(A_ )
elif is_tf_tensor(A_ ):
return obj.numpy()
elif is_torch_tensor(A_ ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(A_ ):
return np.asarray(A_ )
else:
return obj
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
def _lowercase ( self : List[Any] ) -> str:
"""simple docstring"""
__magic_name__ = 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.''' )
__magic_name__ = getattr(self , class_fields[0].name )
__magic_name__ = 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__ ):
__magic_name__ = first_field.items()
__magic_name__ = True
else:
try:
__magic_name__ = iter(UpperCamelCase__ )
__magic_name__ = True
except TypeError:
__magic_name__ = 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
__magic_name__ = 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:
__magic_name__ = element[1]
elif first_field is not None:
__magic_name__ = first_field
else:
for field in class_fields:
__magic_name__ = getattr(self , field.name )
if v is not None:
__magic_name__ = v
def __delitem__( self : Any , *UpperCamelCase__ : List[Any] , **UpperCamelCase__ : Any ) -> Optional[Any]:
"""simple docstring"""
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def _lowercase ( self : Optional[Any] , *UpperCamelCase__ : Union[str, Any] , **UpperCamelCase__ : Union[str, Any] ) -> str:
"""simple docstring"""
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def _lowercase ( self : Any , *UpperCamelCase__ : List[str] , **UpperCamelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def _lowercase ( self : List[str] , *UpperCamelCase__ : Dict , **UpperCamelCase__ : Optional[int] ) -> Tuple:
"""simple docstring"""
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : Any , UpperCamelCase__ : Optional[Any] ) -> Any:
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__magic_name__ = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : str ) -> Optional[Any]:
"""simple docstring"""
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__ : Dict , UpperCamelCase__ : int ) -> int:
"""simple docstring"""
super().__setitem__(UpperCamelCase__ , UpperCamelCase__ )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(UpperCamelCase__ , UpperCamelCase__ )
def _lowercase ( self : str ) -> Tuple[Any]:
"""simple docstring"""
return tuple(self[k] for k in self.keys() )
class UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
@classmethod
def _lowercase ( cls : Union[str, Any] , UpperCamelCase__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = """longest"""
a__ = """max_length"""
a__ = """do_not_pad"""
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = """pt"""
a__ = """tf"""
a__ = """np"""
a__ = """jax"""
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : Dict , UpperCamelCase__ : List[ContextManager] ) -> Any:
"""simple docstring"""
__magic_name__ = context_managers
__magic_name__ = ExitStack()
def __enter__( self : Optional[int] ) -> int:
"""simple docstring"""
for context_manager in self.context_managers:
self.stack.enter_context(UpperCamelCase__ )
def __exit__( self : Dict , *UpperCamelCase__ : str , **UpperCamelCase__ : Optional[int] ) -> str:
"""simple docstring"""
self.stack.__exit__(*UpperCamelCase__ , **UpperCamelCase__ )
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = infer_framework(A_ )
if framework == "tf":
__magic_name__ = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
__magic_name__ = inspect.signature(model_class.forward ) # PyTorch models
else:
__magic_name__ = 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 a__ ( A_ ):
'''simple docstring'''
__magic_name__ = model_class.__name__
__magic_name__ = infer_framework(A_ )
if framework == "tf":
__magic_name__ = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
__magic_name__ = inspect.signature(model_class.forward ) # PyTorch models
else:
__magic_name__ = 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 a__ ( A_, A_ = "", A_ = "." ):
'''simple docstring'''
def _flatten_dict(A_, A_="", A_="." ):
for k, v in d.items():
__magic_name__ = str(A_ ) + delimiter + str(A_ ) if parent_key else k
if v and isinstance(A_, A_ ):
yield from flatten_dict(A_, A_, delimiter=A_ ).items()
else:
yield key, v
return dict(_flatten_dict(A_, A_, A_ ) )
@contextmanager
def a__ ( A_, A_ = False ):
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def a__ ( A_, A_=None ):
'''simple docstring'''
if is_numpy_array(A_ ):
return np.transpose(A_, axes=A_ )
elif is_torch_tensor(A_ ):
return array.T if axes is None else array.permute(*A_ )
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.transpose(A_, perm=A_ )
elif is_jax_tensor(A_ ):
return jnp.transpose(A_, axes=A_ )
else:
raise ValueError(f'''Type not supported for transpose: {type(A_ )}.''' )
def a__ ( A_, A_ ):
'''simple docstring'''
if is_numpy_array(A_ ):
return np.reshape(A_, A_ )
elif is_torch_tensor(A_ ):
return array.reshape(*A_ )
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.reshape(A_, A_ )
elif is_jax_tensor(A_ ):
return jnp.reshape(A_, A_ )
else:
raise ValueError(f'''Type not supported for reshape: {type(A_ )}.''' )
def a__ ( A_, A_=None ):
'''simple docstring'''
if is_numpy_array(A_ ):
return np.squeeze(A_, axis=A_ )
elif is_torch_tensor(A_ ):
return array.squeeze() if axis is None else array.squeeze(dim=A_ )
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.squeeze(A_, axis=A_ )
elif is_jax_tensor(A_ ):
return jnp.squeeze(A_, axis=A_ )
else:
raise ValueError(f'''Type not supported for squeeze: {type(A_ )}.''' )
def a__ ( A_, A_ ):
'''simple docstring'''
if is_numpy_array(A_ ):
return np.expand_dims(A_, A_ )
elif is_torch_tensor(A_ ):
return array.unsqueeze(dim=A_ )
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.expand_dims(A_, axis=A_ )
elif is_jax_tensor(A_ ):
return jnp.expand_dims(A_, axis=A_ )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(A_ )}.''' )
def a__ ( A_ ):
'''simple docstring'''
if is_numpy_array(A_ ):
return np.size(A_ )
elif is_torch_tensor(A_ ):
return array.numel()
elif is_tf_tensor(A_ ):
import tensorflow as tf
return tf.size(A_ )
elif is_jax_tensor(A_ ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(A_ )}.''' )
def a__ ( A_, A_ ):
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(A_, (tuple, list) ):
__magic_name__ = [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:
__magic_name__ = f'''{repo_id}--{value}'''
return auto_map
def a__ ( A_ ):
'''simple docstring'''
for base_class in inspect.getmro(A_ ):
__magic_name__ = base_class.__module__
__magic_name__ = 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}.''' )
| 88 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
_UpperCAmelCase : Optional[Any] =NewType("""DataClass""", Any)
_UpperCAmelCase : Dict =NewType("""DataClassType""", Any)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def lowerCAmelCase ( lowerCAmelCase_ )-> Callable[[str], Any]:
lowerCAmelCase_ : str = {str(lowerCAmelCase_ ): choice for choice in choices}
return lambda lowerCAmelCase_ : str_to_choice.get(lowerCAmelCase_ , lowerCAmelCase_ )
def lowerCAmelCase ( *,
lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = dataclasses.MISSING , lowerCAmelCase_ = dataclasses.MISSING , lowerCAmelCase_ = None , **lowerCAmelCase_ , )-> dataclasses.Field:
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
lowerCAmelCase_ : Dict = {}
if aliases is not None:
lowerCAmelCase_ : str = aliases
if help is not None:
lowerCAmelCase_ : Tuple = help
return dataclasses.field(metadata=lowerCAmelCase_ , default=lowerCAmelCase_ , default_factory=lowerCAmelCase_ , **lowerCAmelCase_ )
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Iterable[DataClassType]
def __init__( self , __lowercase , **__lowercase ) -> List[str]:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
lowerCAmelCase_ : Optional[int] = ArgumentDefaultsHelpFormatter
super().__init__(**__lowercase )
if dataclasses.is_dataclass(__lowercase ):
lowerCAmelCase_ : Union[str, Any] = [dataclass_types]
lowerCAmelCase_ : List[Any] = list(__lowercase )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(__lowercase )
@staticmethod
def lowercase_ ( __lowercase , __lowercase ) -> Union[str, Any]:
lowerCAmelCase_ : Optional[Any] = f"""--{field.name}"""
lowerCAmelCase_ : Tuple = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , __lowercase ):
raise RuntimeError(
'''Unresolved type detected, which should have been done with the help of '''
'''`typing.get_type_hints` method by default''' )
lowerCAmelCase_ : List[str] = kwargs.pop('''aliases''' , [] )
if isinstance(__lowercase , __lowercase ):
lowerCAmelCase_ : Optional[Any] = [aliases]
lowerCAmelCase_ : Any = getattr(field.type , '''__origin__''' , field.type )
if origin_type is Union or (hasattr(__lowercase , '''UnionType''' ) and isinstance(__lowercase , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(__lowercase ) not in field.type.__args__
):
raise ValueError(
'''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
''' the argument parser only supports one type per argument.'''
f""" Problem encountered in field '{field.name}'.""" )
if type(__lowercase ) not in field.type.__args__:
# filter `str` in Union
lowerCAmelCase_ : List[Any] = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
lowerCAmelCase_ : Dict = getattr(field.type , '''__origin__''' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
lowerCAmelCase_ : str = (
field.type.__args__[0] if isinstance(__lowercase , field.type.__args__[1] ) else field.type.__args__[1]
)
lowerCAmelCase_ : List[Any] = getattr(field.type , '''__origin__''' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
lowerCAmelCase_ : Dict = {}
if origin_type is Literal or (isinstance(field.type , __lowercase ) and issubclass(field.type , __lowercase )):
if origin_type is Literal:
lowerCAmelCase_ : Optional[Any] = field.type.__args__
else:
lowerCAmelCase_ : int = [x.value for x in field.type]
lowerCAmelCase_ : str = make_choice_type_function(kwargs['''choices'''] )
if field.default is not dataclasses.MISSING:
lowerCAmelCase_ : str = field.default
else:
lowerCAmelCase_ : Tuple = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
lowerCAmelCase_ : Tuple = copy(__lowercase )
# Hack because type=bool in argparse does not behave as we want.
lowerCAmelCase_ : Dict = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
lowerCAmelCase_ : Union[str, Any] = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
lowerCAmelCase_ : List[str] = default
# This tells argparse we accept 0 or 1 value after --field_name
lowerCAmelCase_ : int = '''?'''
# This is the value that will get picked if we do --field_name (without value)
lowerCAmelCase_ : List[Any] = True
elif isclass(__lowercase ) and issubclass(__lowercase , __lowercase ):
lowerCAmelCase_ : Union[str, Any] = field.type.__args__[0]
lowerCAmelCase_ : Dict = '''+'''
if field.default_factory is not dataclasses.MISSING:
lowerCAmelCase_ : Any = field.default_factory()
elif field.default is dataclasses.MISSING:
lowerCAmelCase_ : Optional[int] = True
else:
lowerCAmelCase_ : List[Any] = field.type
if field.default is not dataclasses.MISSING:
lowerCAmelCase_ : Dict = field.default
elif field.default_factory is not dataclasses.MISSING:
lowerCAmelCase_ : List[Any] = field.default_factory()
else:
lowerCAmelCase_ : int = True
parser.add_argument(__lowercase , *__lowercase , **__lowercase )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
lowerCAmelCase_ : Any = False
parser.add_argument(f"""--no_{field.name}""" , action='''store_false''' , dest=field.name , **__lowercase )
def lowercase_ ( self , __lowercase ) -> List[Any]:
if hasattr(__lowercase , '''_argument_group_name''' ):
lowerCAmelCase_ : str = self.add_argument_group(dtype._argument_group_name )
else:
lowerCAmelCase_ : Dict = self
try:
lowerCAmelCase_ : Dict[str, type] = get_type_hints(__lowercase )
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
'''removing line of `from __future__ import annotations` which opts in Postponed '''
'''Evaluation of Annotations (PEP 563)''' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 1_0) and "unsupported operand type(s) for |" in str(__lowercase ):
lowerCAmelCase_ : Any = '''.'''.join(map(__lowercase , sys.version_info[:3] ) )
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
'''line of `from __future__ import annotations` which opts in union types as '''
'''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
'''support Python versions that lower than 3.10, you need to use '''
'''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
'''`X | None`.''' ) from ex
raise
for field in dataclasses.fields(__lowercase ):
if not field.init:
continue
lowerCAmelCase_ : Optional[int] = type_hints[field.name]
self._parse_dataclass_field(__lowercase , __lowercase )
def lowercase_ ( self , __lowercase=None , __lowercase=False , __lowercase=True , __lowercase=None , __lowercase=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
lowerCAmelCase_ : str = []
if args_filename:
args_files.append(Path(__lowercase ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('''.args''' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
lowerCAmelCase_ : str = ArgumentParser()
args_file_parser.add_argument(__lowercase , type=__lowercase , action='''append''' )
# Use only remaining args for further parsing (remove the args_file_flag)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = args_file_parser.parse_known_args(args=__lowercase )
lowerCAmelCase_ : int = vars(__lowercase ).get(args_file_flag.lstrip('''-''' ) , __lowercase )
if cmd_args_file_paths:
args_files.extend([Path(__lowercase ) for p in cmd_args_file_paths] )
lowerCAmelCase_ : Dict = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
lowerCAmelCase_ : Any = file_args + args if args is not None else file_args + sys.argv[1:]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = self.parse_known_args(args=__lowercase )
lowerCAmelCase_ : Any = []
for dtype in self.dataclass_types:
lowerCAmelCase_ : str = {f.name for f in dataclasses.fields(__lowercase ) if f.init}
lowerCAmelCase_ : str = {k: v for k, v in vars(__lowercase ).items() if k in keys}
for k in keys:
delattr(__lowercase , __lowercase )
lowerCAmelCase_ : Optional[int] = dtype(**__lowercase )
outputs.append(__lowercase )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(__lowercase )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""" )
return (*outputs,)
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
lowerCAmelCase_ : int = set(args.keys() )
lowerCAmelCase_ : str = []
for dtype in self.dataclass_types:
lowerCAmelCase_ : int = {f.name for f in dataclasses.fields(__lowercase ) if f.init}
lowerCAmelCase_ : List[str] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
lowerCAmelCase_ : List[str] = dtype(**__lowercase )
outputs.append(__lowercase )
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__lowercase )}""" )
return tuple(__lowercase )
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
with open(Path(__lowercase ) , encoding='''utf-8''' ) as open_json_file:
lowerCAmelCase_ : Dict = json.loads(open_json_file.read() )
lowerCAmelCase_ : str = self.parse_dict(__lowercase , allow_extra_keys=__lowercase )
return tuple(__lowercase )
def lowercase_ ( self , __lowercase , __lowercase = False ) -> Tuple[DataClass, ...]:
lowerCAmelCase_ : Optional[Any] = self.parse_dict(yaml.safe_load(Path(__lowercase ).read_text() ) , allow_extra_keys=__lowercase )
return tuple(__lowercase ) | 262 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
__lowerCAmelCase = False
@skip_mps
class __magic_name__ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
lowerCAmelCase : List[Any] = StableDiffusionAttendAndExcitePipeline
lowerCAmelCase : str = False
lowerCAmelCase : Optional[Any] = TEXT_TO_IMAGE_PARAMS
lowerCAmelCase : Optional[Any] = TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
lowerCAmelCase : str = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase : List[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def __lowercase ( cls : Tuple ):
super().setUpClass()
torch.use_deterministic_algorithms(_UpperCAmelCase )
@classmethod
def __lowercase ( cls : List[str] ):
super().tearDownClass()
torch.use_deterministic_algorithms(_UpperCAmelCase )
def __lowercase ( self : str ):
torch.manual_seed(0 )
_a : int = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=1 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,cross_attention_dim=32 ,attention_head_dim=(2, 4) ,use_linear_projection=_UpperCAmelCase ,)
_a : Dict = DDIMScheduler(
beta_start=0.0_00_85 ,beta_end=0.0_12 ,beta_schedule='scaled_linear' ,clip_sample=_UpperCAmelCase ,set_alpha_to_one=_UpperCAmelCase ,)
torch.manual_seed(0 )
_a : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,latent_channels=4 ,sample_size=128 ,)
torch.manual_seed(0 )
_a : Any = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1000 ,hidden_act='gelu' ,projection_dim=512 ,)
_a : List[Any] = CLIPTextModel(_UpperCAmelCase )
_a : List[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_a : int = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __lowercase ( self : Optional[Any] ,_UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Optional[int]=0 ):
if str(_UpperCAmelCase ).startswith('mps' ):
_a : Union[str, Any] = torch.manual_seed(_UpperCAmelCase )
else:
_a : Tuple = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
_a : Union[str, Any] = {
'prompt': 'a cat and a frog',
'token_indices': [2, 5],
'generator': generator,
'num_inference_steps': 1,
'guidance_scale': 6.0,
'output_type': 'numpy',
'max_iter_to_alter': 2,
'thresholds': {0: 0.7},
}
return inputs
def __lowercase ( self : Union[str, Any] ):
_a : Any = 'cpu'
_a : Optional[Any] = self.get_dummy_components()
_a : Optional[Any] = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_a : Dict = self.get_dummy_inputs(_UpperCAmelCase )
_a : int = pipe(**_UpperCAmelCase ).images
_a : Dict = image[0, -3:, -3:, -1]
self.assertEqual(image.shape ,(1, 64, 64, 3) )
_a : int = np.array(
[0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96] )
_a : Dict = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase ,1E-3 )
def __lowercase ( self : Union[str, Any] ):
super().test_cpu_offload_forward_pass(expected_max_diff=5E-4 )
def __lowercase ( self : List[Any] ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __lowercase ( self : Union[str, Any] ):
self._test_inference_batch_single_identical(batch_size=2 ,expected_max_diff=7E-4 )
def __lowercase ( self : Any ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def __lowercase ( self : Optional[Any] ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4 )
def __lowercase ( self : Optional[Any] ):
super().test_save_load_local(expected_max_difference=5E-4 )
def __lowercase ( self : List[str] ):
super().test_save_load_optional_components(expected_max_difference=4E-4 )
@require_torch_gpu
@slow
class __magic_name__ ( unittest.TestCase ):
@classmethod
def __lowercase ( cls : str ):
super().setUpClass()
torch.use_deterministic_algorithms(_UpperCAmelCase )
@classmethod
def __lowercase ( cls : Dict ):
super().tearDownClass()
torch.use_deterministic_algorithms(_UpperCAmelCase )
def __lowercase ( self : Dict ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : List[Any] ):
_a : List[str] = torch.manual_seed(51 )
_a : str = StableDiffusionAttendAndExcitePipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' ,safety_checker=_UpperCAmelCase ,torch_dtype=torch.floataa )
pipe.to('cuda' )
_a : List[Any] = 'a painting of an elephant with glasses'
_a : Any = [5, 7]
_a : List[Any] = pipe(
prompt=_UpperCAmelCase ,token_indices=_UpperCAmelCase ,guidance_scale=7.5 ,generator=_UpperCAmelCase ,num_inference_steps=5 ,max_iter_to_alter=5 ,output_type='numpy' ,).images[0]
_a : Any = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy' )
assert np.abs((expected_image - image).max() ) < 5E-1
| 89 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCAmelCase ( lowerCAmelCase_ )-> Union[str, Any]:
return EnvironmentCommand()
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def lowercase_ ( __lowercase ) -> List[Any]:
lowerCAmelCase_ : List[str] = parser.add_parser('''env''' )
download_parser.set_defaults(func=__lowercase )
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Optional[Any] = huggingface_hub.__version__
lowerCAmelCase_ : str = '''not installed'''
lowerCAmelCase_ : str = '''NA'''
if is_torch_available():
import torch
lowerCAmelCase_ : Any = torch.__version__
lowerCAmelCase_ : str = torch.cuda.is_available()
lowerCAmelCase_ : List[str] = '''not installed'''
if is_transformers_available():
import transformers
lowerCAmelCase_ : Any = transformers.__version__
lowerCAmelCase_ : Optional[Any] = '''not installed'''
if is_accelerate_available():
import accelerate
lowerCAmelCase_ : List[Any] = accelerate.__version__
lowerCAmelCase_ : List[str] = '''not installed'''
if is_xformers_available():
import xformers
lowerCAmelCase_ : Optional[Any] = xformers.__version__
lowerCAmelCase_ : int = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(__lowercase ) )
return info
@staticmethod
def lowercase_ ( __lowercase ) -> str:
return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n" | 262 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = '''ssube/stable-diffusion-x4-upscaler-onnx'''
def lowercase_ ( self , lowerCamelCase__=0 ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = floats_tensor((1, 3, 128, 128) , rng=random.Random(lowerCamelCase__ ) )
__lowerCamelCase = torch.manual_seed(lowerCamelCase__ )
__lowerCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array(
[0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array(
[0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array(
[0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> int:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array(
[0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__lowerCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = self.get_dummy_inputs()
__lowerCamelCase = pipe(**lowerCamelCase__ ).images
__lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array(
[0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = ort.SessionOptions()
__lowerCamelCase = False
return options
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((128, 128) )
# using the PNDM scheduler by default
__lowerCamelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(
'ssube/stable-diffusion-x4-upscaler-onnx' , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__lowerCamelCase = init_image.resize((128, 128) )
__lowerCamelCase = LMSDiscreteScheduler.from_pretrained(
'ssube/stable-diffusion-x4-upscaler-onnx' , subfolder='scheduler' )
__lowerCamelCase = OnnxStableDiffusionUpscalePipeline.from_pretrained(
'ssube/stable-diffusion-x4-upscaler-onnx' , scheduler=lowerCamelCase__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__lowerCamelCase = 'A fantasy landscape, trending on artstation'
__lowerCamelCase = torch.manual_seed(0 )
__lowerCamelCase = pipe(
prompt=lowerCamelCase__ , image=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=20 , generator=lowerCamelCase__ , output_type='np' , )
__lowerCamelCase = output.images
__lowerCamelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
__lowerCamelCase = np.array(
[0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 90 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = JukeboxTokenizer
SCREAMING_SNAKE_CASE__ : int = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
import torch
lowerCAmelCase_ : Union[str, Any] = JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
lowerCAmelCase_ : Any = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : List[str] = [
torch.tensor([[
0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
7_6, 7_6]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def lowercase_ ( self ) -> List[Any]:
import torch
lowerCAmelCase_ : Any = JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
lowerCAmelCase_ : str = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : Tuple = [
torch.tensor([[
0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 7_7, 7_7]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) ) | 262 | 0 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = """T5Config"""
def _A (__a , __a , __a ) -> jnp.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = jnp.zeros_like(__a )
SCREAMING_SNAKE_CASE_ : Dict = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
SCREAMING_SNAKE_CASE_ : int = shifted_input_ids.at[:, 0].set(__a )
SCREAMING_SNAKE_CASE_ : Optional[int] = jnp.where(shifted_input_ids == -1_00 , __a , __a )
return shifted_input_ids
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "mt5"
__UpperCamelCase = MTaConfig
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "mt5"
__UpperCamelCase = MTaConfig
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "mt5"
__UpperCamelCase = MTaConfig
| 91 |
from __future__ import annotations
import requests
def lowerCAmelCase ( lowerCAmelCase_ )-> dict:
lowerCAmelCase_ : List[Any] = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(lowerCAmelCase_ ).json()
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> list[dict]:
lowerCAmelCase_ : List[Any] = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
lowerCAmelCase_ : Tuple = requests.get(lowerCAmelCase_ ).json()[:max_stories]
return [get_hackernews_story(lowerCAmelCase_ ) for story_id in story_ids]
def lowerCAmelCase ( lowerCAmelCase_ = 10 )-> str:
lowerCAmelCase_ : Optional[Any] = hackernews_top_stories(lowerCAmelCase_ )
return "\n".join('''* [{title}]({url})'''.format(**lowerCAmelCase_ ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown()) | 262 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""PLBartTokenizer"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PLBartForCausalLM""",
"""PLBartForConditionalGeneration""",
"""PLBartForSequenceClassification""",
"""PLBartModel""",
"""PLBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 92 |
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
_UpperCAmelCase : List[str] =get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_UpperCAmelCase : Optional[int] =25_0004
_UpperCAmelCase : Tuple =25_0020
@require_sentencepiece
@require_tokenizers
class snake_case__( UpperCAmelCase__, unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = MBartTokenizer
SCREAMING_SNAKE_CASE__ : Dict = MBartTokenizerFast
SCREAMING_SNAKE_CASE__ : Tuple = True
SCREAMING_SNAKE_CASE__ : List[str] = True
def lowercase_ ( self ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase_ : str = MBartTokenizer(__lowercase , keep_accents=__lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : Optional[int] = MBartTokenizer(__lowercase , keep_accents=__lowercase )
lowerCAmelCase_ : Dict = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowerCAmelCase_ : Optional[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [
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''',
'''.''',
] , )
lowerCAmelCase_ : Dict = tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(
__lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCAmelCase_ : Union[str, Any] = tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [
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 lowercase_ ( 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
lowerCAmelCase_ : int = (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})""" ):
lowerCAmelCase_ : Optional[int] = self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : int = self.tokenizer_class.from_pretrained(__lowercase , **__lowercase )
lowerCAmelCase_ : Tuple = tempfile.mkdtemp()
lowerCAmelCase_ : Union[str, Any] = tokenizer_r.save_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.save_pretrained(__lowercase )
# 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 ) )
lowerCAmelCase_ : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Tuple = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : Dict = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=True
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : int = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Tuple = tokenizer_p.save_pretrained(__lowercase )
# Checks it save with the same files
self.assertSequenceEqual(__lowercase , __lowercase )
# Checks everything loads correctly in the same way
lowerCAmelCase_ : Optional[int] = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
# Save tokenizer rust, legacy_format=False
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : List[str] = tokenizer_r.save_pretrained(__lowercase , legacy_format=__lowercase )
lowerCAmelCase_ : Optional[int] = tokenizer_p.save_pretrained(__lowercase )
# 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
lowerCAmelCase_ : Dict = tokenizer_r.from_pretrained(__lowercase )
lowerCAmelCase_ : List[Any] = tokenizer_p.from_pretrained(__lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowercase , __lowercase ) )
shutil.rmtree(__lowercase )
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = """facebook/mbart-large-en-ro"""
SCREAMING_SNAKE_CASE__ : int = [
""" 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.""",
]
SCREAMING_SNAKE_CASE__ : Optional[int] = [
"""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.""",
]
SCREAMING_SNAKE_CASE__ : str = [8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]
@classmethod
def lowercase_ ( cls ) -> Optional[int]:
lowerCAmelCase_ : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
lowerCAmelCase_ : Optional[Any] = 1
return cls
def lowercase_ ( self ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 2_5_0_0_2_0 )
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
def lowercase_ ( self ) -> Any:
self.assertIn(__lowercase , self.tokenizer.all_special_ids )
lowerCAmelCase_ : Union[str, Any] = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
lowerCAmelCase_ : Tuple = self.tokenizer.decode(__lowercase , skip_special_tokens=__lowercase )
lowerCAmelCase_ : List[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertNotIn(self.tokenizer.eos_token , __lowercase )
def lowercase_ ( self ) -> Any:
lowerCAmelCase_ : Union[str, Any] = ['''this is gunna be a long sentence ''' * 2_0]
assert isinstance(src_text[0] , __lowercase )
lowerCAmelCase_ : str = 1_0
lowerCAmelCase_ : Tuple = self.tokenizer(__lowercase , max_length=__lowercase , truncation=__lowercase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __lowercase )
self.assertEqual(len(__lowercase ) , __lowercase )
def lowercase_ ( self ) -> int:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def lowercase_ ( self ) -> Dict:
lowerCAmelCase_ : Any = tempfile.mkdtemp()
lowerCAmelCase_ : int = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__lowercase )
lowerCAmelCase_ : Optional[Any] = MBartTokenizer.from_pretrained(__lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __lowercase )
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
lowerCAmelCase_ : Tuple = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowercase , return_tensors='''pt''' )
lowerCAmelCase_ : Tuple = 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 lowercase_ ( self ) -> List[Any]:
lowerCAmelCase_ : str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
lowerCAmelCase_ : int = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
lowerCAmelCase_ : str = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
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 lowercase_ ( self ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = self.tokenizer(self.src_text , padding=__lowercase , truncation=__lowercase , max_length=3 , return_tensors='''pt''' )
lowerCAmelCase_ : Any = self.tokenizer(
text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=1_0 , return_tensors='''pt''' )
lowerCAmelCase_ : int = targets['''input_ids''']
lowerCAmelCase_ : Optional[Any] = shift_tokens_right(__lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def lowercase_ ( self ) -> List[str]:
lowerCAmelCase_ : Any = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(__lowercase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 2_5_0_0_0_1,
} , ) | 262 | 0 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowercase : int = object()
# For specifying empty leaf dict `{}`
_lowercase : List[Any] = object()
def snake_case_ ( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
lowercase_ : List[Any] = tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(__SCREAMING_SNAKE_CASE ) - len(__SCREAMING_SNAKE_CASE ) + 1 ):
lowercase_ : Dict = [x.match(__SCREAMING_SNAKE_CASE ) for x, y in zip(__SCREAMING_SNAKE_CASE , ks[i:] )]
if matches and all(__SCREAMING_SNAKE_CASE ):
return True
return False
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
def replace(__SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[Any] ):
for rule, replacement in rules:
if _match(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return replacement
return val
return replace
def snake_case_ ( ):
"""simple docstring"""
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , __SCREAMING_SNAKE_CASE )),
(("transformer", "wte", "embedding"), P('''mp''' , __SCREAMING_SNAKE_CASE )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__SCREAMING_SNAKE_CASE , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , __SCREAMING_SNAKE_CASE )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__SCREAMING_SNAKE_CASE , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , __SCREAMING_SNAKE_CASE )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
lowercase_ : Optional[Any] = _get_partition_rules()
lowercase_ : List[Any] = _replacement_rules(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[int] = {k: _unmatched for k in flatten_dict(__SCREAMING_SNAKE_CASE )}
lowercase_ : Optional[Any] = {k: replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__SCREAMING_SNAKE_CASE ) )
| 93 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = "cpu" , lowerCAmelCase_ = None )-> None:
lowerCAmelCase_ : str = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase_ , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowerCAmelCase_ : int = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Tuple = src_path
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
fire.Fire(convert) | 262 | 0 |
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
"""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))
| 94 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel | 262 | 0 |
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
while a != 0:
a__ , a__ : Optional[Any] =b % a, a
return b
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if gcd(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) != 1:
a__ : Optional[Any] =f'''mod inverse of {a!r} and {m!r} does not exist'''
raise ValueError(SCREAMING_SNAKE_CASE )
a__ , a__ , a__ : Dict =1, 0, a
a__ , a__ , a__ : List[Any] =0, 1, m
while va != 0:
a__ : str =ua // va
a__ , a__ , a__ , a__ , a__ , a__ : Dict =(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 95 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : str ={
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = """vit_mae"""
def __init__( self , __lowercase=7_6_8 , __lowercase=1_2 , __lowercase=1_2 , __lowercase=3_0_7_2 , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1e-12 , __lowercase=2_2_4 , __lowercase=1_6 , __lowercase=3 , __lowercase=True , __lowercase=1_6 , __lowercase=5_1_2 , __lowercase=8 , __lowercase=2_0_4_8 , __lowercase=0.75 , __lowercase=False , **__lowercase , ) -> str:
super().__init__(**__lowercase )
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Any = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : int = intermediate_size
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : int = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : Union[str, Any] = image_size
lowerCAmelCase_ : Optional[int] = patch_size
lowerCAmelCase_ : Tuple = num_channels
lowerCAmelCase_ : List[str] = qkv_bias
lowerCAmelCase_ : List[Any] = decoder_num_attention_heads
lowerCAmelCase_ : int = decoder_hidden_size
lowerCAmelCase_ : Optional[int] = decoder_num_hidden_layers
lowerCAmelCase_ : Tuple = decoder_intermediate_size
lowerCAmelCase_ : Tuple = mask_ratio
lowerCAmelCase_ : Any = norm_pix_loss | 262 | 0 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _snake_case ( lowercase__ ):
if isinstance(lowercase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class lowerCAmelCase__ :
'''simple docstring'''
def A_ ( self , lowercase , lowercase ):
pass
def A_ ( self ):
pass
def A_ ( self ):
pass
def A_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase=None , **lowercase ):
_lowerCamelCase : Tuple = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase , lowercase )
_lowerCamelCase : int = TFVisionTextDualEncoderModel(lowercase )
_lowerCamelCase : Optional[Any] = model(input_ids=lowercase , pixel_values=lowercase , attention_mask=lowercase )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], config.projection_dim) )
def A_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase=None , **lowercase ):
_lowerCamelCase, _lowerCamelCase : Dict = self.get_vision_text_model(lowercase , lowercase )
_lowerCamelCase : Optional[Any] = TFVisionTextDualEncoderModel(vision_model=lowercase , text_model=lowercase )
_lowerCamelCase : List[str] = model(input_ids=lowercase , pixel_values=lowercase , attention_mask=lowercase )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], model.config.projection_dim) )
def A_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase=None , **lowercase ):
_lowerCamelCase, _lowerCamelCase : List[Any] = self.get_vision_text_model(lowercase , lowercase )
_lowerCamelCase : Tuple = {'vision_model': vision_model, 'text_model': text_model}
_lowerCamelCase : Dict = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase )
_lowerCamelCase : Dict = model(input_ids=lowercase , pixel_values=lowercase , attention_mask=lowercase )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], model.config.projection_dim) )
def A_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase=None , **lowercase ):
_lowerCamelCase, _lowerCamelCase : int = self.get_vision_text_model(lowercase , lowercase )
_lowerCamelCase : List[Any] = TFVisionTextDualEncoderModel(vision_model=lowercase , text_model=lowercase )
_lowerCamelCase : Union[str, Any] = model(input_ids=lowercase , pixel_values=lowercase , attention_mask=lowercase )
_lowerCamelCase : Union[str, Any] = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = TFVisionTextDualEncoderModel.from_pretrained(lowercase )
_lowerCamelCase : Tuple = model(input_ids=lowercase , pixel_values=lowercase , attention_mask=lowercase )
_lowerCamelCase : List[str] = after_output[0].numpy()
_lowerCamelCase : Optional[int] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase , 1E-5 )
def A_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase=None , **lowercase ):
_lowerCamelCase, _lowerCamelCase : List[Any] = self.get_vision_text_model(lowercase , lowercase )
_lowerCamelCase : List[str] = TFVisionTextDualEncoderModel(vision_model=lowercase , text_model=lowercase )
_lowerCamelCase : Tuple = model(
input_ids=lowercase , pixel_values=lowercase , attention_mask=lowercase , output_attentions=lowercase )
_lowerCamelCase : List[Any] = output.vision_model_output.attentions
self.assertEqual(len(lowercase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCamelCase : Any = to_atuple(vision_model.config.image_size )
_lowerCamelCase : List[Any] = to_atuple(vision_model.config.patch_size )
_lowerCamelCase : Optional[int] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCamelCase : Tuple = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCamelCase : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def A_ ( self , lowercase , lowercase , lowercase ):
_lowerCamelCase : Tuple = np.abs((a - b) ).max()
self.assertLessEqual(lowercase , lowercase , F'''Difference between torch and flax is {diff} (>= {tol}).''' )
def A_ ( self ):
_lowerCamelCase : Any = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**lowercase )
def A_ ( self ):
_lowerCamelCase : Optional[Any] = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase )
def A_ ( self ):
_lowerCamelCase : Optional[int] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase )
def A_ ( self ):
_lowerCamelCase : List[Any] = self.prepare_config_and_inputs()
self.check_save_load(**lowercase )
def A_ ( self ):
_lowerCamelCase : Any = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase )
@slow
def A_ ( self ):
_lowerCamelCase, _lowerCamelCase : int = self.get_pretrained_model_and_inputs()
_lowerCamelCase : str = model_a(**lowercase )
_lowerCamelCase : List[Any] = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = TFVisionTextDualEncoderModel.from_pretrained(lowercase )
_lowerCamelCase : Union[str, Any] = model_a(**lowercase )
_lowerCamelCase : Optional[Any] = after_outputs[0].numpy()
_lowerCamelCase : Tuple = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase , 1E-5 )
@require_tf
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
_lowerCamelCase : Optional[Any] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'hf-internal-testing/tiny-random-vit' , 'hf-internal-testing/tiny-random-bert' )
_lowerCamelCase : Tuple = 13
_lowerCamelCase : Tuple = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCamelCase : Dict = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCamelCase : int = random_attention_mask([batch_size, 4] )
_lowerCamelCase : Any = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : int = TFViTModel(lowercase , name='vision_model' )
_lowerCamelCase : Union[str, Any] = TFBertModel(lowercase , name='text_model' )
return vision_model, text_model
def A_ ( self ):
_lowerCamelCase : Optional[int] = TFViTModelTester(self )
_lowerCamelCase : str = TFBertModelTester(self )
_lowerCamelCase : int = vit_model_tester.prepare_config_and_inputs()
_lowerCamelCase : str = bert_model_tester.prepare_config_and_inputs()
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Dict = vision_config_and_inputs
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : Union[str, Any] = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
_lowerCamelCase : int = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'Rocketknight1/tiny-random-deit-tf' , 'hf-internal-testing/tiny-random-roberta' )
_lowerCamelCase : Tuple = 13
_lowerCamelCase : Tuple = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCamelCase : Tuple = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCamelCase : str = random_attention_mask([batch_size, 4] )
_lowerCamelCase : Optional[Any] = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def A_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase=None , **lowercase ):
_lowerCamelCase, _lowerCamelCase : Union[str, Any] = self.get_vision_text_model(lowercase , lowercase )
_lowerCamelCase : Dict = TFVisionTextDualEncoderModel(vision_model=lowercase , text_model=lowercase )
_lowerCamelCase : List[Any] = model(
input_ids=lowercase , pixel_values=lowercase , attention_mask=lowercase , output_attentions=lowercase )
_lowerCamelCase : Union[str, Any] = output.vision_model_output.attentions
self.assertEqual(len(lowercase ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCamelCase : Any = to_atuple(vision_model.config.image_size )
_lowerCamelCase : Union[str, Any] = to_atuple(vision_model.config.patch_size )
_lowerCamelCase : Union[str, Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCamelCase : Optional[int] = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCamelCase : List[Any] = output.text_model_output.attentions
self.assertEqual(len(lowercase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : List[Any] = TFDeiTModel(lowercase , name='vision_model' )
_lowerCamelCase : List[str] = TFRobertaModel(lowercase , name='text_model' )
return vision_model, text_model
def A_ ( self ):
_lowerCamelCase : Any = TFDeiTModelTester(self )
_lowerCamelCase : Union[str, Any] = TFRobertaModelTester(self )
_lowerCamelCase : Optional[int] = vit_model_tester.prepare_config_and_inputs()
_lowerCamelCase : int = bert_model_tester.prepare_config_and_inputs()
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Dict = vision_config_and_inputs
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : Dict = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
_lowerCamelCase : Dict = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'Rocketknight1/tiny-random-clip-tf' , 'hf-internal-testing/tiny-random-bert' )
_lowerCamelCase : Any = 13
_lowerCamelCase : List[Any] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCamelCase : Any = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCamelCase : List[Any] = random_attention_mask([batch_size, 4] )
_lowerCamelCase : List[Any] = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[int] = TFCLIPVisionModel(lowercase , name='vision_model' )
_lowerCamelCase : List[str] = TFBertModel(lowercase , name='text_model' )
return vision_model, text_model
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = TFCLIPVisionModelTester(self )
_lowerCamelCase : Optional[Any] = TFBertModelTester(self )
_lowerCamelCase : str = clip_model_tester.prepare_config_and_inputs()
_lowerCamelCase : Optional[int] = bert_model_tester.prepare_config_and_inputs()
_lowerCamelCase, _lowerCamelCase : List[Any] = vision_config_and_inputs
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : List[str] = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = TFVisionTextDualEncoderModel.from_pretrained(
'clip-italian/clip-italian' , logit_scale_init_value=1.0 , from_pt=lowercase )
_lowerCamelCase : str = VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian' )
_lowerCamelCase : Optional[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
_lowerCamelCase : List[Any] = processor(
text=['una foto di un gatto', 'una foto di un cane'] , images=lowercase , padding=lowercase , return_tensors='np' )
_lowerCamelCase : List[Any] = model(**lowercase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_lowerCamelCase : int = np.array([[1.2_28_47_27, 0.3_10_41_22]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , lowercase , atol=1E-3 ) ) | 96 |
def lowerCAmelCase ( lowerCAmelCase_ = 10**9 )-> int:
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[int] = 2
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : str = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
lowerCAmelCase_ : Any = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""") | 262 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=7 , UpperCamelCase_=3 , UpperCamelCase_=30 , UpperCamelCase_=400 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_=1 / 255 , UpperCamelCase_=True , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=[0.5, 0.5, 0.5] , UpperCamelCase_=True , ):
'''simple docstring'''
UpperCamelCase__ :Tuple = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
UpperCamelCase__ :Tuple = parent
UpperCamelCase__ :Dict = batch_size
UpperCamelCase__ :List[str] = num_channels
UpperCamelCase__ :List[str] = min_resolution
UpperCamelCase__ :Dict = max_resolution
UpperCamelCase__ :Optional[int] = do_resize
UpperCamelCase__ :Any = size
UpperCamelCase__ :List[str] = do_rescale
UpperCamelCase__ :List[str] = rescale_factor
UpperCamelCase__ :List[Any] = do_normalize
UpperCamelCase__ :Optional[Any] = image_mean
UpperCamelCase__ :Dict = image_std
UpperCamelCase__ :Optional[int] = do_pad
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
if not batched:
UpperCamelCase__ :Any = image_inputs[0]
if isinstance(UpperCamelCase_ , Image.Image ):
UpperCamelCase__ , UpperCamelCase__ :int = image.size
else:
UpperCamelCase__ , UpperCamelCase__ :Any = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase__ :Tuple = int(self.size['''shortest_edge'''] * h / w )
UpperCamelCase__ :List[str] = self.size['''shortest_edge''']
elif w > h:
UpperCamelCase__ :List[Any] = self.size['''shortest_edge''']
UpperCamelCase__ :int = int(self.size['''shortest_edge'''] * w / h )
else:
UpperCamelCase__ :Union[str, Any] = self.size['''shortest_edge''']
UpperCamelCase__ :Optional[Any] = self.size['''shortest_edge''']
else:
UpperCamelCase__ :Optional[int] = []
for image in image_inputs:
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase__ :Tuple = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[0] )[0]
UpperCamelCase__ :Optional[Any] = max(UpperCamelCase_ , key=lambda UpperCamelCase_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase ( A__ , unittest.TestCase ):
"""simple docstring"""
_a = DetrImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Dict = DetrImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase_ , '''image_mean''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''image_std''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_normalize''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_rescale''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''rescale_factor''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_resize''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''size''' ) )
self.assertTrue(hasattr(UpperCamelCase_ , '''do_pad''' ) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
UpperCamelCase__ :str = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase_ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ :Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , Image.Image )
# Test not batched input
UpperCamelCase__ :Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ , UpperCamelCase__ :Optional[Any] = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
UpperCamelCase__ :str = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ :Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase_ , numpify=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , np.ndarray )
# Test not batched input
UpperCamelCase__ :Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :List[str] = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :Optional[Any] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Optional[Any] = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ :Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase_ , torchify=UpperCamelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase_ , torch.Tensor )
# Test not batched input
UpperCamelCase__ :Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Tuple = self.image_processor_tester.get_expected_values(UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ :List[str] = image_processing(UpperCamelCase_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ :Optional[Any] = self.image_processor_tester.get_expected_values(UpperCamelCase_ , batched=UpperCamelCase_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Tuple = json.loads(f.read() )
UpperCamelCase__ :Dict = {'''image_id''': 39769, '''annotations''': target}
# encode them
UpperCamelCase__ :int = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''' )
UpperCamelCase__ :Tuple = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :Optional[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Dict = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :int = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :int = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :List[str] = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :int = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :str = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify orig_size
UpperCamelCase__ :Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :Optional[Any] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
UpperCamelCase__ :Any = json.loads(f.read() )
UpperCamelCase__ :str = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
UpperCamelCase__ :str = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
UpperCamelCase__ :Optional[int] = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''' )
UpperCamelCase__ :Tuple = image_processing(images=UpperCamelCase_ , annotations=UpperCamelCase_ , masks_path=UpperCamelCase_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ :Optional[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , UpperCamelCase_ )
UpperCamelCase__ :Dict = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , UpperCamelCase_ , atol=1e-4 ) )
# verify area
UpperCamelCase__ :Optional[int] = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , UpperCamelCase_ ) )
# verify boxes
UpperCamelCase__ :Tuple = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , UpperCamelCase_ )
UpperCamelCase__ :int = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , UpperCamelCase_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase__ :Dict = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , UpperCamelCase_ ) )
# verify is_crowd
UpperCamelCase__ :Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , UpperCamelCase_ ) )
# verify class_labels
UpperCamelCase__ :str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , UpperCamelCase_ ) )
# verify masks
UpperCamelCase__ :Tuple = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , UpperCamelCase_ )
# verify orig_size
UpperCamelCase__ :List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , UpperCamelCase_ ) )
# verify size
UpperCamelCase__ :Any = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , UpperCamelCase_ ) ) | 97 |
import inspect
import unittest
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def lowercase_ ( self ) -> List[str]:
import diffusers
from diffusers.dependency_versions_table import deps
lowerCAmelCase_ : Any = inspect.getmembers(__lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowerCAmelCase_ : Optional[int] = '''k-diffusion'''
elif backend == "invisible_watermark":
lowerCAmelCase_ : Dict = '''invisible-watermark'''
assert backend in deps, f"""{backend} is not in the deps table!""" | 262 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Dict ):
UpperCAmelCase__ = []
def __lowerCAmelCase ( self : int ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : List[Any] ,**lowerCamelCase__ : int ):
self.events.append('on_init_end' )
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Any ,**lowerCamelCase__ : Optional[Any] ):
self.events.append('on_train_begin' )
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : int ,**lowerCamelCase__ : int ):
self.events.append('on_train_end' )
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Any ,**lowerCamelCase__ : Optional[Any] ):
self.events.append('on_epoch_begin' )
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : Dict ,lowerCamelCase__ : str ,lowerCamelCase__ : Dict ,**lowerCamelCase__ : List[Any] ):
self.events.append('on_epoch_end' )
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : str ,lowerCamelCase__ : Union[str, Any] ,**lowerCamelCase__ : Union[str, Any] ):
self.events.append('on_step_begin' )
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : Any ,lowerCamelCase__ : Any ,lowerCamelCase__ : List[str] ,**lowerCamelCase__ : Optional[Any] ):
self.events.append('on_step_end' )
def __lowerCAmelCase ( self : Optional[int] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : List[str] ,**lowerCamelCase__ : List[str] ):
self.events.append('on_evaluate' )
def __lowerCAmelCase ( self : Any ,lowerCamelCase__ : int ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Union[str, Any] ,**lowerCamelCase__ : Union[str, Any] ):
self.events.append('on_predict' )
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Any ,**lowerCamelCase__ : Optional[Any] ):
self.events.append('on_save' )
def __lowerCAmelCase ( self : Tuple ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Optional[int] ,**lowerCamelCase__ : Union[str, Any] ):
self.events.append('on_log' )
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Any ,lowerCamelCase__ : List[Any] ,**lowerCamelCase__ : Optional[int] ):
self.events.append('on_prediction_step' )
@require_torch
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase__ = tempfile.mkdtemp()
def __lowerCAmelCase ( self : int ):
shutil.rmtree(self.output_dir )
def __lowerCAmelCase ( self : Optional[Any] ,lowerCamelCase__ : Optional[Any]=0 ,lowerCamelCase__ : str=0 ,lowerCamelCase__ : List[str]=64 ,lowerCamelCase__ : Tuple=64 ,lowerCamelCase__ : Union[str, Any]=None ,lowerCamelCase__ : Any=False ,**lowerCamelCase__ : List[Any] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
UpperCAmelCase__ = RegressionDataset(length=lowerCamelCase__ )
UpperCAmelCase__ = RegressionDataset(length=lowerCamelCase__ )
UpperCAmelCase__ = RegressionModelConfig(a=lowerCamelCase__ ,b=lowerCamelCase__ )
UpperCAmelCase__ = RegressionPreTrainedModel(lowerCamelCase__ )
UpperCAmelCase__ = TrainingArguments(self.output_dir ,disable_tqdm=lowerCamelCase__ ,report_to=[] ,**lowerCamelCase__ )
return Trainer(
lowerCamelCase__ ,lowerCamelCase__ ,train_dataset=lowerCamelCase__ ,eval_dataset=lowerCamelCase__ ,callbacks=lowerCamelCase__ ,)
def __lowerCAmelCase ( self : Optional[int] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : List[str] ):
self.assertEqual(len(lowerCamelCase__ ) ,len(lowerCamelCase__ ) )
# Order doesn't matter
UpperCAmelCase__ = sorted(lowerCamelCase__ ,key=lambda lowerCamelCase__ : cb.__name__ if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else cb.__class__.__name__ )
UpperCAmelCase__ = sorted(lowerCamelCase__ ,key=lambda lowerCamelCase__ : cb.__name__ if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else cb.__class__.__name__ )
for cba, cba in zip(lowerCamelCase__ ,lowerCamelCase__ ):
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) and isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
elif isinstance(lowerCamelCase__ ,lowerCamelCase__ ) and not isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
self.assertEqual(lowerCamelCase__ ,cba.__class__ )
elif not isinstance(lowerCamelCase__ ,lowerCamelCase__ ) and isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
self.assertEqual(cba.__class__ ,lowerCamelCase__ )
else:
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def __lowerCAmelCase ( self : List[str] ,lowerCamelCase__ : Optional[int] ):
UpperCAmelCase__ = ['on_init_end', 'on_train_begin']
UpperCAmelCase__ = 0
UpperCAmelCase__ = len(trainer.get_eval_dataloader() )
UpperCAmelCase__ = ['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('on_epoch_begin' )
for _ in range(lowerCamelCase__ ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('on_log' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('on_save' )
expected_events.append('on_epoch_end' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def __lowerCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase__ = self.get_trainer()
UpperCAmelCase__ = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
# Callbacks passed at init are added to the default callbacks
UpperCAmelCase__ = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(lowerCamelCase__ )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
UpperCAmelCase__ = self.get_trainer(disable_tqdm=lowerCamelCase__ )
UpperCAmelCase__ = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
def __lowerCAmelCase ( self : Optional[int] ):
UpperCAmelCase__ = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
UpperCAmelCase__ = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(lowerCamelCase__ )
expected_callbacks.remove(lowerCamelCase__ )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
UpperCAmelCase__ = self.get_trainer()
UpperCAmelCase__ = trainer.pop_callback(lowerCamelCase__ )
self.assertEqual(cb.__class__ ,lowerCamelCase__ )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
trainer.add_callback(lowerCamelCase__ )
expected_callbacks.insert(0 ,lowerCamelCase__ )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
# We can also add, pop, or remove by instance
UpperCAmelCase__ = self.get_trainer()
UpperCAmelCase__ = trainer.callback_handler.callbacks[0]
trainer.remove_callback(lowerCamelCase__ )
expected_callbacks.remove(lowerCamelCase__ )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
UpperCAmelCase__ = self.get_trainer()
UpperCAmelCase__ = trainer.callback_handler.callbacks[0]
UpperCAmelCase__ = trainer.pop_callback(lowerCamelCase__ )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
trainer.add_callback(lowerCamelCase__ )
expected_callbacks.insert(0 ,lowerCamelCase__ )
self.check_callbacks_equality(trainer.callback_handler.callbacks ,lowerCamelCase__ )
def __lowerCAmelCase ( self : Tuple ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='ignore' ,category=lowerCamelCase__ )
UpperCAmelCase__ = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
UpperCAmelCase__ = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCamelCase__ ,self.get_expected_events(lowerCamelCase__ ) )
# Independent log/save/eval
UpperCAmelCase__ = self.get_trainer(callbacks=[MyTestTrainerCallback] ,logging_steps=5 )
trainer.train()
UpperCAmelCase__ = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCamelCase__ ,self.get_expected_events(lowerCamelCase__ ) )
UpperCAmelCase__ = self.get_trainer(callbacks=[MyTestTrainerCallback] ,save_steps=5 )
trainer.train()
UpperCAmelCase__ = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCamelCase__ ,self.get_expected_events(lowerCamelCase__ ) )
UpperCAmelCase__ = self.get_trainer(callbacks=[MyTestTrainerCallback] ,eval_steps=5 ,evaluation_strategy='steps' )
trainer.train()
UpperCAmelCase__ = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCamelCase__ ,self.get_expected_events(lowerCamelCase__ ) )
UpperCAmelCase__ = self.get_trainer(callbacks=[MyTestTrainerCallback] ,evaluation_strategy='epoch' )
trainer.train()
UpperCAmelCase__ = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCamelCase__ ,self.get_expected_events(lowerCamelCase__ ) )
# A bit of everything
UpperCAmelCase__ = self.get_trainer(
callbacks=[MyTestTrainerCallback] ,logging_steps=3 ,save_steps=10 ,eval_steps=5 ,evaluation_strategy='steps' ,)
trainer.train()
UpperCAmelCase__ = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowerCamelCase__ ,self.get_expected_events(lowerCamelCase__ ) )
# warning should be emitted for duplicated callbacks
with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
UpperCAmelCase__ = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] ,)
assert str(lowerCamelCase__ ) in warn_mock.call_args[0][0]
| 98 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_UpperCAmelCase : Any =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__lowercase , **__lowercase ) -> None:
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __lowercase , )
super().__init__(*__lowercase , **__lowercase ) | 262 | 0 |
from math import loga
def A_ ( A__ ) -> int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(A__ , A__ ):
raise TypeError('Input value must be a \'int\' type' )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99 |
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : Optional[int] = set()
# edges = list of graph's edges
lowerCAmelCase_ : List[str] = get_edges(lowerCAmelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = edges.pop()
chosen_vertices.add(lowerCAmelCase_ )
chosen_vertices.add(lowerCAmelCase_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(lowerCAmelCase_ )
return chosen_vertices
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}") | 262 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = len(UpperCamelCase_ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
__SCREAMING_SNAKE_CASE = i + 1
else:
__SCREAMING_SNAKE_CASE = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")
| 100 |
from math import sqrt
def lowerCAmelCase ( lowerCAmelCase_ )-> bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
lowerCAmelCase_ : Optional[int] = False
for divisor in range(2 , int(round(sqrt(lowerCAmelCase_ ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
lowerCAmelCase_ : Tuple = False
break
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'status' must been from type bool"
return status
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
lowerCAmelCase_ : Tuple = list(range(2 , n + 1 ) )
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(lowerCAmelCase_ ) ):
for j in range(i + 1 , len(lowerCAmelCase_ ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
lowerCAmelCase_ : str = 0
# filters actual prime numbers.
lowerCAmelCase_ : Optional[int] = [x for x in begin_list if x != 0]
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n > 2), "'N' must been an int and > 2"
lowerCAmelCase_ : List[Any] = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(lowerCAmelCase_ ):
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and number >= 0, "'number' must been an int and >= 0"
lowerCAmelCase_ : int = [] # this list will be returns of the function.
# potential prime number factors.
lowerCAmelCase_ : List[Any] = 2
lowerCAmelCase_ : Optional[int] = number
if number == 0 or number == 1:
ans.append(lowerCAmelCase_ )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(lowerCAmelCase_ ):
while quotient != 1:
if is_prime(lowerCAmelCase_ ) and (quotient % factor == 0):
ans.append(lowerCAmelCase_ )
quotient /= factor
else:
factor += 1
else:
ans.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type list"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[int]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : Dict = 0
# prime factorization of 'number'
lowerCAmelCase_ : Any = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = max(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' bust been an int and >= 0"
lowerCAmelCase_ : List[Any] = 0
# prime factorization of 'number'
lowerCAmelCase_ : Dict = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = min(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'ans' must been from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Optional[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 == 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 == 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "'number' must been an int"
assert isinstance(number % 2 != 0 , lowerCAmelCase_ ), "compare bust been from type bool"
return number % 2 != 0
def lowerCAmelCase ( lowerCAmelCase_ )-> List[str]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (number > 2) and is_even(lowerCAmelCase_ )
), "'number' must been an int, even and > 2"
lowerCAmelCase_ : str = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
lowerCAmelCase_ : int = get_prime_numbers(lowerCAmelCase_ )
lowerCAmelCase_ : List[str] = len(lowerCAmelCase_ )
# run variable for while-loops.
lowerCAmelCase_ : Union[str, Any] = 0
lowerCAmelCase_ : Tuple = None
# exit variable. for break up the loops
lowerCAmelCase_ : int = True
while i < len_pn and loop:
lowerCAmelCase_ : int = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
lowerCAmelCase_ : Tuple = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (len(lowerCAmelCase_ ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : int = 0
while numbera != 0:
lowerCAmelCase_ : str = numbera % numbera
lowerCAmelCase_ : List[Any] = numbera
lowerCAmelCase_ : Any = rest
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
lowerCAmelCase_ : List[Any] = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
lowerCAmelCase_ : int = prime_factorization(lowerCAmelCase_ )
elif numbera == 1 or numbera == 1:
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : Union[str, Any] = max(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Dict = 0
lowerCAmelCase_ : Union[str, Any] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
lowerCAmelCase_ : Tuple = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(max(lowerCAmelCase_ , lowerCAmelCase_ ) ):
ans *= n
else:
lowerCAmelCase_ : List[str] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
lowerCAmelCase_ : Optional[Any] = prime_fac_a.count(lowerCAmelCase_ )
for _ in range(lowerCAmelCase_ ):
ans *= n
done.append(lowerCAmelCase_ )
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'number' must been a positive int"
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : Optional[int] = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(lowerCAmelCase_ ):
ans += 1
# precondition
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and is_prime(
lowerCAmelCase_ ), "'ans' must been a prime number and from type int"
return ans
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
assert (
is_prime(lowerCAmelCase_ ) and is_prime(lowerCAmelCase_ ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
lowerCAmelCase_ : Union[str, Any] = p_number_a + 1 # jump to the next number
lowerCAmelCase_ : Optional[int] = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
while number < p_number_a:
ans.append(lowerCAmelCase_ )
number += 1
# fetch the next prime number.
while not is_prime(lowerCAmelCase_ ):
number += 1
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and ans[0] != p_number_a
and ans[len(lowerCAmelCase_ ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 1), "'n' must been int and >= 1"
lowerCAmelCase_ : List[Any] = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(lowerCAmelCase_ )
# precondition
assert ans[0] == 1 and ans[len(lowerCAmelCase_ ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> List[Any]:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number > 1
), "'number' must been an int and >= 1"
lowerCAmelCase_ : Union[str, Any] = get_divisors(lowerCAmelCase_ )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (divisors[0] == 1)
and (divisors[len(lowerCAmelCase_ ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
lowerCAmelCase_ : Optional[Any] = gcd(abs(lowerCAmelCase_ ) , abs(lowerCAmelCase_ ) )
# precondition
assert (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def lowerCAmelCase ( lowerCAmelCase_ )-> Tuple:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been a int and >= 0"
lowerCAmelCase_ : Any = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def lowerCAmelCase ( lowerCAmelCase_ )-> int:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (n >= 0), "'n' must been an int and >= 0"
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Union[str, Any] = 1 # this will be return
for _ in range(n - 1 ):
lowerCAmelCase_ : Union[str, Any] = ans
ans += fiba
lowerCAmelCase_ : Optional[Any] = tmp
return ans | 262 | 0 |
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