code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
__SCREAMING_SNAKE_CASE : Any = '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = _ask_options(
'''In which compute environment are you running?''' , ['''This machine''', '''AWS (Amazon SageMaker)'''] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
_lowerCamelCase = get_sagemaker_input()
else:
_lowerCamelCase = get_cluster_input()
return config
def lowerCAmelCase_( lowercase_ : Any=None ) -> List[Any]:
if subparsers is not None:
_lowerCamelCase = subparsers.add_parser('''config''' , description=lowercase_ )
else:
_lowerCamelCase = argparse.ArgumentParser('''Accelerate config command''' , description=lowercase_ )
parser.add_argument(
'''--config_file''' , default=lowercase_ , help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) , )
if subparsers is not None:
parser.set_defaults(func=lowercase_ )
return parser
def lowerCAmelCase_( lowercase_ : str ) -> List[Any]:
_lowerCamelCase = get_user_input()
if args.config_file is not None:
_lowerCamelCase = args.config_file
else:
if not os.path.isdir(lowercase_ ):
os.makedirs(lowercase_ )
_lowerCamelCase = default_yaml_config_file
if config_file.endswith('''.json''' ):
config.to_json_file(lowercase_ )
else:
config.to_yaml_file(lowercase_ )
print(F"""accelerate configuration saved at {config_file}""" )
def lowerCAmelCase_( ) -> Dict:
_lowerCamelCase = config_command_parser()
_lowerCamelCase = parser.parse_args()
config_command(lowercase_ )
if __name__ == "__main__":
main()
| 661 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Union[str, Any] = XLMRobertaTokenizer
lowercase__ : Optional[int] = XLMRobertaTokenizerFast
lowercase__ : List[str] = True
lowercase__ : Union[str, Any] = True
def snake_case__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__ ( self ):
_lowerCamelCase = '''<pad>'''
_lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCamelCase__ ) , 1_0_0_2 )
def snake_case__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def snake_case__ ( self ):
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
_lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCamelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_lowerCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
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 = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def snake_case__ ( self ):
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 = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
_lowerCamelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=True
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=False
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
@cached_property
def snake_case__ ( self ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def snake_case__ ( self ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
_lowerCamelCase = XLMRobertaTokenizer(f.name , keep_accents=lowerCamelCase__ )
_lowerCamelCase = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def snake_case__ ( self ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = '''I was born in 92000, and this is falsé.'''
_lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def snake_case__ ( self ):
_lowerCamelCase = '''Hello World!'''
_lowerCamelCase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
_lowerCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_lowerCamelCase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
# fmt: off
_lowerCamelCase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 661 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Dict = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class lowerCAmelCase_ ( lowerCamelCase_ ):
__a : Optional[int] = "visual_bert"
def __init__( self ,snake_case__=30522 ,snake_case__=768 ,snake_case__=512 ,snake_case__=12 ,snake_case__=12 ,snake_case__=3072 ,snake_case__="gelu" ,snake_case__=0.1 ,snake_case__=0.1 ,snake_case__=512 ,snake_case__=2 ,snake_case__=0.02 ,snake_case__=1E-12 ,snake_case__=False ,snake_case__=True ,snake_case__=1 ,snake_case__=0 ,snake_case__=2 ,**snake_case__ ,):
super().__init__(pad_token_id=snake_case__ ,bos_token_id=snake_case__ ,eos_token_id=snake_case__ ,**snake_case__ )
SCREAMING_SNAKE_CASE_ : Any = vocab_size
SCREAMING_SNAKE_CASE_ : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE_ : str = hidden_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = visual_embedding_dim
SCREAMING_SNAKE_CASE_ : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE_ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE_ : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE_ : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE_ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE_ : int = bypass_transformer
SCREAMING_SNAKE_CASE_ : Optional[Any] = special_visual_initialize
| 721 |
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 __UpperCAmelCase ( ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = ArgumentParser('Transformers CLI tool' , usage='transformers-cli <command> [<args>]' )
SCREAMING_SNAKE_CASE_ : int = 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
SCREAMING_SNAKE_CASE_ : Optional[int] = parser.parse_args()
if not hasattr(lowerCamelCase_ , 'func' ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE_ : Optional[Any] = args.func(lowerCamelCase_ )
service.run()
if __name__ == "__main__":
main()
| 685 | 0 |
from math import factorial
UpperCamelCase = {str(digit): factorial(digit) for digit in range(10)}
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError('Parameter number must be int' )
if number < 0:
raise ValueError('Parameter number must be greater than or equal to 0' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(SCREAMING_SNAKE_CASE ) )
def __magic_name__ ( SCREAMING_SNAKE_CASE = 60 , SCREAMING_SNAKE_CASE = 1_000_000 ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) or not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError('Parameters chain_length and number_limit must be int' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'Parameters chain_length and number_limit must be greater than 0' )
# the counter for the chains with the exact desired length
_lowercase : Tuple = 0
# the cached sizes of the previous chains
_lowercase : dict[int, int] = {}
for start_chain_element in range(1 , SCREAMING_SNAKE_CASE ):
# The temporary set will contain the elements of the chain
_lowercase : Dict = set()
_lowercase : Union[str, Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
_lowercase : Tuple = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(SCREAMING_SNAKE_CASE )
chain_set_length += 1
_lowercase : int = digit_factorial_sum(SCREAMING_SNAKE_CASE )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
_lowercase : str = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{solution()}''')
| 66 | from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] = {
"""google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: Dict = 'efficientnet'
def __init__( self , lowerCamelCase__ = 3 , lowerCamelCase__ = 600 , lowerCamelCase__ = 2.0 , lowerCamelCase__ = 3.1 , lowerCamelCase__ = 8 , lowerCamelCase__ = [3, 3, 5, 3, 5, 5, 3] , lowerCamelCase__ = [32, 16, 24, 40, 80, 112, 192] , lowerCamelCase__ = [16, 24, 40, 80, 112, 192, 320] , lowerCamelCase__ = [] , lowerCamelCase__ = [1, 2, 2, 2, 1, 2, 1] , lowerCamelCase__ = [1, 2, 2, 3, 3, 4, 1] , lowerCamelCase__ = [1, 6, 6, 6, 6, 6, 6] , lowerCamelCase__ = 0.2_5 , lowerCamelCase__ = "swish" , lowerCamelCase__ = 2_560 , lowerCamelCase__ = "mean" , lowerCamelCase__ = 0.0_2 , lowerCamelCase__ = 0.0_0_1 , lowerCamelCase__ = 0.9_9 , lowerCamelCase__ = 0.5 , lowerCamelCase__ = 0.2 , **lowerCamelCase__ , ):
super().__init__(**lowerCamelCase__ )
lowerCAmelCase_: str = num_channels
lowerCAmelCase_: str = image_size
lowerCAmelCase_: int = width_coefficient
lowerCAmelCase_: Union[str, Any] = depth_coefficient
lowerCAmelCase_: int = depth_divisor
lowerCAmelCase_: List[str] = kernel_sizes
lowerCAmelCase_: Tuple = in_channels
lowerCAmelCase_: List[str] = out_channels
lowerCAmelCase_: List[str] = depthwise_padding
lowerCAmelCase_: Optional[int] = strides
lowerCAmelCase_: List[str] = num_block_repeats
lowerCAmelCase_: Any = expand_ratios
lowerCAmelCase_: List[Any] = squeeze_expansion_ratio
lowerCAmelCase_: Optional[int] = hidden_act
lowerCAmelCase_: Optional[int] = hidden_dim
lowerCAmelCase_: Dict = pooling_type
lowerCAmelCase_: Optional[Any] = initializer_range
lowerCAmelCase_: int = batch_norm_eps
lowerCAmelCase_: List[str] = batch_norm_momentum
lowerCAmelCase_: List[Any] = dropout_rate
lowerCAmelCase_: Union[str, Any] = drop_connect_rate
lowerCAmelCase_: Tuple = sum(lowerCamelCase__ ) * 4
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: Optional[Any] = version.parse('1.11' )
@property
def _a ( self ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _a ( self ):
return 1E-5 | 613 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
__A = field(default='''image-classification''', metadata={'''include_in_asdict_even_if_is_default''': True} )
__A = Features({'''image''': Image()} )
__A = Features({'''labels''': ClassLabel} )
__A = "image"
__A = "labels"
def __UpperCAmelCase ( self : Optional[Any] , lowercase_ : int) -> List[str]:
"""simple docstring"""
if self.label_column not in features:
raise ValueError(f'Column {self.label_column} is not present in features.')
if not isinstance(features[self.label_column] , lowercase_):
raise ValueError(f'Column {self.label_column} is not a ClassLabel.')
_UpperCamelCase = copy.deepcopy(self)
_UpperCamelCase = self.label_schema.copy()
_UpperCamelCase = features[self.label_column]
_UpperCamelCase = label_schema
return task_template
@property
def __UpperCAmelCase ( self : Dict) -> Dict[str, str]:
"""simple docstring"""
return {
self.image_column: "image",
self.label_column: "labels",
}
| 82 | import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
class _UpperCAmelCase ( enum.Enum ):
'''simple docstring'''
__A = 0
__A = 1
@add_end_docstrings(lowerCAmelCase )
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
__A = '''generated'''
def __init__( self : Any , *lowercase_ : Dict , **lowercase_ : Tuple) -> List[Any]:
"""simple docstring"""
super().__init__(*lowercase_ , **lowercase_)
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)
def __UpperCAmelCase ( self : Optional[int] , lowercase_ : Union[str, Any]=None , lowercase_ : Optional[Any]=None , lowercase_ : Optional[int]=None , lowercase_ : Optional[Any]=None , lowercase_ : Any=None , lowercase_ : Union[str, Any]=None , **lowercase_ : Optional[Any] , ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = {}
if truncation is not None:
_UpperCamelCase = truncation
_UpperCamelCase = generate_kwargs
_UpperCamelCase = {}
if return_tensors is not None and return_type is None:
_UpperCamelCase = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
_UpperCamelCase = return_type
if clean_up_tokenization_spaces is not None:
_UpperCamelCase = clean_up_tokenization_spaces
if stop_sequence is not None:
_UpperCamelCase = self.tokenizer.encode(lowercase_ , add_special_tokens=lowercase_)
if len(lowercase_) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim.")
_UpperCamelCase = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __UpperCAmelCase ( self : int , lowercase_ : int , lowercase_ : int , lowercase_ : int) -> Any:
"""simple docstring"""
return True
def __UpperCAmelCase ( self : Dict , *lowercase_ : List[str] , lowercase_ : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCamelCase = self.model.config.prefix if self.model.config.prefix is not None else ""
if isinstance(args[0] , lowercase_):
if self.tokenizer.pad_token_id is None:
raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input")
_UpperCamelCase = ([prefix + arg for arg in args[0]],)
_UpperCamelCase = True
elif isinstance(args[0] , lowercase_):
_UpperCamelCase = (prefix + args[0],)
_UpperCamelCase = False
else:
raise ValueError(
f' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`')
_UpperCamelCase = self.tokenizer(*lowercase_ , padding=lowercase_ , truncation=lowercase_ , return_tensors=self.framework)
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self : List[Any] , *lowercase_ : Any , **lowercase_ : int) -> Dict:
"""simple docstring"""
_UpperCamelCase = super().__call__(*lowercase_ , **lowercase_)
if (
isinstance(args[0] , lowercase_)
and all(isinstance(lowercase_ , lowercase_) for el in args[0])
and all(len(lowercase_) == 1 for res in result)
):
return [res[0] for res in result]
return result
def __UpperCAmelCase ( self : Tuple , lowercase_ : Union[str, Any] , lowercase_ : str=TruncationStrategy.DO_NOT_TRUNCATE , **lowercase_ : Dict) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = self._parse_and_tokenize(lowercase_ , truncation=lowercase_ , **lowercase_)
return inputs
def __UpperCAmelCase ( self : str , lowercase_ : str , **lowercase_ : str) -> str:
"""simple docstring"""
if self.framework == "pt":
_UpperCamelCase , _UpperCamelCase = model_inputs["input_ids"].shape
elif self.framework == "tf":
_UpperCamelCase , _UpperCamelCase = tf.shape(model_inputs["input_ids"]).numpy()
_UpperCamelCase = generate_kwargs.get("min_length" , self.model.config.min_length)
_UpperCamelCase = generate_kwargs.get("max_length" , self.model.config.max_length)
self.check_inputs(lowercase_ , generate_kwargs["min_length"] , generate_kwargs["max_length"])
_UpperCamelCase = self.model.generate(**lowercase_ , **lowercase_)
_UpperCamelCase = output_ids.shape[0]
if self.framework == "pt":
_UpperCamelCase = output_ids.reshape(lowercase_ , out_b // in_b , *output_ids.shape[1:])
elif self.framework == "tf":
_UpperCamelCase = tf.reshape(lowercase_ , (in_b, out_b // in_b, *output_ids.shape[1:]))
return {"output_ids": output_ids}
def __UpperCAmelCase ( self : Dict , lowercase_ : str , lowercase_ : int=ReturnType.TEXT , lowercase_ : int=False) -> Tuple:
"""simple docstring"""
_UpperCamelCase = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
_UpperCamelCase = {f'{self.return_name}_token_ids': output_ids}
elif return_type == ReturnType.TEXT:
_UpperCamelCase = {
f'{self.return_name}_text': self.tokenizer.decode(
lowercase_ , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ , )
}
records.append(lowercase_)
return records
@add_end_docstrings(lowerCAmelCase )
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
__A = '''summary'''
def __call__( self : Optional[Any] , *lowercase_ : int , **lowercase_ : Dict) -> Optional[int]:
"""simple docstring"""
return super().__call__(*lowercase_ , **lowercase_)
def __UpperCAmelCase ( self : List[str] , lowercase_ : int , lowercase_ : int , lowercase_ : int) -> bool:
"""simple docstring"""
if max_length < min_length:
logger.warning(f'Your min_length={min_length} must be inferior than your max_length={max_length}.')
if input_length < max_length:
logger.warning(
f'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
"a summarization task, where outputs shorter than the input are typically wanted, you might "
f'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})')
@add_end_docstrings(lowerCAmelCase )
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
__A = '''translation'''
def __UpperCAmelCase ( self : Dict , lowercase_ : int , lowercase_ : int , lowercase_ : int) -> int:
"""simple docstring"""
if input_length > 0.9 * max_length:
logger.warning(
f'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
"increasing your max_length manually, e.g. translator('...', max_length=400)")
return True
def __UpperCAmelCase ( self : Tuple , *lowercase_ : Any , lowercase_ : List[Any]=TruncationStrategy.DO_NOT_TRUNCATE , lowercase_ : Any=None , lowercase_ : Optional[Any]=None) -> List[str]:
"""simple docstring"""
if getattr(self.tokenizer , "_build_translation_inputs" , lowercase_):
return self.tokenizer._build_translation_inputs(
*lowercase_ , return_tensors=self.framework , truncation=lowercase_ , src_lang=lowercase_ , tgt_lang=lowercase_)
else:
return super()._parse_and_tokenize(*lowercase_ , truncation=lowercase_)
def __UpperCAmelCase ( self : List[str] , lowercase_ : Dict=None , lowercase_ : str=None , **lowercase_ : List[Any]) -> List[Any]:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = super()._sanitize_parameters(**lowercase_)
if src_lang is not None:
_UpperCamelCase = src_lang
if tgt_lang is not None:
_UpperCamelCase = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
_UpperCamelCase = kwargs.get("task" , self.task)
_UpperCamelCase = task.split("_")
if task and len(lowercase_) == 4:
# translation, XX, to YY
_UpperCamelCase = items[1]
_UpperCamelCase = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self : List[str] , *lowercase_ : List[str] , **lowercase_ : str) -> Union[str, Any]:
"""simple docstring"""
return super().__call__(*lowercase_ , **lowercase_)
| 82 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 41 |
'''simple docstring'''
import os
from math import logaa
def _A ( A__ = "base_exp.txt" ):
"""simple docstring"""
__lowercase = 0
__lowercase = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(A__ ) , A__ ) ) ):
__lowercase , __lowercase = list(map(A__ , line.split(''',''' ) ) )
if x * logaa(A__ ) > largest:
__lowercase = x * logaa(A__ )
__lowercase = i + 1
return result
if __name__ == "__main__":
print(solution())
| 41 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
snake_case_ = 42
class __SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
@register_to_config
def __init__( self : int , snake_case : int = 32 , snake_case : int = 64 , snake_case : int = 20 , snake_case : int = 768 , snake_case : int=77 , snake_case : Tuple=4 , snake_case : float = 0.0 , snake_case : str = "silu" , snake_case : Optional[str] = None , snake_case : Optional[str] = None , snake_case : Optional[str] = "linear" , snake_case : Optional[str] = "prd" , snake_case : Optional[int] = None , snake_case : Optional[int] = None , snake_case : Optional[int] = None , ):
'''simple docstring'''
super().__init__()
A__ : Tuple = num_attention_heads
A__ : Dict = attention_head_dim
A__ : str = num_attention_heads * attention_head_dim
A__ : Optional[int] = additional_embeddings
A__ : Union[str, Any] = time_embed_dim or inner_dim
A__ : str = embedding_proj_dim or embedding_dim
A__ : Tuple = clip_embed_dim or embedding_dim
A__ : int = Timesteps(snake_case , snake_case , 0 )
A__ : str = TimestepEmbedding(snake_case , snake_case , out_dim=snake_case , act_fn=snake_case )
A__ : List[str] = nn.Linear(snake_case , snake_case )
if embedding_proj_norm_type is None:
A__ : Optional[Any] = None
elif embedding_proj_norm_type == "layer":
A__ : Tuple = nn.LayerNorm(snake_case )
else:
raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}' )
A__ : List[Any] = nn.Linear(snake_case , snake_case )
if encoder_hid_proj_type is None:
A__ : int = None
elif encoder_hid_proj_type == "linear":
A__ : str = nn.Linear(snake_case , snake_case )
else:
raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}' )
A__ : Tuple = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , snake_case ) )
if added_emb_type == "prd":
A__ : List[str] = nn.Parameter(torch.zeros(1 , 1 , snake_case ) )
elif added_emb_type is None:
A__ : int = None
else:
raise ValueError(
F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.' )
A__ : Union[str, Any] = nn.ModuleList(
[
BasicTransformerBlock(
snake_case , snake_case , snake_case , dropout=snake_case , activation_fn="""gelu""" , attention_bias=snake_case , )
for d in range(snake_case )
] )
if norm_in_type == "layer":
A__ : Optional[Any] = nn.LayerNorm(snake_case )
elif norm_in_type is None:
A__ : Union[str, Any] = None
else:
raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.' )
A__ : Optional[Any] = nn.LayerNorm(snake_case )
A__ : str = nn.Linear(snake_case , snake_case )
A__ : str = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -10000.0 )
causal_attention_mask.triu_(1 )
A__ : Union[str, Any] = causal_attention_mask[None, ...]
self.register_buffer("""causal_attention_mask""" , snake_case , persistent=snake_case )
A__ : Any = nn.Parameter(torch.zeros(1 , snake_case ) )
A__ : Optional[int] = nn.Parameter(torch.zeros(1 , snake_case ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def _UpperCamelCase ( self : Optional[int] ):
'''simple docstring'''
A__ : str = {}
def fn_recursive_add_processors(snake_case : str , snake_case : torch.nn.Module , snake_case : Dict[str, AttentionProcessor] ):
if hasattr(snake_case , """set_processor""" ):
A__ : Union[str, Any] = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F'{name}.{sub_name}' , snake_case , snake_case )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(snake_case , snake_case , snake_case )
return processors
def _UpperCamelCase ( self : List[str] , snake_case : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):
'''simple docstring'''
A__ : str = len(self.attn_processors.keys() )
if isinstance(snake_case , snake_case ) and len(snake_case ) != count:
raise ValueError(
F'A dict of processors was passed, but the number of processors {len(snake_case )} does not match the'
F' number of attention layers: {count}. Please make sure to pass {count} processor classes.' )
def fn_recursive_attn_processor(snake_case : str , snake_case : torch.nn.Module , snake_case : List[str] ):
if hasattr(snake_case , """set_processor""" ):
if not isinstance(snake_case , snake_case ):
module.set_processor(snake_case )
else:
module.set_processor(processor.pop(F'{name}.processor' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F'{name}.{sub_name}' , snake_case , snake_case )
for name, module in self.named_children():
fn_recursive_attn_processor(snake_case , snake_case , snake_case )
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def _UpperCamelCase ( self : Optional[Any] , snake_case : Optional[Any] , snake_case : Union[torch.Tensor, float, int] , snake_case : torch.FloatTensor , snake_case : Optional[torch.FloatTensor] = None , snake_case : Optional[torch.BoolTensor] = None , snake_case : bool = True , ):
'''simple docstring'''
A__ : List[Any] = hidden_states.shape[0]
A__ : List[Any] = timestep
if not torch.is_tensor(snake_case ):
A__ : Dict = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(snake_case ) and len(timesteps.shape ) == 0:
A__ : List[str] = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
A__ : Any = timesteps * torch.ones(snake_case , dtype=timesteps.dtype , device=timesteps.device )
A__ : Dict = self.time_proj(snake_case )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
A__ : Any = timesteps_projected.to(dtype=self.dtype )
A__ : Union[str, Any] = self.time_embedding(snake_case )
if self.embedding_proj_norm is not None:
A__ : Any = self.embedding_proj_norm(snake_case )
A__ : Dict = self.embedding_proj(snake_case )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
A__ : List[Any] = self.encoder_hidden_states_proj(snake_case )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set""" )
A__ : Optional[Any] = self.proj_in(snake_case )
A__ : Union[str, Any] = self.positional_embedding.to(hidden_states.dtype )
A__ : Tuple = []
A__ : Tuple = 0
if encoder_hidden_states is not None:
additional_embeds.append(snake_case )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
A__ : Optional[Any] = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
A__ : List[str] = hidden_states[:, None, :]
A__ : Optional[Any] = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
A__ : Dict = self.prd_embedding.to(hidden_states.dtype ).expand(snake_case , -1 , -1 )
additional_embeds.append(snake_case )
A__ : Dict = torch.cat(
snake_case , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
A__ : List[str] = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
A__ : Any = F.pad(
snake_case , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
A__ : List[str] = hidden_states + positional_embeddings
if attention_mask is not None:
A__ : List[str] = (1 - attention_mask.to(hidden_states.dtype )) * -10000.0
A__ : Optional[Any] = F.pad(snake_case , (0, self.additional_embeddings) , value=0.0 )
A__ : Union[str, Any] = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
A__ : Optional[int] = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
A__ : Optional[Any] = self.norm_in(snake_case )
for block in self.transformer_blocks:
A__ : Any = block(snake_case , attention_mask=snake_case )
A__ : List[Any] = self.norm_out(snake_case )
if self.prd_embedding is not None:
A__ : Union[str, Any] = hidden_states[:, -1]
else:
A__ : Tuple = hidden_states[:, additional_embeddings_len:]
A__ : List[str] = self.proj_to_clip_embeddings(snake_case )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=snake_case )
def _UpperCamelCase ( self : Optional[int] , snake_case : List[str] ):
'''simple docstring'''
A__ : List[str] = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 498 |
"""simple docstring"""
from __future__ import annotations
A_ = []
def _lowerCAmelCase ( UpperCAmelCase__ : list[list[int]], UpperCAmelCase__ : int, UpperCAmelCase__ : int ) ->bool:
for i in range(len(UpperCAmelCase__ ) ):
if board[row][i] == 1:
return False
for i in range(len(UpperCAmelCase__ ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(UpperCAmelCase__, -1, -1 ), range(UpperCAmelCase__, -1, -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(UpperCAmelCase__, -1, -1 ), range(UpperCAmelCase__, len(UpperCAmelCase__ ) ) ):
if board[i][j] == 1:
return False
return True
def _lowerCAmelCase ( UpperCAmelCase__ : list[list[int]], UpperCAmelCase__ : int ) ->bool:
if row >= len(UpperCAmelCase__ ):
solution.append(UpperCAmelCase__ )
printboard(UpperCAmelCase__ )
print()
return True
for i in range(len(UpperCAmelCase__ ) ):
if is_safe(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ):
A__ : Tuple = 1
solve(UpperCAmelCase__, row + 1 )
A__ : Optional[int] = 0
return False
def _lowerCAmelCase ( UpperCAmelCase__ : list[list[int]] ) ->None:
for i in range(len(UpperCAmelCase__ ) ):
for j in range(len(UpperCAmelCase__ ) ):
if board[i][j] == 1:
print("""Q""", end=""" """ )
else:
print(""".""", end=""" """ )
print()
# n=int(input("The no. of queens"))
A_ = 8
A_ = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 498 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
lowerCAmelCase = 'bart'
lowerCAmelCase = True
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE )
def _a ( ):
"""simple docstring"""
if LOAD_DENSE_INDEX:
lowercase__ = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
lowercase__ = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
lowercase__ = qar_model.eval()
else:
lowercase__ , lowercase__ = (None, None)
if MODEL_TYPE == "bart":
lowercase__ = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
lowercase__ = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
lowercase__ = sas_model.eval()
else:
lowercase__ , lowercase__ = make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE )
def _a ( ):
"""simple docstring"""
if LOAD_DENSE_INDEX:
lowercase__ = faiss.StandardGpuResources()
lowercase__ = datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
lowercase__ = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 1_28) , )
lowercase__ = faiss.IndexFlatIP(1_28 )
lowercase__ = faiss.index_cpu_to_gpu(SCREAMING_SNAKE_CASE , 1 , SCREAMING_SNAKE_CASE )
wikiaab_gpu_index_flat.add(SCREAMING_SNAKE_CASE ) # TODO fix for larger GPU
else:
lowercase__ , lowercase__ = (None, None)
lowercase__ = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE )
def _a ( ):
"""simple docstring"""
lowercase__ = datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
lowercase__ = elia['''train_eli5''']
lowercase__ = np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 1_28) )
lowercase__ = faiss.IndexFlatIP(1_28 )
eli5_train_q_index.add(SCREAMING_SNAKE_CASE )
return (elia_train, eli5_train_q_index)
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = load_indexes()
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = load_models()
lowerCAmelCase, lowerCAmelCase = load_train_data()
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=10 ):
"""simple docstring"""
lowercase__ = embed_questions_for_retrieval([question] , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ , lowercase__ = eli5_train_q_index.search(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ = [elia_train[int(SCREAMING_SNAKE_CASE )] for i in I[0]]
return nn_examples
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="wiki40b" , SCREAMING_SNAKE_CASE="dense" , SCREAMING_SNAKE_CASE=10 ):
"""simple docstring"""
if source == "none":
lowercase__ , lowercase__ = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
lowercase__ , lowercase__ = query_qa_dense_index(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
lowercase__ , lowercase__ = query_es_index(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , index_name='''english_wiki40b_snippets_100w''' , n_results=SCREAMING_SNAKE_CASE , )
lowercase__ = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
lowercase__ = '''question: {} context: {}'''.format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda SCREAMING_SNAKE_CASE : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda SCREAMING_SNAKE_CASE : None),
} )
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=64 , SCREAMING_SNAKE_CASE=2_56 , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=0.95 , SCREAMING_SNAKE_CASE=0.8 ):
"""simple docstring"""
with torch.no_grad():
lowercase__ = qa_sas_generate(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , num_answers=1 , num_beams=SCREAMING_SNAKE_CASE , min_len=SCREAMING_SNAKE_CASE , max_len=SCREAMING_SNAKE_CASE , do_sample=SCREAMING_SNAKE_CASE , temp=SCREAMING_SNAKE_CASE , top_p=SCREAMING_SNAKE_CASE , top_k=SCREAMING_SNAKE_CASE , max_input_length=10_24 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title('Long Form Question Answering with ELI5')
# Start sidebar
lowerCAmelCase = '<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
lowerCAmelCase = '\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class="img-container"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
lowerCAmelCase = '\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n'
st.sidebar.markdown(description, unsafe_allow_html=True)
lowerCAmelCase = [
'Answer the question',
'View the retrieved document only',
'View the most similar ELI5 question and answer',
'Show me everything, please!',
]
lowerCAmelCase = st.sidebar.checkbox('Demo options')
if demo_options:
lowerCAmelCase = st.sidebar.selectbox(
'',
action_list,
index=3,
)
lowerCAmelCase = action_list.index(action_st)
lowerCAmelCase = st.sidebar.selectbox(
'',
['Show full text of passages', 'Show passage section titles'],
index=0,
)
lowerCAmelCase = show_type == 'Show full text of passages'
else:
lowerCAmelCase = 3
lowerCAmelCase = True
lowerCAmelCase = st.sidebar.checkbox('Retrieval options')
if retrieval_options:
lowerCAmelCase = '\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n '
st.sidebar.markdown(retriever_info)
lowerCAmelCase = st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
lowerCAmelCase = st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
lowerCAmelCase = 'wiki40b'
lowerCAmelCase = 'dense'
lowerCAmelCase = 'beam'
lowerCAmelCase = 2
lowerCAmelCase = 64
lowerCAmelCase = 256
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = st.sidebar.checkbox('Generation options')
if generate_options:
lowerCAmelCase = '\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder\'s output probabilities.\n '
st.sidebar.markdown(generate_info)
lowerCAmelCase = st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
lowerCAmelCase = st.sidebar.slider(
'Minimum generation length', min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
lowerCAmelCase = st.sidebar.slider(
'Maximum generation length', min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
lowerCAmelCase = st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
lowerCAmelCase = st.sidebar.slider(
'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
)
lowerCAmelCase = st.sidebar.slider(
'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
)
lowerCAmelCase = None
# start main text
lowerCAmelCase = [
'<MY QUESTION>',
'How do people make chocolate?',
'Why do we get a fever when we are sick?',
'How can different animals perceive different colors?',
'What is natural language processing?',
'What\'s the best way to treat a sunburn?',
'What exactly are vitamins ?',
'How does nuclear energy provide electricity?',
'What\'s the difference between viruses and bacteria?',
'Why are flutes classified as woodwinds when most of them are made out of metal ?',
'Why do people like drinking coffee even though it tastes so bad?',
'What happens when wine ages? How does it make the wine taste better?',
'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?',
'How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?',
'How does New Zealand have so many large bird predators?',
]
lowerCAmelCase = st.selectbox(
'What would you like to ask? ---- select <MY QUESTION> to enter a new query',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
lowerCAmelCase = st.text_input('Enter your question here:', '')
else:
lowerCAmelCase = question_s
if st.button('Show me!'):
if action in [0, 1, 3]:
if index_type == "mixed":
lowerCAmelCase, lowerCAmelCase = make_support(question, source=wiki_source, method='dense', n_results=10)
lowerCAmelCase, lowerCAmelCase = make_support(question, source=wiki_source, method='sparse', n_results=10)
lowerCAmelCase = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
lowerCAmelCase = support_list[:10]
lowerCAmelCase = '<P> ' + ' <P> '.join([res[-1] for res in support_list])
else:
lowerCAmelCase, lowerCAmelCase = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
lowerCAmelCase, lowerCAmelCase = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == 'sampled'),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('### The model generated answer is:')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('--- \n ### The model is drawing information from the following Wikipedia passages:')
for i, res in enumerate(support_list):
lowerCAmelCase = 'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
lowerCAmelCase = res[1].strip()
if sec_titles == "":
lowerCAmelCase = '[{}]({})'.format(res[0], wiki_url)
else:
lowerCAmelCase = sec_titles.split(' & ')
lowerCAmelCase = ' & '.join(
['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list]
)
st.markdown(
'{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'> <span style="font-family:arial; font-size:10pt;">' + res[-1] + '</span>', unsafe_allow_html=True
)
if action in [2, 3]:
lowerCAmelCase = find_nearest_training(question)
lowerCAmelCase = nn_train_list[0]
st.markdown(
'--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
)
lowerCAmelCase = [
'{}. {}'.format(i + 1, ' \n'.join([line.strip() for line in ans.split('\n') if line.strip() != '']))
for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score']))
if i == 0 or sc > 2
]
st.markdown('##### Its answers were: \n\n {}'.format('\n'.join(answers_st)))
lowerCAmelCase = '\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 43 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["PLBartTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"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
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 391 | 0 |
from __future__ import annotations
def _lowerCAmelCase ( _a : list[int] , _a : int ) -> list[list[int]]:
lowerCAmelCase_ : list[list[int]] = []
lowerCAmelCase_ : list[int] = []
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : Optional[int] = sum(_a )
create_state_space_tree(_a , _a , _a , _a , _a , _a )
return result
def _lowerCAmelCase ( _a : list[int] , _a : int , _a : int , _a : list[int] , _a : list[list[int]] , _a : int , ) -> None:
if sum(_a ) > max_sum or (remaining_nums_sum + sum(_a )) < max_sum:
return
if sum(_a ) == max_sum:
result.append(_a )
return
for index in range(_a , len(_a ) ):
create_state_space_tree(
_a , _a , index + 1 , [*path, nums[index]] , _a , remaining_nums_sum - nums[index] , )
UpperCAmelCase_ : int = [3, 34, 4, 12, 5, 2]
UpperCAmelCase_ : Optional[Any] = 9
UpperCAmelCase_ : Optional[int] = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| 709 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
UpperCAmelCase_ : str = logging.get_logger(__name__)
@dataclass
class lowercase__ :
__UpperCamelCase = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys() )} )
__UpperCamelCase = field(
metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""} )
__UpperCamelCase = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__UpperCamelCase = field(
default=__A , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : List[str] = self.task_name.lower()
class lowercase__ ( __A ):
__UpperCamelCase = """train"""
__UpperCamelCase = """dev"""
__UpperCamelCase = """test"""
class lowercase__ ( __A ):
__UpperCamelCase = 42
__UpperCamelCase = 42
__UpperCamelCase = 42
def __init__( self , _lowercase , _lowercase , _lowercase = None , _lowercase = Split.train , _lowercase = None , ):
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _lowercase , )
lowerCAmelCase_ : Any = args
lowerCAmelCase_ : List[str] = glue_processors[args.task_name]()
lowerCAmelCase_ : Tuple = glue_output_modes[args.task_name]
if isinstance(_lowercase , _lowercase ):
try:
lowerCAmelCase_ : Dict = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
lowerCAmelCase_ : Optional[int] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
lowerCAmelCase_ : List[str] = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = label_list[2], label_list[1]
lowerCAmelCase_ : int = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCAmelCase_ : Optional[int] = cached_features_file + """.lock"""
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
lowerCAmelCase_ : Dict = time.time()
lowerCAmelCase_ : str = torch.load(_lowercase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(F'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
lowerCAmelCase_ : List[Any] = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
lowerCAmelCase_ : Dict = self.processor.get_test_examples(args.data_dir )
else:
lowerCAmelCase_ : List[str] = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
lowerCAmelCase_ : Optional[int] = examples[:limit_length]
lowerCAmelCase_ : Any = glue_convert_examples_to_features(
_lowercase , _lowercase , max_length=args.max_seq_length , label_list=_lowercase , output_mode=self.output_mode , )
lowerCAmelCase_ : str = time.time()
torch.save(self.features , _lowercase )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
return len(self.features )
def __getitem__( self , _lowercase ):
return self.features[i]
def UpperCAmelCase__ ( self ):
return self.label_list
| 440 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class a :
snake_case_ = 42
snake_case_ = None
# Automatically constructed
snake_case_ = "dict"
snake_case_ = None
snake_case_ = field(default="Translation" , init=_lowerCamelCase , repr=_lowerCamelCase )
def __call__( self : Union[str, Any] ):
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def A_ ( self : int ):
from .features import Value
return {k: Value('''string''' ) for k in sorted(self.languages )}
@dataclass
class a :
snake_case_ = None
snake_case_ = None
snake_case_ = None
# Automatically constructed
snake_case_ = "dict"
snake_case_ = None
snake_case_ = field(default="TranslationVariableLanguages" , init=_lowerCamelCase , repr=_lowerCamelCase )
def A_ ( self : Optional[Any] ):
snake_case_ = sorted(set(self.languages ) ) if self.languages else None
snake_case_ = len(self.languages ) if self.languages else None
def __call__( self : str ):
return pa.struct({'''language''': pa.list_(pa.string() ), '''translation''': pa.list_(pa.string() )} )
def A_ ( self : List[Any] , lowercase_ : List[str] ):
snake_case_ = set(self.languages )
if self.languages and set(lowercase_ ) - lang_set:
raise ValueError(
F"Some languages in example ({', '.join(sorted(set(lowercase_ ) - lang_set ) )}) are not in valid set ({', '.join(lowercase_ )})." )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
snake_case_ = []
for lang, text in translation_dict.items():
if isinstance(lowercase_ , lowercase_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
snake_case_ ,snake_case_ = zip(*sorted(lowercase_ ) )
return {"language": languages, "translation": translations}
def A_ ( self : Union[str, Any] ):
from .features import Sequence, Value
return {
"language": Sequence(Value('''string''' ) ),
"translation": Sequence(Value('''string''' ) ),
}
| 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append(__UpperCAmelCase )
snake_case_ = True
while queue:
snake_case_ = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(__UpperCAmelCase )
snake_case_ = True
snake_case_ = u
return visited[t]
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
snake_case_ = [-1] * (len(__UpperCAmelCase ))
snake_case_ = 0
while bfs(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ):
snake_case_ = float('''Inf''' )
snake_case_ = sink
while s != source:
# Find the minimum value in select path
snake_case_ = min(__UpperCAmelCase, graph[parent[s]][s] )
snake_case_ = parent[s]
max_flow += path_flow
snake_case_ = sink
while v != source:
snake_case_ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
snake_case_ = parent[v]
return max_flow
a : List[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
a ,a : int = 0, 5
print(ford_fulkerson(graph, source, sink))
| 640 | 1 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
_A : int = logging.get_logger(__name__)
# General docstring
_A : List[Any] = """RegNetConfig"""
# Base docstring
_A : List[Any] = """facebook/regnet-y-040"""
_A : int = [1, 10_88, 7, 7]
# Image classification docstring
_A : Optional[int] = """facebook/regnet-y-040"""
_A : Tuple = """tabby, tabby cat"""
_A : Dict = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class a__ ( nn.Module ):
def __init__( self , _a , _a , _a = 3 , _a = 1 , _a = 1 , _a = "relu" , ):
super().__init__()
lowercase : Optional[int] = nn.Convad(
_a , _a , kernel_size=_a , stride=_a , padding=kernel_size // 2 , groups=_a , bias=_a , )
lowercase : Any = nn.BatchNormad(_a )
lowercase : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def __magic_name__ ( self , _a ):
lowercase : List[Any] = self.convolution(_a )
lowercase : List[str] = self.normalization(_a )
lowercase : Dict = self.activation(_a )
return hidden_state
class a__ ( nn.Module ):
def __init__( self , _a ):
super().__init__()
lowercase : Optional[Any] = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
lowercase : Tuple = config.num_channels
def __magic_name__ ( self , _a ):
lowercase : List[Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
lowercase : Tuple = self.embedder(_a )
return hidden_state
class a__ ( nn.Module ):
def __init__( self , _a , _a , _a = 2 ):
super().__init__()
lowercase : List[str] = nn.Convad(_a , _a , kernel_size=1 , stride=_a , bias=_a )
lowercase : List[str] = nn.BatchNormad(_a )
def __magic_name__ ( self , _a ):
lowercase : Optional[Any] = self.convolution(_a )
lowercase : Optional[Any] = self.normalization(_a )
return hidden_state
class a__ ( nn.Module ):
def __init__( self , _a , _a ):
super().__init__()
lowercase : Optional[int] = nn.AdaptiveAvgPoolad((1, 1) )
lowercase : str = nn.Sequential(
nn.Convad(_a , _a , kernel_size=1 ) , nn.ReLU() , nn.Convad(_a , _a , kernel_size=1 ) , nn.Sigmoid() , )
def __magic_name__ ( self , _a ):
# b c h w -> b c 1 1
lowercase : Union[str, Any] = self.pooler(_a )
lowercase : Dict = self.attention(_a )
lowercase : str = hidden_state * attention
return hidden_state
class a__ ( nn.Module ):
def __init__( self , _a , _a , _a , _a = 1 ):
super().__init__()
lowercase : Tuple = in_channels != out_channels or stride != 1
lowercase : Tuple = max(1 , out_channels // config.groups_width )
lowercase : int = (
RegNetShortCut(_a , _a , stride=_a ) if should_apply_shortcut else nn.Identity()
)
lowercase : List[str] = nn.Sequential(
RegNetConvLayer(_a , _a , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(_a , _a , stride=_a , groups=_a , activation=config.hidden_act ) , RegNetConvLayer(_a , _a , kernel_size=1 , activation=_a ) , )
lowercase : Union[str, Any] = ACTaFN[config.hidden_act]
def __magic_name__ ( self , _a ):
lowercase : str = hidden_state
lowercase : str = self.layer(_a )
lowercase : Optional[int] = self.shortcut(_a )
hidden_state += residual
lowercase : Optional[int] = self.activation(_a )
return hidden_state
class a__ ( nn.Module ):
def __init__( self , _a , _a , _a , _a = 1 ):
super().__init__()
lowercase : Optional[Any] = in_channels != out_channels or stride != 1
lowercase : Optional[int] = max(1 , out_channels // config.groups_width )
lowercase : Union[str, Any] = (
RegNetShortCut(_a , _a , stride=_a ) if should_apply_shortcut else nn.Identity()
)
lowercase : Dict = nn.Sequential(
RegNetConvLayer(_a , _a , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(_a , _a , stride=_a , groups=_a , activation=config.hidden_act ) , RegNetSELayer(_a , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(_a , _a , kernel_size=1 , activation=_a ) , )
lowercase : Optional[int] = ACTaFN[config.hidden_act]
def __magic_name__ ( self , _a ):
lowercase : Optional[Any] = hidden_state
lowercase : Union[str, Any] = self.layer(_a )
lowercase : Any = self.shortcut(_a )
hidden_state += residual
lowercase : str = self.activation(_a )
return hidden_state
class a__ ( nn.Module ):
def __init__( self , _a , _a , _a , _a = 2 , _a = 2 , ):
super().__init__()
lowercase : Optional[Any] = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
lowercase : Tuple = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_a , _a , _a , stride=_a , ) , *[layer(_a , _a , _a ) for _ in range(depth - 1 )] , )
def __magic_name__ ( self , _a ):
lowercase : str = self.layers(_a )
return hidden_state
class a__ ( nn.Module ):
def __init__( self , _a ):
super().__init__()
lowercase : List[str] = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_a , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowercase : List[str] = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_a , config.depths[1:] ):
self.stages.append(RegNetStage(_a , _a , _a , depth=_a ) )
def __magic_name__ ( self , _a , _a = False , _a = True ):
lowercase : Union[str, Any] = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase : List[str] = hidden_states + (hidden_state,)
lowercase : Tuple = stage_module(_a )
if output_hidden_states:
lowercase : Tuple = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_a , hidden_states=_a )
class a__ ( a_ ):
__lowerCAmelCase = RegNetConfig
__lowerCAmelCase = """regnet"""
__lowerCAmelCase = """pixel_values"""
__lowerCAmelCase = True
def __magic_name__ ( self , _a ):
if isinstance(_a , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(_a , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def __magic_name__ ( self , _a , _a=False ):
if isinstance(_a , _a ):
lowercase : Dict = value
_A : List[Any] = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
_A : Dict = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""", a_, )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class a__ ( a_ ):
def __init__( self , _a ):
super().__init__(_a )
lowercase : Optional[int] = config
lowercase : Tuple = RegNetEmbeddings(_a )
lowercase : Optional[Any] = RegNetEncoder(_a )
lowercase : Tuple = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_a )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_a , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __magic_name__ ( self , _a , _a = None , _a = None ):
lowercase : Union[str, Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase : Tuple = return_dict if return_dict is not None else self.config.use_return_dict
lowercase : Optional[int] = self.embedder(_a )
lowercase : List[Any] = self.encoder(
_a , output_hidden_states=_a , return_dict=_a )
lowercase : Tuple = encoder_outputs[0]
lowercase : int = self.pooler(_a )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_a , pooler_output=_a , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""", a_, )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class a__ ( a_ ):
def __init__( self , _a ):
super().__init__(_a )
lowercase : int = config.num_labels
lowercase : Tuple = RegNetModel(_a )
# classification head
lowercase : Union[str, Any] = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_a )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_a , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __magic_name__ ( self , _a = None , _a = None , _a = None , _a = None , ):
lowercase : Dict = return_dict if return_dict is not None else self.config.use_return_dict
lowercase : Any = self.regnet(_a , output_hidden_states=_a , return_dict=_a )
lowercase : Any = outputs.pooler_output if return_dict else outputs[1]
lowercase : Optional[Any] = self.classifier(_a )
lowercase : Union[str, Any] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase : Tuple = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase : Dict = "single_label_classification"
else:
lowercase : Optional[Any] = "multi_label_classification"
if self.config.problem_type == "regression":
lowercase : List[Any] = MSELoss()
if self.num_labels == 1:
lowercase : List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase : Tuple = loss_fct(_a , _a )
elif self.config.problem_type == "single_label_classification":
lowercase : Any = CrossEntropyLoss()
lowercase : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase : str = BCEWithLogitsLoss()
lowercase : Tuple = loss_fct(_a , _a )
if not return_dict:
lowercase : List[Any] = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_a , logits=_a , hidden_states=outputs.hidden_states )
| 518 |
"""simple docstring"""
def __magic_name__ ( __snake_case : str ) -> list:
lowercase : Optional[Any] = [0] * len(__snake_case )
for i in range(1 , len(__snake_case ) ):
# use last results for better performance - dynamic programming
lowercase : int = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowercase : Any = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowercase : List[str] = j
return prefix_result
def __magic_name__ ( __snake_case : str ) -> int:
return max(prefix_function(__snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 518 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ : List[Any] = logging.get_logger(__name__)
lowercase_ : Any = {
'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
'xlm-roberta-large-finetuned-conll02-dutch': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll02-spanish': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-english': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-german': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
),
}
class _lowerCamelCase ( UpperCamelCase_ ):
__a = "xlm-roberta"
def __init__( self , lowerCAmelCase=30522 , lowerCAmelCase=768 , lowerCAmelCase=12 , lowerCAmelCase=12 , lowerCAmelCase=3072 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=512 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-12 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , ) -> Union[str, Any]:
super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase )
SCREAMING_SNAKE_CASE__: Union[str, Any]= vocab_size
SCREAMING_SNAKE_CASE__: Tuple= hidden_size
SCREAMING_SNAKE_CASE__: List[Any]= num_hidden_layers
SCREAMING_SNAKE_CASE__: Optional[Any]= num_attention_heads
SCREAMING_SNAKE_CASE__: Any= hidden_act
SCREAMING_SNAKE_CASE__: List[Any]= intermediate_size
SCREAMING_SNAKE_CASE__: str= hidden_dropout_prob
SCREAMING_SNAKE_CASE__: Optional[int]= attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__: List[str]= max_position_embeddings
SCREAMING_SNAKE_CASE__: Optional[Any]= type_vocab_size
SCREAMING_SNAKE_CASE__: List[Any]= initializer_range
SCREAMING_SNAKE_CASE__: Tuple= layer_norm_eps
SCREAMING_SNAKE_CASE__: Dict= position_embedding_type
SCREAMING_SNAKE_CASE__: Optional[Any]= use_cache
SCREAMING_SNAKE_CASE__: int= classifier_dropout
class _lowerCamelCase ( UpperCamelCase_ ):
@property
def UpperCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__: Any= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
SCREAMING_SNAKE_CASE__: Any= {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 64 | """simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__lowercase : str = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
class _A ( _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Any = BartphoTokenizer
UpperCamelCase_ : Dict = False
UpperCamelCase_ : Optional[Any] = True
def lowercase ( self : Tuple ) -> Dict:
super().setUp()
__snake_case = ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
__snake_case = dict(zip(A_ , range(len(A_ ) ) ) )
__snake_case = {'''unk_token''': '''<unk>'''}
__snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''monolingual_vocab_file'''] )
with open(self.monolingual_vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n" )
__snake_case = BartphoTokenizer(A_ , self.monolingual_vocab_file , **self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase ( self : List[Any] , **A_ : Optional[int] ) -> Any:
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname , **A_ )
def lowercase ( self : Optional[Any] , A_ : List[Any] ) -> Tuple:
__snake_case = '''This is a là test'''
__snake_case = '''This is a<unk><unk> test'''
return input_text, output_text
def lowercase ( self : Optional[int] ) -> Dict:
__snake_case = BartphoTokenizer(A_ , self.monolingual_vocab_file , **self.special_tokens_map )
__snake_case = '''This is a là test'''
__snake_case = '''▁This ▁is ▁a ▁l à ▁t est'''.split()
__snake_case = tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
__snake_case = tokens + [tokenizer.unk_token]
__snake_case = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ) , A_ ) | 564 | 0 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase__ = importlib.util.spec_from_file_location(
'''transformers''',
os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowerCAmelCase__ = spec.loader.load_module()
lowerCAmelCase__ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCAmelCase__ = re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
lowerCAmelCase__ = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : int = []
for config_class in list(CONFIG_MAPPING.values() ):
lowerCAmelCase : Any = False
# source code of `config_class`
lowerCAmelCase : Dict = inspect.getsource(SCREAMING_SNAKE_CASE )
lowerCAmelCase : List[str] = _re_checkpoint.findall(SCREAMING_SNAKE_CASE )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
lowerCAmelCase : int = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
lowerCAmelCase : Optional[int] = f"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
lowerCAmelCase : int = True
break
lowerCAmelCase : Any = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase : Any = "\n".join(sorted(SCREAMING_SNAKE_CASE ) )
raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 718 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''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__ ( lowercase ):
"""simple docstring"""
a : Union[str, Any] ="vit"
def __init__( self , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3_072 , snake_case__="gelu" , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=224 , snake_case__=16 , snake_case__=3 , snake_case__=True , snake_case__=16 , **snake_case__ , ):
"""simple docstring"""
super().__init__(**snake_case__ )
lowerCAmelCase : Optional[Any] = hidden_size
lowerCAmelCase : List[Any] = num_hidden_layers
lowerCAmelCase : List[Any] = num_attention_heads
lowerCAmelCase : Union[str, Any] = intermediate_size
lowerCAmelCase : Any = hidden_act
lowerCAmelCase : Optional[Any] = hidden_dropout_prob
lowerCAmelCase : Optional[Any] = attention_probs_dropout_prob
lowerCAmelCase : List[str] = initializer_range
lowerCAmelCase : Optional[Any] = layer_norm_eps
lowerCAmelCase : Optional[int] = image_size
lowerCAmelCase : Optional[Any] = patch_size
lowerCAmelCase : Tuple = num_channels
lowerCAmelCase : Optional[int] = qkv_bias
lowerCAmelCase : str = encoder_stride
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : List[Any] =version.parse("1.11" )
@property
def lowercase__ ( self ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowercase__ ( self ):
"""simple docstring"""
return 1e-4
| 681 | 0 |
from __future__ import annotations
import math
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :List[Any] = u
for i in range(1 , a ):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = temp * (u - i)
return temp
def lowercase ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = int(input("enter the numbers of values: " ) )
SCREAMING_SNAKE_CASE_ :list[list[float]] = []
for _ in range(a ):
y.append([] )
for i in range(a ):
for j in range(a ):
y[i].append(a )
SCREAMING_SNAKE_CASE_ :Any = 0
print("enter the values of parameters in a list: " )
SCREAMING_SNAKE_CASE_ :Dict = list(map(a , input().split() ) )
print("enter the values of corresponding parameters: " )
for i in range(a ):
SCREAMING_SNAKE_CASE_ :List[Any] = float(input() )
SCREAMING_SNAKE_CASE_ :Optional[Any] = int(input("enter the value to interpolate: " ) )
SCREAMING_SNAKE_CASE_ :str = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , a ):
for j in range(n - i ):
SCREAMING_SNAKE_CASE_ :List[str] = y[j + 1][i - 1] - y[j][i - 1]
SCREAMING_SNAKE_CASE_ :Tuple = y[0][0]
for i in range(1 , a ):
summ += (ucal(a , a ) * y[0][i]) / math.factorial(a )
print(F"the value at {value} is {summ}" )
if __name__ == "__main__":
main()
| 631 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class _UpperCAmelCase ( lowercase ):
def __init__( self : Optional[int] , UpperCAmelCase : Any=-1):
# in NER datasets, the last column is usually reserved for NER label
SCREAMING_SNAKE_CASE_ :Tuple = label_idx
def _snake_case ( self : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Union[Split, str]):
if isinstance(UpperCAmelCase , UpperCAmelCase):
SCREAMING_SNAKE_CASE_ :List[Any] = mode.value
SCREAMING_SNAKE_CASE_ :Optional[Any] = os.path.join(UpperCAmelCase , F"{mode}.txt")
SCREAMING_SNAKE_CASE_ :Tuple = 1
SCREAMING_SNAKE_CASE_ :str = []
with open(UpperCAmelCase , encoding="utf-8") as f:
SCREAMING_SNAKE_CASE_ :Tuple = []
SCREAMING_SNAKE_CASE_ :int = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=UpperCAmelCase , labels=UpperCAmelCase))
guid_index += 1
SCREAMING_SNAKE_CASE_ :Tuple = []
SCREAMING_SNAKE_CASE_ :Any = []
else:
SCREAMING_SNAKE_CASE_ :int = line.split(" ")
words.append(splits[0])
if len(UpperCAmelCase) > 1:
labels.append(splits[self.label_idx].replace("\n" , ""))
else:
# Examples could have no label for mode = "test"
labels.append("O")
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=UpperCAmelCase , labels=UpperCAmelCase))
return examples
def _snake_case ( self : List[Any] , UpperCAmelCase : TextIO , UpperCAmelCase : TextIO , UpperCAmelCase : List):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
writer.write(UpperCAmelCase)
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
writer.write(UpperCAmelCase)
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0])
def _snake_case ( self : List[str] , UpperCAmelCase : str):
if path:
with open(UpperCAmelCase , "r") as f:
SCREAMING_SNAKE_CASE_ :Any = f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class _UpperCAmelCase ( lowercase ):
def __init__( self : Dict):
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2)
def _snake_case ( self : Union[str, Any] , UpperCAmelCase : str):
if path:
with open(UpperCAmelCase , "r") as f:
SCREAMING_SNAKE_CASE_ :Optional[int] = f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE_ :Dict = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class _UpperCAmelCase ( lowercase ):
def _snake_case ( self : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[Split, str]):
if isinstance(UpperCAmelCase , UpperCAmelCase):
SCREAMING_SNAKE_CASE_ :List[str] = mode.value
SCREAMING_SNAKE_CASE_ :List[str] = os.path.join(UpperCAmelCase , F"{mode}.txt")
SCREAMING_SNAKE_CASE_ :Dict = 1
SCREAMING_SNAKE_CASE_ :List[str] = []
with open(UpperCAmelCase , encoding="utf-8") as f:
for sentence in parse_incr(UpperCAmelCase):
SCREAMING_SNAKE_CASE_ :List[str] = []
SCREAMING_SNAKE_CASE_ :int = []
for token in sentence:
words.append(token["form"])
labels.append(token["upos"])
assert len(UpperCAmelCase) == len(UpperCAmelCase)
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=UpperCAmelCase , labels=UpperCAmelCase))
guid_index += 1
return examples
def _snake_case ( self : List[Any] , UpperCAmelCase : TextIO , UpperCAmelCase : TextIO , UpperCAmelCase : List):
SCREAMING_SNAKE_CASE_ :List[str] = 0
for sentence in parse_incr(UpperCAmelCase):
SCREAMING_SNAKE_CASE_ :str = preds_list[example_id]
SCREAMING_SNAKE_CASE_ :List[Any] = ""
for token in sentence:
out += F"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
out += "\n"
writer.write(UpperCAmelCase)
example_id += 1
def _snake_case ( self : Tuple , UpperCAmelCase : str):
if path:
with open(UpperCAmelCase , "r") as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 631 | 1 |
'''simple docstring'''
a__ : Optional[Any] =frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
a__ : Optional[int] =frozenset(['''prompt''', '''negative_prompt'''])
a__ : Union[str, Any] =frozenset([])
a__ : List[str] =frozenset(['''image'''])
a__ : Dict =frozenset(
[
'''image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
a__ : List[Any] =frozenset(['''image'''])
a__ : Union[str, Any] =frozenset(
[
'''prompt''',
'''image''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
]
)
a__ : Optional[Any] =frozenset(['''prompt''', '''image''', '''negative_prompt'''])
a__ : int =frozenset(
[
# Text guided image variation with an image mask
'''prompt''',
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
]
)
a__ : Union[str, Any] =frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])
a__ : List[Any] =frozenset(
[
# image variation with an image mask
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
a__ : Optional[Any] =frozenset(['''image''', '''mask_image'''])
a__ : Optional[Any] =frozenset(
[
'''example_image''',
'''image''',
'''mask_image''',
'''height''',
'''width''',
'''guidance_scale''',
]
)
a__ : List[str] =frozenset(['''example_image''', '''image''', '''mask_image'''])
a__ : List[str] =frozenset(['''class_labels'''])
a__ : Union[str, Any] =frozenset(['''class_labels'''])
a__ : int =frozenset(['''batch_size'''])
a__ : str =frozenset([])
a__ : Optional[int] =frozenset(['''batch_size'''])
a__ : str =frozenset([])
a__ : Dict =frozenset(
[
'''prompt''',
'''audio_length_in_s''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
a__ : List[str] =frozenset(['''prompt''', '''negative_prompt'''])
a__ : List[str] =frozenset(['''input_tokens'''])
a__ : List[str] =frozenset(['''input_tokens'''])
| 434 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
a__ : Optional[Any] =logging.get_logger(__name__)
def lowercase__ ( __lowercase : Union[str, Any] , __lowercase : Optional[int] , __lowercase : str , __lowercase : str=None , __lowercase : Optional[int]=None ) -> Union[str, Any]:
"""simple docstring"""
if "." in tensor_name:
__UpperCamelCase = tensor_name.split('.' )
for split in splits[:-1]:
__UpperCamelCase = getattr(__lowercase , __lowercase )
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''' )
__UpperCamelCase = new_module
__UpperCamelCase = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.''' )
__UpperCamelCase = tensor_name in module._buffers
__UpperCamelCase = getattr(__lowercase , __lowercase )
if old_value.device == torch.device('meta' ) and device not in ["meta", torch.device('meta' )] and value is None:
raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.''' )
__UpperCamelCase = False
__UpperCamelCase = False
if is_buffer or not is_bitsandbytes_available():
__UpperCamelCase = False
__UpperCamelCase = False
else:
__UpperCamelCase = hasattr(bnb.nn , 'Params4bit' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
__UpperCamelCase = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
__UpperCamelCase = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
__UpperCamelCase = old_value.to(__lowercase )
elif isinstance(__lowercase , torch.Tensor ):
__UpperCamelCase = value.to('cpu' )
if value.dtype == torch.inta:
__UpperCamelCase = version.parse(importlib.metadata.version('bitsandbytes' ) ) > version.parse(
'0.37.2' )
if not is_abit_serializable:
raise ValueError(
'Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '
'Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.' )
else:
__UpperCamelCase = torch.tensor(__lowercase , device='cpu' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , __lowercase ) and fpaa_statistics is None:
__UpperCamelCase = new_value.T
__UpperCamelCase = old_value.__dict__
if is_abit:
__UpperCamelCase = bnb.nn.IntaParams(__lowercase , requires_grad=__lowercase , **__lowercase ).to(__lowercase )
elif is_abit:
__UpperCamelCase = bnb.nn.Paramsabit(__lowercase , requires_grad=__lowercase , **__lowercase ).to(__lowercase )
__UpperCamelCase = new_value
if fpaa_statistics is not None:
setattr(module.weight , 'SCB' , fpaa_statistics.to(__lowercase ) )
else:
if value is None:
__UpperCamelCase = old_value.to(__lowercase )
elif isinstance(__lowercase , torch.Tensor ):
__UpperCamelCase = value.to(__lowercase )
else:
__UpperCamelCase = torch.tensor(__lowercase , device=__lowercase )
if is_buffer:
__UpperCamelCase = new_value
else:
__UpperCamelCase = nn.Parameter(__lowercase , requires_grad=old_value.requires_grad )
__UpperCamelCase = new_value
def lowercase__ ( __lowercase : List[Any] , __lowercase : Dict=None , __lowercase : List[Any]=None , __lowercase : str=None , __lowercase : int=False ) -> Optional[int]:
"""simple docstring"""
for name, module in model.named_children():
if current_key_name is None:
__UpperCamelCase = []
current_key_name.append(__lowercase )
if (isinstance(__lowercase , nn.Linear ) or isinstance(__lowercase , __lowercase )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '.'.join(__lowercase ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(__lowercase , __lowercase ):
__UpperCamelCase , __UpperCamelCase = module.weight.shape
else:
__UpperCamelCase = module.in_features
__UpperCamelCase = module.out_features
if quantization_config.quantization_method() == "llm_int8":
__UpperCamelCase = bnb.nn.LinearabitLt(
__lowercase , __lowercase , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
__UpperCamelCase = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
__UpperCamelCase = bnb.nn.Linearabit(
__lowercase , __lowercase , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
__UpperCamelCase = True
# Store the module class in case we need to transpose the weight later
__UpperCamelCase = type(__lowercase )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(__lowercase )
if len(list(module.children() ) ) > 0:
__UpperCamelCase , __UpperCamelCase = _replace_with_bnb_linear(
__lowercase , __lowercase , __lowercase , __lowercase , has_been_replaced=__lowercase , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def lowercase__ ( __lowercase : Optional[int] , __lowercase : Tuple=None , __lowercase : List[Any]=None , __lowercase : Union[str, Any]=None ) -> Dict:
"""simple docstring"""
__UpperCamelCase = ['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
__UpperCamelCase , __UpperCamelCase = _replace_with_bnb_linear(
__lowercase , __lowercase , __lowercase , __lowercase )
if not has_been_replaced:
logger.warning(
'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
' Please double check your model architecture, or submit an issue on github if you think this is'
' a bug.' )
return model
def lowercase__ ( *__lowercase : Tuple , **__lowercase : Any ) -> Optional[Any]:
"""simple docstring"""
warnings.warn(
'`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead' , __lowercase , )
return replace_with_bnb_linear(*__lowercase , **__lowercase )
def lowercase__ ( *__lowercase : Tuple , **__lowercase : int ) -> Any:
"""simple docstring"""
warnings.warn(
'`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead' , __lowercase , )
return set_module_quantized_tensor_to_device(*__lowercase , **__lowercase )
def lowercase__ ( __lowercase : Optional[int] ) -> int:
"""simple docstring"""
__UpperCamelCase = deepcopy(__lowercase ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
__UpperCamelCase = find_tied_parameters(__lowercase )
# For compatibility with Accelerate < 0.18
if isinstance(__lowercase , __lowercase ):
__UpperCamelCase = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
__UpperCamelCase = sum(__lowercase , [] )
__UpperCamelCase = len(__lowercase ) > 0
# Check if it is a base model
__UpperCamelCase = not hasattr(__lowercase , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
__UpperCamelCase = list(model.named_children() )
__UpperCamelCase = [list_modules[-1][0]]
# add last module together with tied weights
__UpperCamelCase = set(__lowercase ) - set(__lowercase )
__UpperCamelCase = list(set(__lowercase ) ) + list(__lowercase )
# remove ".weight" from the keys
__UpperCamelCase = ['.weight', '.bias']
__UpperCamelCase = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
__UpperCamelCase = name.replace(__lowercase , '' )
filtered_module_names.append(__lowercase )
return filtered_module_names
| 434 | 1 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_UpperCamelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCamelCase_ :
"""simple docstring"""
a_ =field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Model type selected in the list: """ + """, """.join(SCREAMING_SNAKE_CASE_ )} )
a_ =field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
a_ =field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a_ =field(
default=128 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
a_ =field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
a_ =field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
a_ =field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a_ =field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
a_ =field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a_ =field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a_ =field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
a_ =field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class lowerCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
a_ ="""train"""
a_ ="""dev"""
class lowerCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
a_ =42
a_ =42
a_ =42
a_ =42
def __init__( self : List[Any] , _a : SquadDataTrainingArguments , _a : PreTrainedTokenizer , _a : Optional[int] = None , _a : Union[str, Split] = Split.train , _a : Optional[bool] = False , _a : Optional[str] = None , _a : Optional[str] = "pt" , ) -> List[str]:
__lowerCamelCase : List[Any] = args
__lowerCamelCase : Any = is_language_sensitive
__lowerCamelCase : Union[str, Any] = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_a , _a ):
try:
__lowerCamelCase : Tuple = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
__lowerCamelCase : List[Any] = mode
# Load data features from cache or dataset file
__lowerCamelCase : Optional[int] = 'v2' if args.version_2_with_negative else 'v1'
__lowerCamelCase : Tuple = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCamelCase : Any = cached_features_file + '.lock'
with FileLock(_a ):
if os.path.exists(_a ) and not args.overwrite_cache:
__lowerCamelCase : Union[str, Any] = time.time()
__lowerCamelCase : List[Any] = torch.load(_a )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__lowerCamelCase : Dict = self.old_features['features']
__lowerCamelCase : Dict = self.old_features.get('dataset' , _a )
__lowerCamelCase : Dict = self.old_features.get('examples' , _a )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
' future run' )
else:
if mode == Split.dev:
__lowerCamelCase : List[Any] = self.processor.get_dev_examples(args.data_dir )
else:
__lowerCamelCase : Optional[int] = self.processor.get_train_examples(args.data_dir )
__lowerCamelCase ,__lowerCamelCase : Any = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_a , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_a , )
__lowerCamelCase : Any = time.time()
torch.save(
{'features': self.features, 'dataset': self.dataset, 'examples': self.examples} , _a , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self : Union[str, Any] ) -> Tuple:
return len(self.features )
def __getitem__( self : int , _a : str ) -> Dict[str, torch.Tensor]:
# Convert to Tensors and build dataset
__lowerCamelCase : Union[str, Any] = self.features[i]
__lowerCamelCase : str = torch.tensor(feature.input_ids , dtype=torch.long )
__lowerCamelCase : Dict = torch.tensor(feature.attention_mask , dtype=torch.long )
__lowerCamelCase : int = torch.tensor(feature.token_type_ids , dtype=torch.long )
__lowerCamelCase : Dict = torch.tensor(feature.cls_index , dtype=torch.long )
__lowerCamelCase : Dict = torch.tensor(feature.p_mask , dtype=torch.float )
__lowerCamelCase : Dict = torch.tensor(feature.is_impossible , dtype=torch.float )
__lowerCamelCase : List[str] = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'token_type_ids': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'cls_index': cls_index, 'p_mask': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'is_impossible': is_impossible} )
if self.is_language_sensitive:
inputs.update({'langs': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__lowerCamelCase : List[str] = torch.tensor(feature.start_position , dtype=torch.long )
__lowerCamelCase : Dict = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'start_positions': start_positions, 'end_positions': end_positions} )
return inputs
| 459 |
'''simple docstring'''
from __future__ import annotations
_UpperCamelCase = 10
def a_ ( _lowerCAmelCase ) -> list[int]:
__lowerCamelCase : str = 1
__lowerCamelCase : Union[str, Any] = max(_lowerCAmelCase )
while placement <= max_digit:
# declare and initialize empty buckets
__lowerCamelCase : list[list] = [[] for _ in range(_lowerCAmelCase )]
# split list_of_ints between the buckets
for i in list_of_ints:
__lowerCamelCase : List[str] = int((i / placement) % RADIX )
buckets[tmp].append(_lowerCAmelCase )
# put each buckets' contents into list_of_ints
__lowerCamelCase : Dict = 0
for b in range(_lowerCAmelCase ):
for i in buckets[b]:
__lowerCamelCase : List[str] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 459 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
class UpperCamelCase ( lowercase_ ):
lowercase = ['input_features']
def __init__( self ,__UpperCamelCase=80 ,__UpperCamelCase=1_6000 ,__UpperCamelCase=160 ,__UpperCamelCase=30 ,__UpperCamelCase=400 ,__UpperCamelCase=0.0 ,__UpperCamelCase=False ,**__UpperCamelCase ,) -> Any:
'''simple docstring'''
super().__init__(
feature_size=__UpperCamelCase ,sampling_rate=__UpperCamelCase ,padding_value=__UpperCamelCase ,return_attention_mask=__UpperCamelCase ,**__UpperCamelCase ,)
lowercase_ : Dict = n_fft
lowercase_ : Optional[Any] = hop_length
lowercase_ : Optional[int] = chunk_length
lowercase_ : Optional[Any] = chunk_length * sampling_rate
lowercase_ : Tuple = self.n_samples // hop_length
lowercase_ : int = sampling_rate
lowercase_ : int = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 ,num_mel_filters=__UpperCamelCase ,min_frequency=0.0 ,max_frequency=8000.0 ,sampling_rate=__UpperCamelCase ,norm='slaney' ,mel_scale='slaney' ,)
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> np.ndarray:
'''simple docstring'''
lowercase_ : Union[str, Any] = spectrogram(
__UpperCamelCase ,window_function(self.n_fft ,'hann' ) ,frame_length=self.n_fft ,hop_length=self.hop_length ,power=2.0 ,mel_filters=self.mel_filters ,log_mel='log10' ,)
lowercase_ : Optional[Any] = log_spec[:, :-1]
lowercase_ : int = np.maximum(__UpperCamelCase ,log_spec.max() - 8.0 )
lowercase_ : List[Any] = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def _UpperCAmelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase = 0.0 ) -> List[np.ndarray]:
'''simple docstring'''
if attention_mask is not None:
lowercase_ : Optional[int] = np.array(__UpperCamelCase ,np.intaa )
lowercase_ : Optional[Any] = []
for vector, length in zip(__UpperCamelCase ,attention_mask.sum(-1 ) ):
lowercase_ : Union[str, Any] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
lowercase_ : Any = padding_value
normed_input_values.append(__UpperCamelCase )
else:
lowercase_ : Optional[int] = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self ,__UpperCamelCase ,__UpperCamelCase = True ,__UpperCamelCase = None ,__UpperCamelCase = None ,__UpperCamelCase = None ,__UpperCamelCase = "max_length" ,__UpperCamelCase = None ,__UpperCamelCase = None ,__UpperCamelCase = None ,**__UpperCamelCase ,) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
lowercase_ : Optional[int] = isinstance(__UpperCamelCase ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
lowercase_ : Union[str, Any] = is_batched_numpy or (
isinstance(__UpperCamelCase ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowercase_ : Union[str, Any] = [np.asarray([speech] ,dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__UpperCamelCase ,np.ndarray ):
lowercase_ : Dict = np.asarray(__UpperCamelCase ,dtype=np.floataa )
elif isinstance(__UpperCamelCase ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowercase_ : Dict = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowercase_ : List[Any] = [np.asarray([raw_speech] ).T]
lowercase_ : str = BatchFeature({'input_features': raw_speech} )
# convert into correct format for padding
lowercase_ : str = self.pad(
__UpperCamelCase ,padding=__UpperCamelCase ,max_length=max_length if max_length else self.n_samples ,truncation=__UpperCamelCase ,pad_to_multiple_of=__UpperCamelCase ,return_attention_mask=return_attention_mask or do_normalize ,)
# zero-mean and unit-variance normalization
if do_normalize:
lowercase_ : int = self.zero_mean_unit_var_norm(
padded_inputs['input_features'] ,attention_mask=padded_inputs['attention_mask'] ,padding_value=self.padding_value ,)
lowercase_ : Tuple = np.stack(padded_inputs['input_features'] ,axis=0 )
# make sure list is in array format
lowercase_ : Dict = padded_inputs.get('input_features' ).transpose(2 ,0 ,1 )
lowercase_ : List[str] = [self._np_extract_fbank_features(__UpperCamelCase ) for waveform in input_features[0]]
if isinstance(input_features[0] ,__UpperCamelCase ):
lowercase_ : Optional[Any] = [np.asarray(__UpperCamelCase ,dtype=np.floataa ) for feature in input_features]
else:
lowercase_ : str = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
lowercase_ : str = padded_inputs['attention_mask'][:, :: self.hop_length]
if return_tensors is not None:
lowercase_ : Union[str, Any] = padded_inputs.convert_to_tensors(__UpperCamelCase )
return padded_inputs
def _UpperCAmelCase ( self ) -> Dict[str, Any]:
'''simple docstring'''
lowercase_ : Dict = copy.deepcopy(self.__dict__ )
lowercase_ : Union[str, Any] = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 711 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class UpperCamelCase ( lowercase_ ):
lowercase = 'switch_transformers'
lowercase = ['past_key_values']
lowercase = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self ,__UpperCamelCase=3_2128 ,__UpperCamelCase=768 ,__UpperCamelCase=64 ,__UpperCamelCase=2048 ,__UpperCamelCase=64 ,__UpperCamelCase=12 ,__UpperCamelCase=3 ,__UpperCamelCase=12 ,__UpperCamelCase=3 ,__UpperCamelCase=12 ,__UpperCamelCase=8 ,__UpperCamelCase=False ,__UpperCamelCase=0.01 ,__UpperCamelCase="float32" ,__UpperCamelCase=False ,__UpperCamelCase=32 ,__UpperCamelCase=128 ,__UpperCamelCase=0.1 ,__UpperCamelCase=1e-6 ,__UpperCamelCase=0.001 ,__UpperCamelCase=0.001 ,__UpperCamelCase=1.0 ,__UpperCamelCase="relu" ,__UpperCamelCase=True ,__UpperCamelCase=False ,__UpperCamelCase=True ,__UpperCamelCase=0 ,__UpperCamelCase=1 ,**__UpperCamelCase ,) -> str:
'''simple docstring'''
lowercase_ : List[str] = vocab_size
lowercase_ : Optional[Any] = d_model
lowercase_ : Dict = d_kv
lowercase_ : Dict = d_ff
lowercase_ : str = num_sparse_encoder_layers
lowercase_ : List[str] = num_layers
lowercase_ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowercase_ : Optional[Any] = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
lowercase_ : Tuple = self.num_layers // self.num_sparse_encoder_layers
else:
lowercase_ : Union[str, Any] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
lowercase_ : Union[str, Any] = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
lowercase_ : List[Any] = self.num_decoder_layers # HACK: this will create 0 sparse layers
lowercase_ : List[Any] = num_heads
lowercase_ : Dict = num_experts
lowercase_ : List[str] = expert_capacity
lowercase_ : Optional[Any] = router_bias
lowercase_ : Optional[Any] = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
lowercase_ : Optional[Any] = router_dtype
lowercase_ : Union[str, Any] = router_ignore_padding_tokens
lowercase_ : Any = relative_attention_num_buckets
lowercase_ : List[Any] = relative_attention_max_distance
lowercase_ : str = dropout_rate
lowercase_ : Any = layer_norm_epsilon
lowercase_ : Tuple = initializer_factor
lowercase_ : str = feed_forward_proj
lowercase_ : List[Any] = use_cache
lowercase_ : str = add_router_probs
lowercase_ : Tuple = router_z_loss_coef
lowercase_ : int = router_aux_loss_coef
lowercase_ : List[str] = self.feed_forward_proj.split('-' )
lowercase_ : List[str] = act_info[-1]
lowercase_ : Optional[Any] = act_info[0] == 'gated'
if len(__UpperCamelCase ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
lowercase_ : Union[str, Any] = 'gelu_new'
super().__init__(
pad_token_id=__UpperCamelCase ,eos_token_id=__UpperCamelCase ,is_encoder_decoder=__UpperCamelCase ,**__UpperCamelCase ,)
| 477 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _A( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : Dict = BioGptTokenizer
UpperCamelCase : Any = False
def UpperCAmelCase_ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__A : Dict = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
__A : Optional[Any] = dict(zip(_A , range(len(_A ) ) ) )
__A : Union[str, Any] = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']
__A : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__A : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(_A ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(_A ) )
def UpperCAmelCase_ ( self , _A ):
__A : List[str] = 'lower newer'
__A : Tuple = 'lower newer'
return input_text, output_text
def UpperCAmelCase_ ( self ):
__A : Dict = BioGptTokenizer(self.vocab_file , self.merges_file )
__A : List[str] = 'lower'
__A : Any = ['low', 'er</w>']
__A : List[str] = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__A : Any = tokens + ['<unk>']
__A : Union[str, Any] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_A ) , _A )
@slow
def UpperCAmelCase_ ( self ):
__A : Union[str, Any] = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
__A : Union[str, Any] = tokenizer.encode('sequence builders' , add_special_tokens=_A )
__A : List[str] = tokenizer.encode('multi-sequence build' , add_special_tokens=_A )
__A : Optional[Any] = tokenizer.build_inputs_with_special_tokens(_A )
__A : Tuple = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 239 |
def _SCREAMING_SNAKE_CASE ( a ) -> bool:
__A : str = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _SCREAMING_SNAKE_CASE ( a = 50_00 ) -> int:
__A : Optional[int] = [(i * (3 * i - 1)) // 2 for i in range(1 , a )]
for i, pentagonal_i in enumerate(a ):
for j in range(a , len(a ) ):
__A : Dict = pentagonal_nums[j]
__A : Tuple = pentagonal_i + pentagonal_j
__A : Optional[Any] = pentagonal_j - pentagonal_i
if is_pentagonal(a ) and is_pentagonal(a ):
return b
return -1
if __name__ == "__main__":
print(F"""{solution() = }""")
| 239 | 1 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def UpperCamelCase_ ( lowerCamelCase : Callable ) -> Callable:
"""simple docstring"""
@wraps(lowerCamelCase )
def _inner_fn(*lowerCamelCase : str , **lowerCamelCase : List[Any] ):
warnings.warn(
(f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""") , lowerCamelCase , )
return fn(*lowerCamelCase , **lowerCamelCase )
return _inner_fn
| 147 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
),
}
class _UpperCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
snake_case_ = 'xlm-prophetnet'
snake_case_ = ['past_key_values']
snake_case_ = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self : Tuple , snake_case : Optional[float] = 0.1 , snake_case : Optional[Union[str, Callable]] = "gelu" , snake_case : Optional[int] = 3_0522 , snake_case : Optional[int] = 1024 , snake_case : Optional[int] = 4096 , snake_case : Optional[int] = 12 , snake_case : Optional[int] = 16 , snake_case : Optional[int] = 4096 , snake_case : Optional[int] = 12 , snake_case : Optional[int] = 16 , snake_case : Optional[float] = 0.1 , snake_case : Optional[float] = 0.1 , snake_case : Optional[int] = 512 , snake_case : Optional[float] = 0.02 , snake_case : Optional[bool] = True , snake_case : Optional[bool] = True , snake_case : Optional[int] = 0 , snake_case : Optional[int] = 2 , snake_case : Optional[int] = 32 , snake_case : Optional[int] = 128 , snake_case : Optional[bool] = False , snake_case : Optional[float] = 0.0 , snake_case : Optional[bool] = True , snake_case : Optional[int] = 0 , snake_case : Optional[int] = 1 , snake_case : Optional[int] = 2 , **snake_case : List[str] , ) -> str:
'''simple docstring'''
__magic_name__ : List[str] = vocab_size
__magic_name__ : Optional[int] = hidden_size
__magic_name__ : Any = encoder_ffn_dim
__magic_name__ : str = num_encoder_layers
__magic_name__ : List[str] = num_encoder_attention_heads
__magic_name__ : Dict = decoder_ffn_dim
__magic_name__ : int = num_decoder_layers
__magic_name__ : str = num_decoder_attention_heads
__magic_name__ : Tuple = max_position_embeddings
__magic_name__ : Optional[int] = init_std # Normal(0, this parameter)
__magic_name__ : Optional[int] = activation_function
# parameters for xlmprophetnet
__magic_name__ : int = ngram
__magic_name__ : List[Any] = num_buckets
__magic_name__ : int = relative_max_distance
__magic_name__ : List[str] = disable_ngram_loss
__magic_name__ : Union[str, Any] = eps
# 3 Types of Dropout
__magic_name__ : Tuple = attention_dropout
__magic_name__ : List[Any] = activation_dropout
__magic_name__ : Optional[int] = dropout
__magic_name__ : Dict = use_cache
super().__init__(
pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , is_encoder_decoder=snake_case , add_cross_attention=snake_case , decoder_start_token_id=snake_case , **snake_case , )
@property
def _UpperCAmelCase ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def _UpperCAmelCase ( self : List[Any] , snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 147 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase_ ( self : Union[str, Any] ):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(_UpperCAmelCase ):
_A = AutoConfig.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
_A = FlaxAutoModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(_UpperCAmelCase ):
_A = AutoConfig.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
_A = FlaxAutoModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
@slow
def lowerCAmelCase_ ( self : Tuple ):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
_A = AutoTokenizer.from_pretrained(_UpperCAmelCase )
_A = FlaxBertModel.from_pretrained(_UpperCAmelCase )
_A = tokenizer('Do you support jax jitted function?' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**_UpperCAmelCase : List[str] ):
return model(**_UpperCAmelCase )
eval(**_UpperCAmelCase ).block_until_ready()
@slow
def lowerCAmelCase_ ( self : List[str] ):
for model_name in ["roberta-base", "roberta-large"]:
_A = AutoTokenizer.from_pretrained(_UpperCAmelCase )
_A = FlaxRobertaModel.from_pretrained(_UpperCAmelCase )
_A = tokenizer('Do you support jax jitted function?' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**_UpperCAmelCase : Dict ):
return model(**_UpperCAmelCase )
eval(**_UpperCAmelCase ).block_until_ready()
def lowerCAmelCase_ ( self : Dict ):
with self.assertRaisesRegex(
_UpperCAmelCase , 'bert-base is not a local folder and is not a valid model identifier' ):
_A = FlaxAutoModel.from_pretrained('bert-base' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
with self.assertRaisesRegex(
_UpperCAmelCase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_A = FlaxAutoModel.from_pretrained(_UpperCAmelCase , revision='aaaaaa' )
def lowerCAmelCase_ ( self : List[Any] ):
with self.assertRaisesRegex(
_UpperCAmelCase , 'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack' , ):
_A = FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def lowerCAmelCase_ ( self : Tuple ):
with self.assertRaisesRegex(_UpperCAmelCase , 'Use `from_pt=True` to load this model' ):
_A = FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
| 7 |
"""simple docstring"""
class lowercase_ :
'''simple docstring'''
def __init__( self : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = None
_A = None
_A = graph
self._normalize_graph(_UpperCAmelCase , _UpperCAmelCase )
_A = len(_UpperCAmelCase )
_A = None
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict ):
if sources is int:
_A = [sources]
if sinks is int:
_A = [sinks]
if len(_UpperCAmelCase ) == 0 or len(_UpperCAmelCase ) == 0:
return
_A = sources[0]
_A = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(_UpperCAmelCase ) > 1 or len(_UpperCAmelCase ) > 1:
_A = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_A = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_A = max_input_flow
_A = 0
_A = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_A = max_input_flow
_A = size - 1
def lowerCAmelCase_ ( self : Optional[Any] ):
if self.maximum_flow_algorithm is None:
raise Exception('You need to set maximum flow algorithm before.' )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : Union[str, Any] ):
_A = algorithm(self )
class lowercase_ :
'''simple docstring'''
def __init__( self : List[Any] , _UpperCAmelCase : Union[str, Any] ):
_A = flow_network
_A = flow_network.verticesCount
_A = flow_network.sourceIndex
_A = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_A = flow_network.graph
_A = False
def lowerCAmelCase_ ( self : Optional[Any] ):
if not self.executed:
self._algorithm()
_A = True
def lowerCAmelCase_ ( self : int ):
pass
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : Any ):
super().__init__(_UpperCAmelCase )
# use this to save your result
_A = -1
def lowerCAmelCase_ ( self : Optional[Any] ):
if not self.executed:
raise Exception('You should execute algorithm before using its result!' )
return self.maximum_flow
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : List[Any] ):
super().__init__(_UpperCAmelCase )
_A = [[0] * self.verticies_count for i in range(self.verticies_count )]
_A = [0] * self.verticies_count
_A = [0] * self.verticies_count
def lowerCAmelCase_ ( self : Dict ):
_A = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_A = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_A = 0
while i < len(_UpperCAmelCase ):
_A = vertices_list[i]
_A = self.heights[vertex_index]
self.process_vertex(_UpperCAmelCase )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(_UpperCAmelCase ) )
_A = 0
else:
i += 1
_A = sum(self.preflow[self.source_index] )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Any ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(_UpperCAmelCase , _UpperCAmelCase )
self.relabel(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple ):
_A = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : int ):
_A = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_A = self.heights[to_index]
if min_height is not None:
_A = min_height + 1
if __name__ == "__main__":
a = [0]
a = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
a = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
a = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
a = flow_network.find_maximum_flow()
print(F'''maximum flow is {maximum_flow}''')
| 7 | 1 |
"""simple docstring"""
class _a :
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : str ):
lowerCamelCase__ = val
lowerCamelCase__ = None
lowerCamelCase__ = None
def _UpperCamelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Tuple ):
if self.val:
if val < self.val:
if self.left is None:
lowerCamelCase__ = Node(UpperCamelCase_ )
else:
self.left.insert(UpperCamelCase_ )
elif val > self.val:
if self.right is None:
lowerCamelCase__ = Node(UpperCamelCase_ )
else:
self.right.insert(UpperCamelCase_ )
else:
lowerCamelCase__ = val
def snake_case ( _a: Optional[Any] , _a: List[str] )-> List[Any]:
'''simple docstring'''
if root:
inorder(root.left , _lowercase )
res.append(root.val )
inorder(root.right , _lowercase )
def snake_case ( _a: Optional[int] )-> Tuple:
'''simple docstring'''
if len(_lowercase ) == 0:
return arr
lowerCamelCase__ = Node(arr[0] )
for i in range(1 , len(_lowercase ) ):
root.insert(arr[i] )
# Traverse BST in order.
lowerCamelCase__ = []
inorder(_lowercase , _lowercase )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 704 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 659 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCamelCase_ = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n"
UpperCamelCase_ = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"
UpperCamelCase_ = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for 'record': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'prediction_text': the predicted answer text\n - for 'multirc': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question-answer pair as specified by the dataset\n - 'prediction': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for 'record': list of question-answers dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'answers': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for 'record':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1': F1 score\n - for 'multirc':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1_m': Per-question macro-F1 score\n - 'f1_a': Average F1 score over all answers\n - for 'axb':\n 'matthews_correlation': Matthew Correlation\n - for 'cb':\n - 'accuracy': Accuracy\n - 'f1': F1 score\n - for all others:\n - 'accuracy': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def lowercase__( __UpperCamelCase: str ,__UpperCamelCase: Any ):
"""simple docstring"""
return float((preds == labels).mean() )
def lowercase__( __UpperCamelCase: Dict ,__UpperCamelCase: Optional[int] ,__UpperCamelCase: Union[str, Any]="binary" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = simple_accuracy(__UpperCamelCase ,__UpperCamelCase )
SCREAMING_SNAKE_CASE : Any = float(fa_score(y_true=__UpperCamelCase ,y_pred=__UpperCamelCase ,average=__UpperCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowercase__( __UpperCamelCase: List[Any] ,__UpperCamelCase: Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = {}
for id_pred, label in zip(__UpperCamelCase ,__UpperCamelCase ):
SCREAMING_SNAKE_CASE : str = f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
SCREAMING_SNAKE_CASE : Any = id_pred['prediction']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
SCREAMING_SNAKE_CASE : Optional[Any] = [(pred, label)]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = [], []
for question, preds_labels in question_map.items():
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = zip(*__UpperCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = fa_score(y_true=__UpperCamelCase ,y_pred=__UpperCamelCase ,average='macro' )
fas.append(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = int(sum(pred == label for pred, label in preds_labels ) == len(__UpperCamelCase ) )
ems.append(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Tuple = float(sum(__UpperCamelCase ) / len(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE : Optional[int] = sum(__UpperCamelCase ) / len(__UpperCamelCase )
SCREAMING_SNAKE_CASE : int = float(fa_score(y_true=__UpperCamelCase ,y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _a ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
'''simple docstring'''
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(self._get_feature_types() ), codebase_urls=[], reference_urls=[], format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"prediction_text": datasets.Value('string' ),
},
"references": {
"idx": {
"passage": datasets.Value('int64' ),
"query": datasets.Value('int64' ),
},
"answers": datasets.Sequence(datasets.Value('string' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('int64' ),
"paragraph": datasets.Value('int64' ),
"question": datasets.Value('int64' ),
},
"prediction": datasets.Value('int64' ),
},
"references": datasets.Value('int64' ),
}
else:
return {
"predictions": datasets.Value('int64' ),
"references": datasets.Value('int64' ),
}
def UpperCamelCase_ ( self, A, A ):
'''simple docstring'''
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(A, A )}
elif self.config_name == "cb":
return acc_and_fa(A, A, fa_avg='macro' )
elif self.config_name == "record":
SCREAMING_SNAKE_CASE : List[Any] = [
{
'qas': [
{'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
for ref in references
]
}
]
SCREAMING_SNAKE_CASE : Any = {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
return evaluate_record(A, A )[0]
elif self.config_name == "multirc":
return evaluate_multirc(A, A )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(A, A )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]' )
| 28 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCAmelCase__ ( unittest.TestCase ):
def __UpperCamelCase ( self : List[str] ) -> Any:
A = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(__UpperCamelCase ) )
def __UpperCamelCase ( self : str ) -> List[Any]:
A = [
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(__UpperCamelCase ) )
def __UpperCamelCase ( self : Any ) -> Tuple:
A = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(__UpperCamelCase ) )
def __UpperCamelCase ( self : Dict ) -> Any:
A = [
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
self.assertTrue(is_safetensors_compatible(__UpperCamelCase ) )
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
A = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
# Removed: 'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(__UpperCamelCase ) )
def __UpperCamelCase ( self : List[str] ) -> List[str]:
A = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
A = 'fp16'
self.assertTrue(is_safetensors_compatible(__UpperCamelCase , variant=__UpperCamelCase ) )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
A = [
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
A = 'fp16'
self.assertTrue(is_safetensors_compatible(__UpperCamelCase , variant=__UpperCamelCase ) )
def __UpperCamelCase ( self : int ) -> Optional[int]:
# pass variant but use the non-variant filenames
A = [
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
A = 'fp16'
self.assertTrue(is_safetensors_compatible(__UpperCamelCase , variant=__UpperCamelCase ) )
def __UpperCamelCase ( self : Any ) -> List[str]:
A = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
A = 'fp16'
self.assertFalse(is_safetensors_compatible(__UpperCamelCase , variant=__UpperCamelCase ) )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
A = [
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
]
A = 'fp16'
self.assertTrue(is_safetensors_compatible(__UpperCamelCase , variant=__UpperCamelCase ) )
def __UpperCamelCase ( self : List[str] ) -> int:
# pass variant but use the non-variant filenames
A = [
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
A = 'fp16'
self.assertTrue(is_safetensors_compatible(__UpperCamelCase , variant=__UpperCamelCase ) )
def __UpperCamelCase ( self : List[str] ) -> Optional[Any]:
A = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
# 'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
A = 'fp16'
self.assertFalse(is_safetensors_compatible(__UpperCamelCase , variant=__UpperCamelCase ) ) | 106 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
A_ = logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase )
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
def __init__( self : List[Any] , *snake_case : str , **snake_case : List[str] ):
'''simple docstring'''
super().__init__(*snake_case , **snake_case )
requires_backends(self , """vision""" )
self.check_model_type(snake_case )
def __call__( self : Dict , snake_case : Union[str, List[str], "Image.Image", List["Image.Image"]] , **snake_case : Optional[Any] ):
'''simple docstring'''
return super().__call__(snake_case , **snake_case )
def _UpperCamelCase ( self : List[str] , **snake_case : Tuple ):
'''simple docstring'''
return {}, {}, {}
def _UpperCamelCase ( self : str , snake_case : List[Any] ):
'''simple docstring'''
A__ : List[str] = load_image(snake_case )
A__ : str = image.size
A__ : int = self.image_processor(images=snake_case , return_tensors=self.framework )
return model_inputs
def _UpperCamelCase ( self : Tuple , snake_case : Union[str, Any] ):
'''simple docstring'''
A__ : str = self.model(**snake_case )
return model_outputs
def _UpperCamelCase ( self : Tuple , snake_case : Dict ):
'''simple docstring'''
A__ : Optional[int] = model_outputs.predicted_depth
A__ : Tuple = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode="""bicubic""" , align_corners=snake_case )
A__ : List[Any] = prediction.squeeze().cpu().numpy()
A__ : List[str] = (output * 255 / np.max(snake_case )).astype("""uint8""" )
A__ : Dict = Image.fromarray(snake_case )
A__ : int = {}
A__ : List[str] = predicted_depth
A__ : Tuple = depth
return output_dict
| 713 |
"""simple docstring"""
import baseaa
def _lowerCAmelCase ( UpperCAmelCase__ : str ) ->bytes:
return baseaa.baaencode(string.encode("""utf-8""" ) )
def _lowerCAmelCase ( UpperCAmelCase__ : bytes ) ->str:
return baseaa.baadecode(UpperCAmelCase__ ).decode("""utf-8""" )
if __name__ == "__main__":
A_ = '''Hello World!'''
A_ = baseaa_encode(test)
print(encoded)
A_ = baseaa_decode(encoded)
print(decoded)
| 498 | 0 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class _UpperCAmelCase( lowerCamelCase ):
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = pa.array(TypedSequence([1, 2, 3]))
self.assertEqual(arr.type , pa.intaa())
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
with self.assertRaises(__a):
_UpperCamelCase = pa.array(TypedSequence([1, 2, 3]) , type=pa.intaa())
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
with self.assertRaises(__a):
_UpperCamelCase = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''bool''') , type=Value('''int64''')))
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = pa.array(TypedSequence([1, 2, 3] , type=Value('''int32''')))
self.assertEqual(arr.type , pa.intaa())
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
_UpperCamelCase = pa.array(TypedSequence(['''foo''', '''bar'''] , type=Value('''int64''')))
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''int32''')))
self.assertEqual(arr.type , pa.intaa())
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=Value('''int64''')))
self.assertEqual(arr.type , pa.string())
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , '''int64''')))
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64'''))
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
_UpperCamelCase = pa.array(TypedSequence(['''foo''', '''bar'''] , type=ArrayaD((1, 3) , '''int64''')))
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , '''int64''')))
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64'''))
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=ArrayaD((1, 3) , '''int64''')))
self.assertEqual(arr.type , pa.string())
@require_pil
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
import PIL.Image
_UpperCamelCase = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta).reshape(2 , 5))
with patch(
'''datasets.arrow_writer.cast_to_python_objects''' , side_effect=__a) as mock_cast_to_python_objects:
_UpperCamelCase = pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image] , type=Image()))
_UpperCamelCase , _UpperCamelCase = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('''optimize_list_casting''' , __a)
self.assertFalse(kwargs['''optimize_list_casting'''])
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = pa.BufferReader(__snake_case ) if isinstance(__snake_case, pa.Buffer ) else pa.memory_map(__snake_case )
_UpperCamelCase = pa.ipc.open_stream(__snake_case )
_UpperCamelCase = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('''writer_batch_size''', [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''', [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
_UpperCamelCase = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case, schema=__snake_case, writer_batch_size=__snake_case ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCamelCase = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def lowerCamelCase__ ( ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
_UpperCamelCase = Features({'''labels''': ClassLabel(names=['''neg''', '''pos'''] )} )
with ArrowWriter(stream=__snake_case, features=__snake_case ) as writer:
writer.write({'''labels''': 0} )
writer.write({'''labels''': 1} )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
_UpperCamelCase = pa.BufferReader(output.getvalue() )
_UpperCamelCase = pa.ipc.open_stream(__snake_case )
_UpperCamelCase = f.read_all()
_UpperCamelCase = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(__snake_case )
@pytest.mark.parametrize('''writer_batch_size''', [None, 1, 10] )
def lowerCamelCase__ ( __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case, writer_batch_size=__snake_case, hash_salt='''split_name''', check_duplicates=__snake_case, ) as writer:
with pytest.raises(__snake_case ):
writer.write({'''col_1''': '''foo''', '''col_2''': 1}, key=[1, 2] )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''', [None, 2, 10] )
def lowerCamelCase__ ( __snake_case ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case, writer_batch_size=__snake_case, hash_salt='''split_name''', check_duplicates=__snake_case, ) as writer:
with pytest.raises(__snake_case ):
writer.write({'''col_1''': '''foo''', '''col_2''': 1}, key=10 )
writer.write({'''col_1''': '''bar''', '''col_2''': 2}, key=10 )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''', [None, 2, 10] )
def lowerCamelCase__ ( __snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case, writer_batch_size=__snake_case, hash_salt='''split_name''', check_duplicates=__snake_case, ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1}, key=1 )
writer.write({'''col_1''': '''bar''', '''col_2''': 2}, key=2 )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''', [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''', [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
_UpperCamelCase = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case, schema=__snake_case, writer_batch_size=__snake_case ) as writer:
writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} )
writer.write_batch({'''col_1''': [], '''col_2''': []} )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCamelCase = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''', [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''', [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
_UpperCamelCase = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case, schema=__snake_case, writer_batch_size=__snake_case ) as writer:
writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} ) )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCamelCase = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''', [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''', [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Any:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
_UpperCamelCase = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case, schema=__snake_case, writer_batch_size=__snake_case ) as writer:
writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]} ) )
writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]} ) )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCamelCase = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCamelCase = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
_UpperCamelCase = os.path.join(__snake_case, '''test.arrow''' )
with ArrowWriter(path=__snake_case, schema=pa.schema(__snake_case ) ) as writer:
writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(__snake_case, metadata=writer._schema.metadata )
_check_output(__snake_case, 1 )
def lowerCamelCase__ ( __snake_case ) -> Optional[int]:
"""simple docstring"""
if pa.types.is_list(__snake_case ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
if isinstance(lst[0], __snake_case ):
change_first_primitive_element_in_list(lst[0], __snake_case )
else:
_UpperCamelCase = value
@pytest.mark.parametrize('''optimized_int_type, expected_dtype''', [(None, pa.intaa()), (Value('''int32''' ), pa.intaa())] )
@pytest.mark.parametrize('''sequence''', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = pa.array(TypedSequence(__snake_case, optimized_int_type=__snake_case ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'''col, expected_dtype''', [
('''attention_mask''', pa.inta()),
('''special_tokens_mask''', pa.inta()),
('''token_type_ids''', pa.inta()),
('''input_ids''', pa.intaa()),
('''other''', pa.intaa()),
], )
@pytest.mark.parametrize('''sequence''', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = pa.array(OptimizedTypedSequence(__snake_case, col=__snake_case ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
_UpperCamelCase = copy.deepcopy(__snake_case )
_UpperCamelCase = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(__snake_case, __snake_case )
_UpperCamelCase = pa.array(OptimizedTypedSequence(__snake_case, col=__snake_case ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('''raise_exception''', [False, True] )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Any:
"""simple docstring"""
_UpperCamelCase = str(tmp_path / '''dataset-train.arrow''' )
try:
with ArrowWriter(path=__snake_case ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def lowerCamelCase__ ( __snake_case ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = '''mock://dataset-train.arrow'''
with ArrowWriter(path=__snake_case, storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs, type(__snake_case ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(__snake_case )
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = pa.BufferOutputStream()
with ParquetWriter(stream=__snake_case ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_UpperCamelCase , _UpperCamelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_UpperCamelCase = pa.BufferReader(output.getvalue() )
_UpperCamelCase = pq.read_table(__snake_case )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('''embed_local_files''', [False, True] )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
import PIL.Image
_UpperCamelCase = str(tmp_path / '''test_image_rgb.jpg''' )
PIL.Image.fromarray(np.zeros((5, 5), dtype=np.uinta ) ).save(__snake_case, format='''png''' )
_UpperCamelCase = pa.BufferOutputStream()
with ParquetWriter(
stream=__snake_case, features=Features({'''image''': Image()} ), embed_local_files=__snake_case ) as writer:
writer.write({'''image''': image_path} )
writer.finalize()
_UpperCamelCase = pa.BufferReader(output.getvalue() )
_UpperCamelCase = pq.read_table(__snake_case )
_UpperCamelCase = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['''image'''][0]['''path'''], __snake_case )
with open(__snake_case, '''rb''' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = pa.schema([pa.field('''col_1''', pa.string(), nullable=__snake_case )] )
_UpperCamelCase = pa.BufferOutputStream()
with ArrowWriter(stream=__snake_case ) as writer:
writer._build_writer(inferred_schema=__snake_case )
assert writer._schema == pa.schema([pa.field('''col_1''', pa.string() )] )
| 19 |
import operator as op
UpperCamelCase = 'scaler.pt'
UpperCamelCase = 'pytorch_model'
UpperCamelCase = 'random_states'
UpperCamelCase = 'optimizer'
UpperCamelCase = 'scheduler'
UpperCamelCase = 'pytorch_model.bin'
UpperCamelCase = 'pytorch_model.bin.index.json'
UpperCamelCase = 'model.safetensors'
UpperCamelCase = 'model.safetensors.index.json'
UpperCamelCase = '1.10.2'
UpperCamelCase = 'py38'
UpperCamelCase = '4.17.0'
UpperCamelCase = ['ml.p3.16xlarge', 'ml.p3dn.24xlarge', 'ml.p4dn.24xlarge']
UpperCamelCase = ['FULL_SHARD', 'SHARD_GRAD_OP', 'NO_SHARD', 'HYBRID_SHARD', 'HYBRID_SHARD_ZERO2']
UpperCamelCase = ['TRANSFORMER_BASED_WRAP', 'SIZE_BASED_WRAP', 'NO_WRAP']
UpperCamelCase = ['BACKWARD_PRE', 'BACKWARD_POST', 'NO_PREFETCH']
UpperCamelCase = ['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT']
UpperCamelCase = '2.0.1'
UpperCamelCase = ['pdsh', 'standard', 'openmpi', 'mvapich']
UpperCamelCase = ['default', 'reduce-overhead', 'max-autotune']
UpperCamelCase = {'>': op.gt, '>=': op.ge, '==': op.eq, '!=': op.ne, '<=': op.le, '<': op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
UpperCamelCase = [
'nnodes',
'nproc_per_node',
'rdzv_backend',
'rdzv_endpoint',
'rdzv_id',
'rdzv_conf',
'standalone',
'max_restarts',
'monitor_interval',
'start_method',
'role',
'module',
'm',
'no_python',
'run_path',
'log_dir',
'r',
'redirects',
't',
'tee',
'node_rank',
'master_addr',
'master_port',
]
UpperCamelCase = ['DEEPSPEED', 'MULTI_GPU', 'FSDP', 'MEGATRON_LM']
UpperCamelCase = ['DEEPSPEED', 'MULTI_XPU', 'FSDP']
| 61 | 0 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict =logging.get_logger(__name__)
# TODO Update this
A : List[str] ={
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class _a ( __a ):
__a : List[Any] = """esm"""
def __init__( self : str , lowercase : Optional[Any]=None , lowercase : Any=None , lowercase : Union[str, Any]=None , lowercase : Dict=768 , lowercase : Dict=12 , lowercase : str=12 , lowercase : Optional[int]=3_072 , lowercase : Dict=0.1 , lowercase : str=0.1 , lowercase : Tuple=1_026 , lowercase : int=0.02 , lowercase : int=1E-12 , lowercase : Any="absolute" , lowercase : List[Any]=True , lowercase : Any=None , lowercase : Optional[Any]=False , lowercase : Optional[Any]=False , lowercase : str=None , lowercase : List[Any]=None , **lowercase : List[Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase , mask_token_id=lowercase , **lowercase )
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = position_embedding_type
UpperCAmelCase = use_cache
UpperCAmelCase = emb_layer_norm_before
UpperCAmelCase = token_dropout
UpperCAmelCase = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
UpperCAmelCase = EsmFoldConfig()
elif isinstance(lowercase , lowercase ):
UpperCAmelCase = EsmFoldConfig(**lowercase )
UpperCAmelCase = esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
UpperCAmelCase = get_default_vocab_list()
else:
UpperCAmelCase = vocab_list
else:
UpperCAmelCase = None
UpperCAmelCase = None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , lowercase ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = super().to_dict()
if isinstance(self.esmfold_config , lowercase ):
UpperCAmelCase = self.esmfold_config.to_dict()
return output
@dataclass
class _a :
__a : str = None
__a : bool = True
__a : bool = False
__a : bool = False
__a : bool = False
__a : float = 0
__a : bool = True
__a : bool = False
__a : int = 128
__a : "TrunkConfig" = None
def A ( self : List[str] ):
'''simple docstring'''
if self.trunk is None:
UpperCAmelCase = TrunkConfig()
elif isinstance(self.trunk , lowercase ):
UpperCAmelCase = TrunkConfig(**self.trunk )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = asdict(self )
UpperCAmelCase = self.trunk.to_dict()
return output
@dataclass
class _a :
__a : int = 48
__a : int = 1_024
__a : int = 128
__a : int = 32
__a : int = 32
__a : int = 32
__a : float = 0
__a : float = 0
__a : bool = False
__a : int = 4
__a : Optional[int] = 128
__a : "StructureModuleConfig" = None
def A ( self : List[Any] ):
'''simple docstring'''
if self.structure_module is None:
UpperCAmelCase = StructureModuleConfig()
elif isinstance(self.structure_module , lowercase ):
UpperCAmelCase = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}." )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f" {self.sequence_state_dim} and {self.sequence_state_dim}." )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f" {self.pairwise_state_dim} and {self.pairwise_state_dim}." )
UpperCAmelCase = self.sequence_state_dim // self.sequence_head_width
UpperCAmelCase = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}." )
if self.dropout >= 0.4:
raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}." )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = asdict(self )
UpperCAmelCase = self.structure_module.to_dict()
return output
@dataclass
class _a :
__a : int = 384
__a : int = 128
__a : int = 16
__a : int = 128
__a : int = 12
__a : int = 4
__a : int = 8
__a : float = 0.1
__a : int = 8
__a : int = 1
__a : int = 2
__a : int = 7
__a : int = 10
__a : float = 1e-8
__a : float = 1e5
def A ( self : str ):
'''simple docstring'''
return asdict(self )
def snake_case_ ():
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 708 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 358 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ : Dict = get_logger()
a_ : Optional[dict] = None
class __UpperCamelCase ( TensorFormatter[Mapping, '''jax.Array''', Mapping] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
super().__init__(features=SCREAMING_SNAKE_CASE )
import jax
from jaxlib.xla_client import Device
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError(
f"Expected {device} to be a `str` not {type(SCREAMING_SNAKE_CASE )}, as `jaxlib.xla_extension.Device` "
'''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
'''the device with `str()` to get its string identifier that will be internally mapped '''
'''to the actual `jaxlib.xla_extension.Device`.''' )
a__ = device if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
a__ = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"Device with string identifier {self.device} not listed among the available "
f"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
f"device: {str(jax.devices()[0] )}." )
a__ = str(jax.devices()[0] )
a__ = jnp_array_kwargs
@staticmethod
def _UpperCAmelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
import jax
return {str(SCREAMING_SNAKE_CASE ): device for device in jax.devices()}
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> str:
import jax
import jax.numpy as jnp
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and column:
if all(
isinstance(SCREAMING_SNAKE_CASE , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(SCREAMING_SNAKE_CASE , axis=0 )
return column
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Any:
import jax
import jax.numpy as jnp
if isinstance(SCREAMING_SNAKE_CASE , (str, bytes, type(SCREAMING_SNAKE_CASE )) ):
return value
elif isinstance(SCREAMING_SNAKE_CASE , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
a__ = {}
if isinstance(SCREAMING_SNAKE_CASE , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
a__ = {'''dtype''': jnp.intaa}
else:
a__ = {'''dtype''': jnp.intaa}
elif isinstance(SCREAMING_SNAKE_CASE , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
a__ = {'''dtype''': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(SCREAMING_SNAKE_CASE , PIL.Image.Image ):
a__ = np.asarray(SCREAMING_SNAKE_CASE )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
a__ = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(SCREAMING_SNAKE_CASE , **{**default_dtype, **self.jnp_array_kwargs} )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> str:
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(SCREAMING_SNAKE_CASE , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(SCREAMING_SNAKE_CASE , '''__array__''' ) and not isinstance(SCREAMING_SNAKE_CASE , jax.Array ):
a__ = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(SCREAMING_SNAKE_CASE , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE ) for substruct in data_struct] )
elif isinstance(SCREAMING_SNAKE_CASE , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE ) for substruct in data_struct] )
return self._tensorize(SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Tuple:
return map_nested(self._recursive_tensorize , SCREAMING_SNAKE_CASE , map_list=SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Mapping:
a__ = self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE )
a__ = self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE )
return self.recursive_tensorize(SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> "jax.Array":
a__ = self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE )
a__ = self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE , pa_table.column_names[0] )
a__ = self.recursive_tensorize(SCREAMING_SNAKE_CASE )
a__ = self._consolidate(SCREAMING_SNAKE_CASE )
return column
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Mapping:
a__ = self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE )
a__ = self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE )
a__ = self.recursive_tensorize(SCREAMING_SNAKE_CASE )
for column_name in batch:
a__ = self._consolidate(batch[column_name] )
return batch
| 194 |
from __future__ import annotations
import requests
def __a ( __UpperCAmelCase ):
a__ = f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(__UpperCAmelCase ).json()
def __a ( __UpperCAmelCase = 10 ):
a__ = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
a__ = requests.get(__UpperCAmelCase ).json()[:max_stories]
return [get_hackernews_story(__UpperCAmelCase ) for story_id in story_ids]
def __a ( __UpperCAmelCase = 10 ):
a__ = hackernews_top_stories(__UpperCAmelCase )
return "\n".join('''* [{title}]({url})'''.format(**__UpperCAmelCase ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 194 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __snake_case( A_ ):
_A = None
_A = None
_A = None
_A = None
class __snake_case( A_ ):
def __init__( self , A_=1 , A_=0 , A_=2 , A_=512 , A_="cls" , A_=False , A_=True , **A_ , ):
'''simple docstring'''
super().__init__(pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , **A_ )
_SCREAMING_SNAKE_CASE = project_dim
_SCREAMING_SNAKE_CASE = pooler_fn
_SCREAMING_SNAKE_CASE = learn_encoder
_SCREAMING_SNAKE_CASE = use_attention_mask
class __snake_case( A_ ):
_A = [r'''pooler''', r'''logit_scale''']
_A = [r'''position_ids''', r'''predictions.decoder.bias''']
_A = '''roberta'''
_A = RobertaSeriesConfig
def __init__( self , A_ ):
'''simple docstring'''
super().__init__(A_ )
_SCREAMING_SNAKE_CASE = XLMRobertaModel(A_ )
_SCREAMING_SNAKE_CASE = nn.Linear(config.hidden_size , config.project_dim )
_SCREAMING_SNAKE_CASE = getattr(A_ , '''has_pre_transformation''' , A_ )
if self.has_pre_transformation:
_SCREAMING_SNAKE_CASE = nn.Linear(config.hidden_size , config.project_dim )
_SCREAMING_SNAKE_CASE = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def A ( self , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict
_SCREAMING_SNAKE_CASE = self.base_model(
input_ids=A_ , attention_mask=A_ , token_type_ids=A_ , position_ids=A_ , head_mask=A_ , inputs_embeds=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , output_attentions=A_ , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=A_ , )
if self.has_pre_transformation:
_SCREAMING_SNAKE_CASE = outputs["""hidden_states"""][-2]
_SCREAMING_SNAKE_CASE = self.pre_LN(A_ )
_SCREAMING_SNAKE_CASE = self.transformation_pre(A_ )
return TransformationModelOutput(
projection_state=A_ , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
_SCREAMING_SNAKE_CASE = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=A_ , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 708 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __snake_case( __A ):
def __lt__( self , A_ ):
'''simple docstring'''
return self[-1] < other[-1]
def __eq__( self , A_ ):
'''simple docstring'''
return self[-1] == other[-1]
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = []
# sort into stacks
for element in collection:
_SCREAMING_SNAKE_CASE = Stack([element] )
_SCREAMING_SNAKE_CASE = bisect_left(UpperCamelCase__ , UpperCamelCase__ )
if i != len(UpperCamelCase__ ):
stacks[i].append(UpperCamelCase__ )
else:
stacks.append(UpperCamelCase__ )
# use a heap-based merge to merge stack efficiently
_SCREAMING_SNAKE_CASE = merge(*(reversed(UpperCamelCase__ ) for stack in stacks) )
return collection
if __name__ == "__main__":
lowerCamelCase : int = input("""Enter numbers separated by a comma:\n""").strip()
lowerCamelCase : Optional[Any] = [int(item) for item in user_input.split(""",""")]
print(patience_sort(unsorted))
| 168 | 0 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Optional[Any] = logging.get_logger(__name__)
__snake_case : List[Any] = {'vocab_file': 'spiece.model'}
__snake_case : int = {
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
__snake_case : List[str] = {
'AI-Sweden/gpt-sw3-126m': 2048,
'AI-Sweden/gpt-sw3-350m': 2048,
'AI-Sweden/gpt-sw3-1.6b': 2048,
'AI-Sweden/gpt-sw3-6.7b': 2048,
'AI-Sweden/gpt-sw3-20b': 2048,
}
class A ( a ):
__UpperCAmelCase : Any = VOCAB_FILES_NAMES
__UpperCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : int = ["""input_ids""", """attention_mask"""]
def __init__( self , snake_case_ , snake_case_=False , snake_case_=False , snake_case_=False , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_ = None , **snake_case_ , ) -> None:
_a = {} if sp_model_kwargs is None else sp_model_kwargs
_a = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
_a = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
_a = '''<|endoftext|>''' if eos_token is None else eos_token
_a = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
_a = unk_token if pad_token is None else pad_token
_a = eos_token if bos_token is None else bos_token
else:
_a = '''<pad>''' if pad_token is None else pad_token
_a = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=snake_case_ , remove_space=snake_case_ , keep_accents=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
_a = do_lower_case
_a = remove_space
_a = keep_accents
_a = vocab_file
_a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
# Used for whitespace normalization in input texts
# fmt : off
_a = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
_a = re.compile(
F'''[{''.join(map(snake_case_ , list(range(0 , 9 ) ) + list(range(1_1 , 3_2 ) ) + list(range(1_2_7 , 1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]''' )
def __getstate__( self ) -> Union[str, Any]:
_a = self.__dict__.copy()
_a = None
return state
def __setstate__( self , snake_case_ ) -> Any:
_a = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_a = {}
_a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def __lowerCAmelCase ( self ) -> int:
return len(self.sp_model )
def __lowerCAmelCase ( self , snake_case_ ) -> str:
_a = self.non_printing_characters_re.sub("" , snake_case_ )
# Normalize whitespaces
_a = ''''''.join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
_a = unicodedata.normalize("NFC" , snake_case_ )
return text
def __lowerCAmelCase ( self , snake_case_ , **snake_case_ ) -> List[str]:
_a = self.preprocess_text(snake_case_ )
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def __lowerCAmelCase ( self , snake_case_ ) -> int:
return self.sp_model.PieceToId(snake_case_ )
def __lowerCAmelCase ( self , snake_case_ ) -> str:
return self.sp_model.IdToPiece(snake_case_ )
@staticmethod
def __lowerCAmelCase ( snake_case_ ) -> str:
return out_string
def __lowerCAmelCase ( self , snake_case_ ) -> str:
_a = []
_a = ''''''
_a = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(snake_case_ ) + token
_a = True
_a = []
else:
current_sub_tokens.append(snake_case_ )
_a = False
out_string += self.sp_model.decode(snake_case_ )
return out_string
def __lowerCAmelCase ( self ) -> Dict[str, int]:
_a = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowerCAmelCase ( self , snake_case_ , snake_case_ = None ) -> Tuple[str]:
if not os.path.isdir(snake_case_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_a = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , "wb" ) as fi:
_a = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
def __lowerCAmelCase ( self , snake_case_ , snake_case_ = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
if isinstance(snake_case_ , snake_case_ ):
_a = self.preprocess_text(snake_case_ )
_a = self.sp_model.encode(snake_case_ )
else:
_a = [self.preprocess_text(snake_case_ ) for t in text]
_a = self.sp_model.encode(snake_case_ )
if return_tensors is True or return_tensors == "pt":
_a = torch.tensor(snake_case_ )
return token_ids
def __lowerCAmelCase ( self , snake_case_ ) -> str:
return self.sp_model.decode(snake_case_ )
def __lowerCAmelCase ( self , snake_case_ ) -> List[int]:
_a = [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
_a = (
F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(snake_case_ ) + F'''{self.bos_token}Bot:'''
)
return self.encode(text=snake_case_ )
| 131 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''speech_to_text_2'''
a__ =['''past_key_values''']
a__ ={'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , A=1_0_0_0_0 , A=6 , A=2_0_4_8 , A=4 , A=0.0 , A=True , A="relu" , A=2_5_6 , A=0.1 , A=0.0 , A=0.0 , A=0.02 , A=2 , A=True , A=1 , A=0 , A=2 , A=1_0_2_4 , **A , ) -> Optional[Any]:
_UpperCAmelCase : List[str] = vocab_size
_UpperCAmelCase : Union[str, Any] = d_model
_UpperCAmelCase : Dict = decoder_ffn_dim
_UpperCAmelCase : Dict = decoder_layers
_UpperCAmelCase : Optional[Any] = decoder_attention_heads
_UpperCAmelCase : int = dropout
_UpperCAmelCase : Any = attention_dropout
_UpperCAmelCase : Any = activation_dropout
_UpperCAmelCase : Union[str, Any] = activation_function
_UpperCAmelCase : List[str] = init_std
_UpperCAmelCase : Any = decoder_layerdrop
_UpperCAmelCase : Tuple = use_cache
_UpperCAmelCase : List[Any] = decoder_layers
_UpperCAmelCase : Dict = scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCAmelCase : Dict = max_target_positions
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , decoder_start_token_id=A , **A , )
| 506 | 0 |
import math
import qiskit
def __lowerCamelCase ( snake_case__ = 1 ,snake_case__ = 1 ,snake_case__ = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(snake_case__ ,snake_case__ )
or isinstance(snake_case__ ,snake_case__ )
or isinstance(snake_case__ ,snake_case__ )
):
raise TypeError("""inputs must be integers.""" )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError("""inputs must be positive.""" )
if (
(math.floor(snake_case__ ) != input_a)
or (math.floor(snake_case__ ) != input_a)
or (math.floor(snake_case__ ) != carry_in)
):
raise ValueError("""inputs must be exact integers.""" )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError("""inputs must be less or equal to 2.""" )
# build registers
_SCREAMING_SNAKE_CASE = qiskit.QuantumRegister(4 ,"""qr""" )
_SCREAMING_SNAKE_CASE = qiskit.ClassicalRegister(2 ,"""cr""" )
# list the entries
_SCREAMING_SNAKE_CASE = [input_a, input_a, carry_in]
_SCREAMING_SNAKE_CASE = qiskit.QuantumCircuit(snake_case__ ,snake_case__ )
for i in range(0 ,3 ):
if entry[i] == 2:
quantum_circuit.h(snake_case__ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(snake_case__ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(snake_case__ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 ,1 ,3 ) # ccx = toffoli gate
quantum_circuit.cx(0 ,1 )
quantum_circuit.ccx(1 ,2 ,3 )
quantum_circuit.cx(1 ,2 )
quantum_circuit.cx(0 ,1 )
quantum_circuit.measure([2, 3] ,snake_case__ ) # measure the last two qbits
_SCREAMING_SNAKE_CASE = qiskit.Aer.get_backend("""aer_simulator""" )
_SCREAMING_SNAKE_CASE = qiskit.execute(snake_case__ ,snake_case__ ,shots=10_00 )
return job.result().get_counts(snake_case__ )
if __name__ == "__main__":
print(f"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 569 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __UpperCAmelCase (TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
def __init__( self: str , UpperCAmelCase_: Union[str, Any]=None , **UpperCAmelCase_: Dict ):
'''simple docstring'''
super().__init__(features=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = torch_tensor_kwargs
import torch # noqa import torch at initialization
def UpperCamelCase ( self: Optional[Any] , UpperCAmelCase_: Optional[int] ):
'''simple docstring'''
import torch
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and column:
if all(
isinstance(UpperCAmelCase_ , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(UpperCAmelCase_ )
return column
def UpperCamelCase ( self: int , UpperCAmelCase_: Optional[int] ):
'''simple docstring'''
import torch
if isinstance(UpperCAmelCase_ , (str, bytes, type(UpperCAmelCase_ )) ):
return value
elif isinstance(UpperCAmelCase_ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_SCREAMING_SNAKE_CASE = {}
if isinstance(UpperCAmelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
_SCREAMING_SNAKE_CASE = {"""dtype""": torch.intaa}
elif isinstance(UpperCAmelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_SCREAMING_SNAKE_CASE = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(UpperCAmelCase_ , PIL.Image.Image ):
_SCREAMING_SNAKE_CASE = np.asarray(UpperCAmelCase_ )
return torch.tensor(UpperCAmelCase_ , **{**default_dtype, **self.torch_tensor_kwargs} )
def UpperCamelCase ( self: Tuple , UpperCAmelCase_: str ):
'''simple docstring'''
import torch
# support for torch, tf, jax etc.
if hasattr(UpperCAmelCase_ , """__array__""" ) and not isinstance(UpperCAmelCase_ , torch.Tensor ):
_SCREAMING_SNAKE_CASE = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(UpperCAmelCase_ , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(UpperCAmelCase_ ) for substruct in data_struct] )
elif isinstance(UpperCAmelCase_ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(UpperCAmelCase_ ) for substruct in data_struct] )
return self._tensorize(UpperCAmelCase_ )
def UpperCamelCase ( self: Dict , UpperCAmelCase_: dict ):
'''simple docstring'''
return map_nested(self._recursive_tensorize , UpperCAmelCase_ , map_list=UpperCAmelCase_ )
def UpperCamelCase ( self: str , UpperCAmelCase_: pa.Table ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_row(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_row(UpperCAmelCase_ )
return self.recursive_tensorize(UpperCAmelCase_ )
def UpperCamelCase ( self: Any , UpperCAmelCase_: pa.Table ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_column(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_column(UpperCAmelCase_ , pa_table.column_names[0] )
_SCREAMING_SNAKE_CASE = self.recursive_tensorize(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self._consolidate(UpperCAmelCase_ )
return column
def UpperCamelCase ( self: str , UpperCAmelCase_: pa.Table ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_batch(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_batch(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.recursive_tensorize(UpperCAmelCase_ )
for column_name in batch:
_SCREAMING_SNAKE_CASE = self._consolidate(batch[column_name] )
return batch
| 569 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( UpperCAmelCase_ ,unittest.TestCase ):
SCREAMING_SNAKE_CASE__ : Any = LongformerTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
SCREAMING_SNAKE_CASE__ : Optional[int] = LongformerTokenizerFast
SCREAMING_SNAKE_CASE__ : Dict = True
def snake_case_ ( self ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__SCREAMING_SNAKE_CASE: str = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
__SCREAMING_SNAKE_CASE: Optional[int] = dict(zip(__a , range(len(__a ) ) ) )
__SCREAMING_SNAKE_CASE: Optional[Any] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__SCREAMING_SNAKE_CASE: List[str] = {'''unk_token''': '''<unk>'''}
__SCREAMING_SNAKE_CASE: Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__SCREAMING_SNAKE_CASE: List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__a ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__a ) )
def snake_case_ ( self , **_lowerCAmelCase ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__a )
def snake_case_ ( self , **_lowerCAmelCase ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__a )
def snake_case_ ( self , _lowerCAmelCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: str = '''lower newer'''
__SCREAMING_SNAKE_CASE: Tuple = '''lower newer'''
return input_text, output_text
def snake_case_ ( self ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Dict = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
__SCREAMING_SNAKE_CASE: List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE: Optional[Any] = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
__SCREAMING_SNAKE_CASE: Tuple = tokenizer.tokenize(__a ) # , add_prefix_space=True)
self.assertListEqual(__a , __a )
__SCREAMING_SNAKE_CASE: Tuple = tokens + [tokenizer.unk_token]
__SCREAMING_SNAKE_CASE: List[str] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def snake_case_ ( self ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Tuple = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('''Hello world!''' , add_special_tokens=__a ) , [0, 31414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode('''Hello world! cécé herlolip 418''' , add_special_tokens=__a ) , [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2] , )
@slow
def snake_case_ ( self ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Tuple = self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096''' )
__SCREAMING_SNAKE_CASE: int = tokenizer.encode('''sequence builders''' , add_special_tokens=__a )
__SCREAMING_SNAKE_CASE: str = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__a )
__SCREAMING_SNAKE_CASE: Dict = tokenizer.encode(
'''sequence builders''' , add_special_tokens=__a , add_prefix_space=__a )
__SCREAMING_SNAKE_CASE: Dict = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=__a , add_prefix_space=__a )
__SCREAMING_SNAKE_CASE: Optional[int] = tokenizer.build_inputs_with_special_tokens(__a )
__SCREAMING_SNAKE_CASE: str = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def snake_case_ ( self ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE: Optional[int] = '''Encode this sequence.'''
__SCREAMING_SNAKE_CASE: List[str] = tokenizer.byte_encoder[''' '''.encode('''utf-8''' )[0]]
# Testing encoder arguments
__SCREAMING_SNAKE_CASE: Dict = tokenizer.encode(__a , add_special_tokens=__a , add_prefix_space=__a )
__SCREAMING_SNAKE_CASE: Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(__a , __a )
__SCREAMING_SNAKE_CASE: Union[str, Any] = tokenizer.encode(__a , add_special_tokens=__a , add_prefix_space=__a )
__SCREAMING_SNAKE_CASE: Tuple = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(__a , __a )
tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} )
__SCREAMING_SNAKE_CASE: Optional[Any] = tokenizer.encode(__a , add_special_tokens=__a )
__SCREAMING_SNAKE_CASE: List[str] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(__a , __a )
# Testing spaces after special tokens
__SCREAMING_SNAKE_CASE: Optional[int] = '''<mask>'''
tokenizer.add_special_tokens(
{'''mask_token''': AddedToken(__a , lstrip=__a , rstrip=__a )} ) # mask token has a left space
__SCREAMING_SNAKE_CASE: Dict = tokenizer.convert_tokens_to_ids(__a )
__SCREAMING_SNAKE_CASE: Optional[int] = '''Encode <mask> sequence'''
__SCREAMING_SNAKE_CASE: Tuple = '''Encode <mask>sequence'''
__SCREAMING_SNAKE_CASE: int = tokenizer.encode(__a )
__SCREAMING_SNAKE_CASE: Tuple = encoded.index(__a )
__SCREAMING_SNAKE_CASE: Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(__a , __a )
__SCREAMING_SNAKE_CASE: List[str] = tokenizer.encode(__a )
__SCREAMING_SNAKE_CASE: Optional[Any] = encoded.index(__a )
__SCREAMING_SNAKE_CASE: Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(__a , __a )
def snake_case_ ( self ):
"""simple docstring"""
pass
def snake_case_ ( self ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE: Dict = self.rust_tokenizer_class.from_pretrained(__a , **__a )
__SCREAMING_SNAKE_CASE: Dict = self.tokenizer_class.from_pretrained(__a , **__a )
__SCREAMING_SNAKE_CASE: Any = '''A, <mask> AllenNLP sentence.'''
__SCREAMING_SNAKE_CASE: Optional[Any] = tokenizer_r.encode_plus(__a , add_special_tokens=__a , return_token_type_ids=__a )
__SCREAMING_SNAKE_CASE: Any = tokenizer_p.encode_plus(__a , add_special_tokens=__a , return_token_type_ids=__a )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['''token_type_ids'''] ) , sum(tokens_p['''token_type_ids'''] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ) , sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ) , )
__SCREAMING_SNAKE_CASE: List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
__SCREAMING_SNAKE_CASE: Any = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(
__a , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
__a , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
def snake_case_ ( self ):
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
__SCREAMING_SNAKE_CASE: Optional[Any] = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=__a , add_prefix_space=__a , trim_offsets=__a )
__SCREAMING_SNAKE_CASE: List[str] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
__SCREAMING_SNAKE_CASE: Union[str, Any] = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['''add_prefix_space'''] , __a )
self.assertEqual(post_processor_state['''add_prefix_space'''] , __a )
self.assertEqual(post_processor_state['''trim_offsets'''] , __a )
def snake_case_ ( self ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__SCREAMING_SNAKE_CASE: Optional[int] = '''hello''' # `hello` is a token in the vocabulary of `pretrained_name`
__SCREAMING_SNAKE_CASE: Dict = f"""{text_of_1_token} {text_of_1_token}"""
__SCREAMING_SNAKE_CASE: Dict = self.rust_tokenizer_class.from_pretrained(
__a , use_fast=__a , add_prefix_space=__a , trim_offsets=__a )
__SCREAMING_SNAKE_CASE: Tuple = tokenizer_r(__a , return_offsets_mapping=__a , add_special_tokens=__a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__a ) + 1, len(__a ) + 1 + len(__a )) , )
__SCREAMING_SNAKE_CASE: Optional[int] = self.rust_tokenizer_class.from_pretrained(
__a , use_fast=__a , add_prefix_space=__a , trim_offsets=__a )
__SCREAMING_SNAKE_CASE: int = tokenizer_r(__a , return_offsets_mapping=__a , add_special_tokens=__a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__a ) + 1, len(__a ) + 1 + len(__a )) , )
__SCREAMING_SNAKE_CASE: Any = self.rust_tokenizer_class.from_pretrained(
__a , use_fast=__a , add_prefix_space=__a , trim_offsets=__a )
__SCREAMING_SNAKE_CASE: Dict = tokenizer_r(__a , return_offsets_mapping=__a , add_special_tokens=__a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__a ), len(__a ) + 1 + len(__a )) , )
__SCREAMING_SNAKE_CASE: List[Any] = self.rust_tokenizer_class.from_pretrained(
__a , use_fast=__a , add_prefix_space=__a , trim_offsets=__a )
__SCREAMING_SNAKE_CASE: Optional[Any] = tokenizer_r(__a , return_offsets_mapping=__a , add_special_tokens=__a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__a ), len(__a ) + 1 + len(__a )) , )
__SCREAMING_SNAKE_CASE: List[Any] = f""" {text}"""
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
__SCREAMING_SNAKE_CASE: List[Any] = self.rust_tokenizer_class.from_pretrained(
__a , use_fast=__a , add_prefix_space=__a , trim_offsets=__a )
__SCREAMING_SNAKE_CASE: str = tokenizer_r(__a , return_offsets_mapping=__a , add_special_tokens=__a )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__a ) + 1, 1 + len(__a ) + 1 + len(__a )) , )
__SCREAMING_SNAKE_CASE: Optional[Any] = self.rust_tokenizer_class.from_pretrained(
__a , use_fast=__a , add_prefix_space=__a , trim_offsets=__a )
__SCREAMING_SNAKE_CASE: Optional[Any] = tokenizer_r(__a , return_offsets_mapping=__a , add_special_tokens=__a )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__a ), 1 + len(__a ) + 1 + len(__a )) , )
__SCREAMING_SNAKE_CASE: Optional[Any] = self.rust_tokenizer_class.from_pretrained(
__a , use_fast=__a , add_prefix_space=__a , trim_offsets=__a )
__SCREAMING_SNAKE_CASE: Dict = tokenizer_r(__a , return_offsets_mapping=__a , add_special_tokens=__a )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__a ), 1 + len(__a ) + 1 + len(__a )) , )
| 202 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(UpperCAmelCase__ )
if n > 1:
factors.append(UpperCAmelCase__ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 623 | 0 |
import argparse
import os
import re
_UpperCamelCase = "src/diffusers"
# Pattern that looks at the indentation in a line.
_UpperCamelCase = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_UpperCamelCase = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_UpperCamelCase = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
_UpperCamelCase = re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_UpperCamelCase = re.compile(r"\[([^\]]+)\]")
def _lowercase ( lowercase__ ):
__lowerCAmelCase : Union[str, Any] = _re_indent.search(lowercase__ )
return "" if search is None else search.groups()[0]
def _lowercase ( lowercase__ , lowercase__="" , lowercase__=None , lowercase__=None ):
__lowerCAmelCase : Any = 0
__lowerCAmelCase : Tuple = code.split('''\n''' )
if start_prompt is not None:
while not lines[index].startswith(lowercase__ ):
index += 1
__lowerCAmelCase : Any = ['''\n'''.join(lines[:index] )]
else:
__lowerCAmelCase : List[str] = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
__lowerCAmelCase : Union[str, Any] = [lines[index]]
index += 1
while index < len(lowercase__ ) and (end_prompt is None or not lines[index].startswith(lowercase__ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(lowercase__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ''' ''' ):
current_block.append(lines[index] )
blocks.append('''\n'''.join(lowercase__ ) )
if index < len(lowercase__ ) - 1:
__lowerCAmelCase : Union[str, Any] = [lines[index + 1]]
index += 1
else:
__lowerCAmelCase : Dict = []
else:
blocks.append('''\n'''.join(lowercase__ ) )
__lowerCAmelCase : Dict = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(lowercase__ ) > 0:
blocks.append('''\n'''.join(lowercase__ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(lowercase__ ):
blocks.append('''\n'''.join(lines[index:] ) )
return blocks
def _lowercase ( lowercase__ ):
def _inner(lowercase__ ):
return key(lowercase__ ).lower().replace('''_''' , '''''' )
return _inner
def _lowercase ( lowercase__ , lowercase__=None ):
# If no key is provided, we use a noop.
def noop(lowercase__ ):
return x
if key is None:
__lowerCAmelCase : List[str] = noop
# Constants are all uppercase, they go first.
__lowerCAmelCase : Union[str, Any] = [obj for obj in objects if key(lowercase__ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
__lowerCAmelCase : Any = [obj for obj in objects if key(lowercase__ )[0].isupper() and not key(lowercase__ ).isupper()]
# Functions begin with a lowercase, they go last.
__lowerCAmelCase : Optional[int] = [obj for obj in objects if not key(lowercase__ )[0].isupper()]
__lowerCAmelCase : Tuple = ignore_underscore(lowercase__ )
return sorted(lowercase__ , key=lowercase__ ) + sorted(lowercase__ , key=lowercase__ ) + sorted(lowercase__ , key=lowercase__ )
def _lowercase ( lowercase__ ):
# This inner function sort imports between [ ].
def _replace(lowercase__ ):
__lowerCAmelCase : int = match.groups()[0]
if "," not in imports:
return f"""[{imports}]"""
__lowerCAmelCase : Any = [part.strip().replace('''"''' , '''''' ) for part in imports.split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
__lowerCAmelCase : Tuple = keys[:-1]
return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(lowercase__ )] ) + "]"
__lowerCAmelCase : str = import_statement.split('''\n''' )
if len(lowercase__ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
__lowerCAmelCase : Any = 2 if lines[1].strip() == '''[''' else 1
__lowerCAmelCase : List[Any] = [(i, _re_strip_line.search(lowercase__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
__lowerCAmelCase : List[Any] = sort_objects(lowercase__ , key=lambda lowercase__ : x[1] )
__lowerCAmelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(lowercase__ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
__lowerCAmelCase : Optional[Any] = _re_bracket_content.sub(_replace , lines[1] )
else:
__lowerCAmelCase : Any = [part.strip().replace('''"''' , '''''' ) for part in lines[1].split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
__lowerCAmelCase : str = keys[:-1]
__lowerCAmelCase : int = get_indent(lines[1] ) + ''', '''.join([f"""\"{k}\"""" for k in sort_objects(lowercase__ )] )
return "\n".join(lowercase__ )
else:
# Finally we have to deal with imports fitting on one line
__lowerCAmelCase : Any = _re_bracket_content.sub(_replace , lowercase__ )
return import_statement
def _lowercase ( lowercase__ , lowercase__=True ):
with open(lowercase__ , '''r''' ) as f:
__lowerCAmelCase : Any = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
__lowerCAmelCase : List[Any] = split_code_in_indented_blocks(
lowercase__ , start_prompt='''_import_structure = {''' , end_prompt='''if TYPE_CHECKING:''' )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(lowercase__ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
__lowerCAmelCase : int = main_blocks[block_idx]
__lowerCAmelCase : Optional[int] = block.split('''\n''' )
# Get to the start of the imports.
__lowerCAmelCase : List[str] = 0
while line_idx < len(lowercase__ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
__lowerCAmelCase : str = len(lowercase__ )
else:
line_idx += 1
if line_idx >= len(lowercase__ ):
continue
# Ignore beginning and last line: they don't contain anything.
__lowerCAmelCase : List[Any] = '''\n'''.join(block_lines[line_idx:-1] )
__lowerCAmelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
__lowerCAmelCase : Optional[Any] = split_code_in_indented_blocks(lowercase__ , indent_level=lowercase__ )
# We have two categories of import key: list or _import_structure[key].append/extend
__lowerCAmelCase : List[str] = _re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
__lowerCAmelCase : List[str] = [(pattern.search(lowercase__ ).groups()[0] if pattern.search(lowercase__ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
__lowerCAmelCase : Optional[Any] = [(i, key) for i, key in enumerate(lowercase__ ) if key is not None]
__lowerCAmelCase : Union[str, Any] = [x[0] for x in sorted(lowercase__ , key=lambda lowercase__ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
__lowerCAmelCase : int = 0
__lowerCAmelCase : Optional[int] = []
for i in range(len(lowercase__ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
__lowerCAmelCase : Tuple = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(lowercase__ )
count += 1
# And we put our main block back together with its first and last line.
__lowerCAmelCase : List[str] = '''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(lowercase__ ):
if check_only:
return True
else:
print(f"""Overwriting {file}.""" )
with open(lowercase__ , '''w''' ) as f:
f.write('''\n'''.join(lowercase__ ) )
def _lowercase ( lowercase__=True ):
__lowerCAmelCase : Dict = []
for root, _, files in os.walk(lowercase__ ):
if "__init__.py" in files:
__lowerCAmelCase : int = sort_imports(os.path.join(lowercase__ , '''__init__.py''' ) , check_only=lowercase__ )
if result:
__lowerCAmelCase : int = [os.path.join(lowercase__ , '''__init__.py''' )]
if len(lowercase__ ) > 0:
raise ValueError(f"""Would overwrite {len(lowercase__ )} files, run `make style`.""" )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
_UpperCamelCase = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 583 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeForMaskedLM",
"LukeModel",
"LukePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 583 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
_snake_case = '0.12' # assumed parallelism: 8
@require_flax
@is_staging_test
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __lowercase( cls ) -> int:
__UpperCamelCase = TOKEN
HfFolder.save_token(_SCREAMING_SNAKE_CASE )
@classmethod
def __lowercase( cls ) -> Union[str, Any]:
try:
delete_repo(token=cls._token , repo_id='test-model-flax' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-model-flax-org' )
except HTTPError:
pass
def __lowercase( self ) -> Optional[Any]:
__UpperCamelCase = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
__UpperCamelCase = FlaxBertModel(_SCREAMING_SNAKE_CASE )
model.push_to_hub('test-model-flax' , use_auth_token=self._token )
__UpperCamelCase = FlaxBertModel.from_pretrained(f"""{USER}/test-model-flax""" )
__UpperCamelCase = flatten_dict(unfreeze(model.params ) )
__UpperCamelCase = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__UpperCamelCase = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 , msg=f"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='test-model-flax' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_SCREAMING_SNAKE_CASE , repo_id='test-model-flax' , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
__UpperCamelCase = FlaxBertModel.from_pretrained(f"""{USER}/test-model-flax""" )
__UpperCamelCase = flatten_dict(unfreeze(model.params ) )
__UpperCamelCase = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__UpperCamelCase = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 , msg=f"""{key} not identical""" )
def __lowercase( self ) -> List[Any]:
__UpperCamelCase = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
__UpperCamelCase = FlaxBertModel(_SCREAMING_SNAKE_CASE )
model.push_to_hub('valid_org/test-model-flax-org' , use_auth_token=self._token )
__UpperCamelCase = FlaxBertModel.from_pretrained('valid_org/test-model-flax-org' )
__UpperCamelCase = flatten_dict(unfreeze(model.params ) )
__UpperCamelCase = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__UpperCamelCase = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 , msg=f"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-model-flax-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
_SCREAMING_SNAKE_CASE , repo_id='valid_org/test-model-flax-org' , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
__UpperCamelCase = FlaxBertModel.from_pretrained('valid_org/test-model-flax-org' )
__UpperCamelCase = flatten_dict(unfreeze(model.params ) )
__UpperCamelCase = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__UpperCamelCase = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 , msg=f"""{key} not identical""" )
def _a ( __lowercase , __lowercase ) -> str:
"""simple docstring"""
__UpperCamelCase = True
__UpperCamelCase = flatten_dict(modela.params )
__UpperCamelCase = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
__UpperCamelCase = False
return models_are_equal
@require_flax
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase( self ) -> List[Any]:
__UpperCamelCase = BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only' )
__UpperCamelCase = FlaxBertModel(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = 'bert'
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
__UpperCamelCase = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE )
self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def __lowercase( self ) -> Union[str, Any]:
__UpperCamelCase = BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only' )
__UpperCamelCase = FlaxBertModel(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = 'bert'
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , max_shard_size='10KB' )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
__UpperCamelCase = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE )
self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def __lowercase( self ) -> Dict:
__UpperCamelCase = 'bert'
__UpperCamelCase = 'hf-internal-testing/tiny-random-bert-subfolder'
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
__UpperCamelCase = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def __lowercase( self ) -> List[str]:
__UpperCamelCase = 'bert'
__UpperCamelCase = 'hf-internal-testing/tiny-random-bert-sharded-subfolder'
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
__UpperCamelCase = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
| 383 |
from ...configuration_utils import PretrainedConfig
_snake_case = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
),
'google/tapas-base-finetuned-wikisql-supervised': (
'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
),
'google/tapas-base-finetuned-tabfact': (
'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
),
}
class lowerCAmelCase_ ( _lowercase ):
"""simple docstring"""
UpperCAmelCase__ = "tapas"
def __init__( self , _SCREAMING_SNAKE_CASE=30_522 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3_072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=1_024 , _SCREAMING_SNAKE_CASE=[3, 256, 256, 2, 256, 256, 10] , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1e-1_2 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=1_0.0 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="ratio" , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
__UpperCamelCase = vocab_size
__UpperCamelCase = hidden_size
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = hidden_act
__UpperCamelCase = intermediate_size
__UpperCamelCase = hidden_dropout_prob
__UpperCamelCase = attention_probs_dropout_prob
__UpperCamelCase = max_position_embeddings
__UpperCamelCase = type_vocab_sizes
__UpperCamelCase = initializer_range
__UpperCamelCase = layer_norm_eps
# Fine-tuning task hyperparameters
__UpperCamelCase = positive_label_weight
__UpperCamelCase = num_aggregation_labels
__UpperCamelCase = aggregation_loss_weight
__UpperCamelCase = use_answer_as_supervision
__UpperCamelCase = answer_loss_importance
__UpperCamelCase = use_normalized_answer_loss
__UpperCamelCase = huber_loss_delta
__UpperCamelCase = temperature
__UpperCamelCase = aggregation_temperature
__UpperCamelCase = use_gumbel_for_cells
__UpperCamelCase = use_gumbel_for_aggregation
__UpperCamelCase = average_approximation_function
__UpperCamelCase = cell_selection_preference
__UpperCamelCase = answer_loss_cutoff
__UpperCamelCase = max_num_rows
__UpperCamelCase = max_num_columns
__UpperCamelCase = average_logits_per_cell
__UpperCamelCase = select_one_column
__UpperCamelCase = allow_empty_column_selection
__UpperCamelCase = init_cell_selection_weights_to_zero
__UpperCamelCase = reset_position_index_per_cell
__UpperCamelCase = disable_per_token_loss
# Aggregation hyperparameters
__UpperCamelCase = aggregation_labels
__UpperCamelCase = no_aggregation_label_index
if isinstance(self.aggregation_labels , _SCREAMING_SNAKE_CASE ):
__UpperCamelCase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in aggregation_labels.items()}
| 383 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def A (__A : Tuple , __A : List[Any]=None ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = None
if token is not None:
UpperCAmelCase_ = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}
UpperCAmelCase_ = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
UpperCAmelCase_ = requests.get(__A , headers=__A ).json()
UpperCAmelCase_ = {}
try:
job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
UpperCAmelCase_ = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(__A ):
UpperCAmelCase_ = requests.get(url + F"""&page={i + 2}""" , headers=__A ).json()
job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
return job_links
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def A (__A : Union[str, Any] , __A : Optional[Any]=None ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = None
if token is not None:
UpperCAmelCase_ = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}
UpperCAmelCase_ = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
UpperCAmelCase_ = requests.get(__A , headers=__A ).json()
UpperCAmelCase_ = {}
try:
artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']} )
UpperCAmelCase_ = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(__A ):
UpperCAmelCase_ = requests.get(url + F"""&page={i + 2}""" , headers=__A ).json()
artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']} )
return artifacts
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def A (__A : Any , __A : Tuple , __A : Optional[int] , __A : List[str] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = None
if token is not None:
UpperCAmelCase_ = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}
UpperCAmelCase_ = requests.get(__A , headers=__A , allow_redirects=__A )
UpperCAmelCase_ = result.headers['''Location''']
UpperCAmelCase_ = requests.get(__A , allow_redirects=__A )
UpperCAmelCase_ = os.path.join(__A , F"""{artifact_name}.zip""" )
with open(__A , '''wb''' ) as fp:
fp.write(response.content )
def A (__A : Union[str, Any] , __A : Optional[int]=None ) -> int:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = None
with zipfile.ZipFile(__A ) as z:
for filename in z.namelist():
if not os.path.isdir(__A ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(__A ) as f:
for line in f:
UpperCAmelCase_ = line.decode('''UTF-8''' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
UpperCAmelCase_ = line[: line.index(''': ''' )]
UpperCAmelCase_ = line[line.index(''': ''' ) + len(''': ''' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('''FAILED ''' ):
# `test` is the test method that failed
UpperCAmelCase_ = line[len('''FAILED ''' ) :]
failed_tests.append(__A )
elif filename == "job_name.txt":
UpperCAmelCase_ = line
if len(__A ) != len(__A ):
raise ValueError(
F"""`errors` and `failed_tests` should have the same number of elements. Got {len(__A )} for `errors` """
F"""and {len(__A )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
''' problem.''' )
UpperCAmelCase_ = None
if job_name and job_links:
UpperCAmelCase_ = job_links.get(__A , __A )
# A list with elements of the form (line of error, error, failed test)
UpperCAmelCase_ = [x + [y] + [job_link] for x, y in zip(__A , __A )]
return result
def A (__A : List[str] , __A : Any=None ) -> int:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = [os.path.join(__A , __A ) for p in os.listdir(__A ) if p.endswith('''.zip''' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(__A , job_links=__A ) )
return errors
def A (__A : Tuple , __A : Dict=None ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = Counter()
counter.update([x[1] for x in logs] )
UpperCAmelCase_ = counter.most_common()
UpperCAmelCase_ = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
UpperCAmelCase_ = {'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}
UpperCAmelCase_ = dict(sorted(r.items() , key=lambda __A : item[1]["count"] , reverse=__A ) )
return r
def A (__A : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = test.split('''::''' )[0]
if test.startswith('''tests/models/''' ):
UpperCAmelCase_ = test.split('''/''' )[2]
else:
UpperCAmelCase_ = None
return test
def A (__A : str , __A : int=None ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = [(x[0], x[1], get_model(x[2] )) for x in logs]
UpperCAmelCase_ = [x for x in logs if x[2] is not None]
UpperCAmelCase_ = {x[2] for x in logs}
UpperCAmelCase_ = {}
for test in tests:
UpperCAmelCase_ = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
UpperCAmelCase_ = counter.most_common()
UpperCAmelCase_ = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
UpperCAmelCase_ = sum(error_counts.values() )
if n_errors > 0:
UpperCAmelCase_ = {'''count''': n_errors, '''errors''': error_counts}
UpperCAmelCase_ = dict(sorted(r.items() , key=lambda __A : item[1]["count"] , reverse=__A ) )
return r
def A (__A : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = '''| no. | error | status |'''
UpperCAmelCase_ = '''|-:|:-|:-|'''
UpperCAmelCase_ = [header, sep]
for error in reduced_by_error:
UpperCAmelCase_ = reduced_by_error[error]['''count''']
UpperCAmelCase_ = F"""| {count} | {error[:100]} | |"""
lines.append(__A )
return "\n".join(__A )
def A (__A : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = '''| model | no. of errors | major error | count |'''
UpperCAmelCase_ = '''|-:|-:|-:|-:|'''
UpperCAmelCase_ = [header, sep]
for model in reduced_by_model:
UpperCAmelCase_ = reduced_by_model[model]['''count''']
UpperCAmelCase_ , UpperCAmelCase_ = list(reduced_by_model[model]['''errors'''].items() )[0]
UpperCAmelCase_ = F"""| {model} | {count} | {error[:60]} | {_count} |"""
lines.append(__A )
return "\n".join(__A )
if __name__ == "__main__":
snake_case_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Where to store the downloaded artifacts and other result files.",
)
parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
snake_case_ : Union[str, Any] = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
snake_case_ : Dict = get_job_links(args.workflow_run_id, token=args.token)
snake_case_ : Dict = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
snake_case_ : List[Any] = k.find(" / ")
snake_case_ : List[str] = k[index + len(" / ") :]
snake_case_ : Optional[int] = v
with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
snake_case_ : Optional[int] = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
snake_case_ : Optional[Any] = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
snake_case_ : str = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
snake_case_ : Dict = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
snake_case_ : str = reduce_by_error(errors)
snake_case_ : Optional[Any] = reduce_by_model(errors)
snake_case_ : int = make_github_table(reduced_by_error)
snake_case_ : Optional[int] = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
| 715 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : List[str] = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
snake_case_ : Optional[int] = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
snake_case_ : Tuple = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class __snake_case ( a ):
UpperCAmelCase__ : List[str] = '''whisper'''
UpperCAmelCase__ : Optional[int] = ['''past_key_values''']
UpperCAmelCase__ : Any = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : List[str] , _snake_case : Dict=51865 , _snake_case : int=80 , _snake_case : Optional[int]=6 , _snake_case : Optional[int]=4 , _snake_case : Tuple=6 , _snake_case : List[Any]=4 , _snake_case : Optional[int]=1536 , _snake_case : List[Any]=1536 , _snake_case : Tuple=0.0 , _snake_case : Union[str, Any]=0.0 , _snake_case : Union[str, Any]=50257 , _snake_case : Optional[Any]=True , _snake_case : str=True , _snake_case : Dict="gelu" , _snake_case : List[str]=256 , _snake_case : List[str]=0.0 , _snake_case : Any=0.0 , _snake_case : Optional[Any]=0.0 , _snake_case : Any=0.0_2 , _snake_case : Any=False , _snake_case : Optional[int]=1500 , _snake_case : Dict=448 , _snake_case : List[Any]=50256 , _snake_case : Optional[int]=50256 , _snake_case : str=50256 , _snake_case : Tuple=None , _snake_case : Optional[int]=[220, 50256] , _snake_case : List[str]=False , _snake_case : Optional[Any]=256 , _snake_case : Optional[int]=False , _snake_case : str=0.0_5 , _snake_case : Tuple=10 , _snake_case : List[Any]=2 , _snake_case : Dict=0.0 , _snake_case : List[Any]=10 , _snake_case : str=0 , _snake_case : Tuple=7 , **_snake_case : Optional[int] , ):
"""simple docstring"""
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = num_mel_bins
UpperCAmelCase_ = d_model
UpperCAmelCase_ = encoder_layers
UpperCAmelCase_ = encoder_attention_heads
UpperCAmelCase_ = decoder_layers
UpperCAmelCase_ = decoder_attention_heads
UpperCAmelCase_ = decoder_ffn_dim
UpperCAmelCase_ = encoder_ffn_dim
UpperCAmelCase_ = dropout
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = activation_dropout
UpperCAmelCase_ = activation_function
UpperCAmelCase_ = init_std
UpperCAmelCase_ = encoder_layerdrop
UpperCAmelCase_ = decoder_layerdrop
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = encoder_layers
UpperCAmelCase_ = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase_ = max_source_positions
UpperCAmelCase_ = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase_ = classifier_proj_size
UpperCAmelCase_ = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase_ = apply_spec_augment
UpperCAmelCase_ = mask_time_prob
UpperCAmelCase_ = mask_time_length
UpperCAmelCase_ = mask_time_min_masks
UpperCAmelCase_ = mask_feature_prob
UpperCAmelCase_ = mask_feature_length
UpperCAmelCase_ = mask_feature_min_masks
UpperCAmelCase_ = median_filter_width
super().__init__(
pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , is_encoder_decoder=_snake_case , decoder_start_token_id=_snake_case , suppress_tokens=_snake_case , begin_suppress_tokens=_snake_case , **_snake_case , )
class __snake_case ( a ):
@property
def lowerCamelCase ( self : Optional[Any]):
"""simple docstring"""
UpperCAmelCase_ = OrderedDict(
[
('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
])
if self.use_past:
UpperCAmelCase_ = {0: '''batch'''}
else:
UpperCAmelCase_ = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(_snake_case , direction='''inputs''')
return common_inputs
def lowerCamelCase ( self : List[str] , _snake_case : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , _snake_case : int = -1 , _snake_case : int = -1 , _snake_case : bool = False , _snake_case : Optional["TensorType"] = None , _snake_case : int = 22050 , _snake_case : float = 5.0 , _snake_case : int = 220 , ):
"""simple docstring"""
UpperCAmelCase_ = OrderedDict()
UpperCAmelCase_ = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=_snake_case , framework=_snake_case , sampling_rate=_snake_case , time_duration=_snake_case , frequency=_snake_case , )
UpperCAmelCase_ = encoder_inputs['''input_features'''].shape[2]
UpperCAmelCase_ = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase_ = super().generate_dummy_inputs(
preprocessor.tokenizer , _snake_case , _snake_case , _snake_case , _snake_case)
UpperCAmelCase_ = encoder_inputs.pop('''input_features''')
UpperCAmelCase_ = decoder_inputs.pop('''decoder_input_ids''')
if "past_key_values" in decoder_inputs:
UpperCAmelCase_ = decoder_inputs.pop('''past_key_values''')
return dummy_inputs
@property
def lowerCamelCase ( self : Dict):
"""simple docstring"""
return 1e-3
| 169 | 0 |
'''simple docstring'''
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any] ) ->Optional[Any]:
UpperCAmelCase_ = name
UpperCAmelCase_ = value
UpperCAmelCase_ = weight
def __repr__( self : List[Any] ) ->Tuple:
return f"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def lowerCAmelCase__ ( self : Union[str, Any] ) ->List[str]:
return self.value
def lowerCAmelCase__ ( self : Dict ) ->Tuple:
return self.name
def lowerCAmelCase__ ( self : Any ) ->Optional[Any]:
return self.weight
def lowerCAmelCase__ ( self : str ) ->Dict:
return self.value / self.weight
def __lowerCamelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : int , _UpperCamelCase : int ):
'''simple docstring'''
UpperCAmelCase_ = []
for i in range(len(UpperCAmelCase__ ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def __lowerCamelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Dict , _UpperCamelCase : str ):
'''simple docstring'''
UpperCAmelCase_ = sorted(UpperCAmelCase__ , key=UpperCAmelCase__ , reverse=UpperCAmelCase__ )
UpperCAmelCase_ = []
UpperCAmelCase_ = 0.0, 0.0
for i in range(len(UpperCAmelCase__ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __lowerCamelCase ( ):
'''simple docstring'''
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 390 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
A_ : Union[str, Any] = datasets.utils.logging.get_logger(__name__)
A_ : Optional[Any] = ['names', 'prefix']
A_ : List[str] = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
A_ : List[Any] = ['encoding_errors', 'on_bad_lines']
A_ : Optional[Any] = ['date_format']
@dataclass
class _lowerCAmelCase( datasets.BuilderConfig ):
"""simple docstring"""
a : str =","
a : Optional[str] =None
a : Optional[Union[int, List[int], str]] ="infer"
a : Optional[List[str]] =None
a : Optional[List[str]] =None
a : Optional[Union[int, str, List[int], List[str]]] =None
a : Optional[Union[List[int], List[str]]] =None
a : Optional[str] =None
a : bool =True
a : Optional[Literal["c", "python", "pyarrow"]] =None
a : Dict[Union[int, str], Callable[[Any], Any]] =None
a : Optional[list] =None
a : Optional[list] =None
a : bool =False
a : Optional[Union[int, List[int]]] =None
a : Optional[int] =None
a : Optional[Union[str, List[str]]] =None
a : bool =True
a : bool =True
a : bool =False
a : bool =True
a : Optional[str] =None
a : str ="."
a : Optional[str] =None
a : str ='"'
a : int =0
a : Optional[str] =None
a : Optional[str] =None
a : Optional[str] =None
a : Optional[str] =None
a : bool =True
a : bool =True
a : int =0
a : bool =True
a : bool =False
a : Optional[str] =None
a : int =10000
a : Optional[datasets.Features] =None
a : Optional[str] ="strict"
a : Literal["error", "warn", "skip"] ="error"
a : Optional[str] =None
def _a ( self ):
if self.delimiter is not None:
UpperCamelCase_: Optional[Any] = self.delimiter
if self.column_names is not None:
UpperCamelCase_: int = self.column_names
@property
def _a ( self ):
UpperCamelCase_: Any = {
'sep': self.sep,
'header': self.header,
'names': self.names,
'index_col': self.index_col,
'usecols': self.usecols,
'prefix': self.prefix,
'mangle_dupe_cols': self.mangle_dupe_cols,
'engine': self.engine,
'converters': self.converters,
'true_values': self.true_values,
'false_values': self.false_values,
'skipinitialspace': self.skipinitialspace,
'skiprows': self.skiprows,
'nrows': self.nrows,
'na_values': self.na_values,
'keep_default_na': self.keep_default_na,
'na_filter': self.na_filter,
'verbose': self.verbose,
'skip_blank_lines': self.skip_blank_lines,
'thousands': self.thousands,
'decimal': self.decimal,
'lineterminator': self.lineterminator,
'quotechar': self.quotechar,
'quoting': self.quoting,
'escapechar': self.escapechar,
'comment': self.comment,
'encoding': self.encoding,
'dialect': self.dialect,
'error_bad_lines': self.error_bad_lines,
'warn_bad_lines': self.warn_bad_lines,
'skipfooter': self.skipfooter,
'doublequote': self.doublequote,
'memory_map': self.memory_map,
'float_precision': self.float_precision,
'chunksize': self.chunksize,
'encoding_errors': self.encoding_errors,
'on_bad_lines': self.on_bad_lines,
'date_format': self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , _lowerCamelCase ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class _lowerCAmelCase( datasets.ArrowBasedBuilder ):
"""simple docstring"""
a : Dict =CsvConfig
def _a ( self ):
return datasets.DatasetInfo(features=self.config.features )
def _a ( self , _lowerCamelCase ):
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
UpperCamelCase_: Tuple = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCamelCase , (str, list, tuple) ):
UpperCamelCase_: List[Any] = data_files
if isinstance(_lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: str = [files]
UpperCamelCase_: Tuple = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
UpperCamelCase_: Tuple = []
for split_name, files in data_files.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Dict = [files]
UpperCamelCase_: int = [dl_manager.iter_files(_lowerCamelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=_lowerCamelCase , gen_kwargs={'files': files} ) )
return splits
def _a ( self , _lowerCamelCase ):
if self.config.features is not None:
UpperCamelCase_: List[Any] = self.config.features.arrow_schema
if all(not require_storage_cast(_lowerCamelCase ) for feature in self.config.features.values() ):
# cheaper cast
UpperCamelCase_: Optional[int] = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=_lowerCamelCase )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
UpperCamelCase_: int = table_cast(_lowerCamelCase , _lowerCamelCase )
return pa_table
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: List[str] = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
UpperCamelCase_: Dict = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(_lowerCamelCase ) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase ) ):
UpperCamelCase_: Optional[Any] = pd.read_csv(_lowerCamelCase , iterator=_lowerCamelCase , dtype=_lowerCamelCase , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(_lowerCamelCase ):
UpperCamelCase_: Union[str, Any] = pa.Table.from_pandas(_lowerCamelCase )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_lowerCamelCase )
except ValueError as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase )}: {e}''' )
raise | 57 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowercase : Optional[Any] = False
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
pass
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
pipe.to(SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(
image=SCREAMING_SNAKE_CASE , generator=SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=5_0 , output_type="numpy" , ).images
lowerCAmelCase = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
lowerCAmelCase = np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 713 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
lowercase : Union[str, Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
lowercase : Optional[int] = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
lowercase : int = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def __a ( A__ , A__ , A__ , A__ , A__ = None , A__ = False , ) -> List[str]:
if label_map is not None:
for old_id, new_id in label_map.items():
lowerCAmelCase = new_id
# turn into Numpy arrays
lowerCAmelCase = np.array(A__ )
lowerCAmelCase = np.array(A__ )
if reduce_labels:
lowerCAmelCase = 255
lowerCAmelCase = label - 1
lowerCAmelCase = 255
lowerCAmelCase = label != ignore_index
lowerCAmelCase = np.not_equal(A__ , A__ )
lowerCAmelCase = pred_label[mask]
lowerCAmelCase = np.array(A__ )[mask]
lowerCAmelCase = pred_label[pred_label == label]
lowerCAmelCase = np.histogram(A__ , bins=A__ , range=(0, num_labels - 1) )[0]
lowerCAmelCase = np.histogram(A__ , bins=A__ , range=(0, num_labels - 1) )[0]
lowerCAmelCase = np.histogram(A__ , bins=A__ , range=(0, num_labels - 1) )[0]
lowerCAmelCase = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def __a ( A__ , A__ , A__ , A__ , A__ = None , A__ = False , ) -> Optional[int]:
lowerCAmelCase = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(A__ , A__ ):
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = intersect_and_union(
A__ , A__ , A__ , A__ , A__ , A__ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def __a ( A__ , A__ , A__ , A__ , A__ = None , A__ = None , A__ = False , ) -> Dict:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = total_intersect_and_union(
A__ , A__ , A__ , A__ , A__ , A__ )
# compute metrics
lowerCAmelCase = {}
lowerCAmelCase = total_area_intersect.sum() / total_area_label.sum()
lowerCAmelCase = total_area_intersect / total_area_union
lowerCAmelCase = total_area_intersect / total_area_label
lowerCAmelCase = np.nanmean(A__ )
lowerCAmelCase = np.nanmean(A__ )
lowerCAmelCase = all_acc
lowerCAmelCase = iou
lowerCAmelCase = acc
if nan_to_num is not None:
lowerCAmelCase = {metric: np.nan_to_num(A__ , nan=A__ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def __A ( self : Tuple ) -> Any:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) , reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] , )
def __A ( self : Tuple , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : bool , SCREAMING_SNAKE_CASE : Optional[int] = None , SCREAMING_SNAKE_CASE : Optional[Dict[int, int]] = None , SCREAMING_SNAKE_CASE : bool = False , ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = mean_iou(
results=SCREAMING_SNAKE_CASE , gt_seg_maps=SCREAMING_SNAKE_CASE , num_labels=SCREAMING_SNAKE_CASE , ignore_index=SCREAMING_SNAKE_CASE , nan_to_num=SCREAMING_SNAKE_CASE , label_map=SCREAMING_SNAKE_CASE , reduce_labels=SCREAMING_SNAKE_CASE , )
return iou_result
| 159 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
raise ValueError('''String lengths must match!''' )
snake_case_ = 0
for chara, chara in zip(__UpperCAmelCase, __UpperCAmelCase ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertConfig,
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
class a :
def __init__( self : Tuple , lowercase_ : Any , ):
snake_case_ = parent
snake_case_ = 13
snake_case_ = 7
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = 2
snake_case_ = 99
snake_case_ = 0
snake_case_ = 32
snake_case_ = 2
snake_case_ = 4
snake_case_ = 0.1
snake_case_ = 0.1
snake_case_ = 512
snake_case_ = 16
snake_case_ = 2
snake_case_ = 0.02
snake_case_ = 3
snake_case_ = 4
snake_case_ = '''last'''
snake_case_ = True
snake_case_ = None
snake_case_ = 0
def A_ ( self : Union[str, Any] ):
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] , dtype=tf.floataa )
snake_case_ = None
if self.use_input_lengths:
snake_case_ = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , 2 , dtype=tf.floataa )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , bos_token_id=self.bos_token_id , )
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def A_ ( self : List[Any] , lowercase_ : Dict , lowercase_ : Optional[int] , lowercase_ : Any , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : int , lowercase_ : str , lowercase_ : int , ):
snake_case_ = TFFlaubertModel(config=lowercase_ )
snake_case_ = {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
snake_case_ = model(lowercase_ )
snake_case_ = [input_ids, input_mask]
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self : List[str] , lowercase_ : int , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Dict , lowercase_ : List[Any] , ):
snake_case_ = TFFlaubertWithLMHeadModel(lowercase_ )
snake_case_ = {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : str , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Any , lowercase_ : str , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : str , ):
snake_case_ = TFFlaubertForQuestionAnsweringSimple(lowercase_ )
snake_case_ = {'''input_ids''': input_ids, '''lengths''': input_lengths}
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A_ ( self : Optional[Any] , lowercase_ : Any , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : int , lowercase_ : Dict , ):
snake_case_ = TFFlaubertForSequenceClassification(lowercase_ )
snake_case_ = {'''input_ids''': input_ids, '''lengths''': input_lengths}
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A_ ( self : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int , lowercase_ : Tuple , lowercase_ : Union[str, Any] , ):
snake_case_ = self.num_labels
snake_case_ = TFFlaubertForTokenClassification(config=lowercase_ )
snake_case_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : Dict , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : Any , lowercase_ : List[Any] , lowercase_ : int , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Any , ):
snake_case_ = self.num_choices
snake_case_ = TFFlaubertForMultipleChoice(config=lowercase_ )
snake_case_ = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) )
snake_case_ = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) )
snake_case_ = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) )
snake_case_ = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Dict ):
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,(
snake_case_
) ,
) = config_and_inputs
snake_case_ = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''langs''': token_type_ids,
'''lengths''': input_lengths,
}
return config, inputs_dict
@require_tf
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
snake_case_ = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
snake_case_ = (
{
"feature-extraction": TFFlaubertModel,
"fill-mask": TFFlaubertWithLMHeadModel,
"question-answering": TFFlaubertForQuestionAnsweringSimple,
"text-classification": TFFlaubertForSequenceClassification,
"token-classification": TFFlaubertForTokenClassification,
"zero-shot": TFFlaubertForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case_ = False
snake_case_ = False
def A_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def A_ ( self : int ):
snake_case_ = TFFlaubertModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , emb_dim=37 )
def A_ ( self : List[str] ):
self.config_tester.run_common_tests()
def A_ ( self : Optional[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_ )
def A_ ( self : List[str] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_token_classification(*lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowercase_ )
@slow
def A_ ( self : Optional[int] ):
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = TFFlaubertModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_tf
@require_sentencepiece
@require_tokenizers
class a ( unittest.TestCase ):
@slow
def A_ ( self : int ):
snake_case_ = TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''' )
snake_case_ = tf.convert_to_tensor(
[[0, 158, 735, 2592, 1424, 6727, 82, 1]] , dtype=tf.intaa , ) # "J'aime flaubert !"
snake_case_ = model(lowercase_ )[0]
snake_case_ = tf.TensorShape((1, 8, 512) )
self.assertEqual(output.shape , lowercase_ )
# compare the actual values for a slice.
snake_case_ = tf.convert_to_tensor(
[
[
[-1.876_8773, -1.56_6555, 0.2707_2418],
[-1.692_0038, -0.587_3505, 1.932_9599],
[-2.956_3985, -1.699_3835, 1.797_2052],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 640 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_lowerCamelCase = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : tuple , lowercase_ : Path , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=False , ):
'''simple docstring'''
output_path.parent.mkdir(parents=lowercase_ , exist_ok=lowercase_ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
lowercase_ , lowercase_ , f=output_path.as_posix() , input_names=lowercase_ , output_names=lowercase_ , dynamic_axes=lowercase_ , do_constant_folding=lowercase_ , use_external_data_format=lowercase_ , enable_onnx_checker=lowercase_ , opset_version=lowercase_ , )
else:
export(
lowercase_ , lowercase_ , f=output_path.as_posix() , input_names=lowercase_ , output_names=lowercase_ , dynamic_axes=lowercase_ , do_constant_folding=lowercase_ , opset_version=lowercase_ , )
@torch.no_grad()
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : str , lowercase_ : int , lowercase_ : bool = False ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : str = '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''cpu'''
__SCREAMING_SNAKE_CASE : int = StableDiffusionPipeline.from_pretrained(lowercase_ , torch_dtype=lowercase_ ).to(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = Path(lowercase_ )
# TEXT ENCODER
__SCREAMING_SNAKE_CASE : Any = pipeline.text_encoder.config.max_position_embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = pipeline.text_encoder.config.hidden_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = pipeline.tokenizer(
'''A sample prompt''' , padding='''max_length''' , max_length=pipeline.tokenizer.model_max_length , truncation=lowercase_ , return_tensors='''pt''' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=lowercase_ , dtype=torch.intaa )) , output_path=output_path / '''text_encoder''' / '''model.onnx''' , ordered_input_names=['''input_ids'''] , output_names=['''last_hidden_state''', '''pooler_output'''] , dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''sequence'''},
} , opset=lowercase_ , )
del pipeline.text_encoder
# UNET
__SCREAMING_SNAKE_CASE : Any = pipeline.unet.config.in_channels
__SCREAMING_SNAKE_CASE : int = pipeline.unet.config.sample_size
__SCREAMING_SNAKE_CASE : int = output_path / '''unet''' / '''model.onnx'''
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , lowercase_ , lowercase_ , lowercase_ ).to(device=lowercase_ , dtype=lowercase_ ),
torch.randn(2 ).to(device=lowercase_ , dtype=lowercase_ ),
torch.randn(2 , lowercase_ , lowercase_ ).to(device=lowercase_ , dtype=lowercase_ ),
False,
) , output_path=lowercase_ , ordered_input_names=['''sample''', '''timestep''', '''encoder_hidden_states''', '''return_dict'''] , output_names=['''out_sample'''] , dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''timestep''': {0: '''batch'''},
'''encoder_hidden_states''': {0: '''batch''', 1: '''sequence'''},
} , opset=lowercase_ , use_external_data_format=lowercase_ , )
__SCREAMING_SNAKE_CASE : int = str(unet_path.absolute().as_posix() )
__SCREAMING_SNAKE_CASE : Tuple = os.path.dirname(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = onnx.load(lowercase_ )
# clean up existing tensor files
shutil.rmtree(lowercase_ )
os.mkdir(lowercase_ )
# collate external tensor files into one
onnx.save_model(
lowercase_ , lowercase_ , save_as_external_data=lowercase_ , all_tensors_to_one_file=lowercase_ , location='''weights.pb''' , convert_attribute=lowercase_ , )
del pipeline.unet
# VAE ENCODER
__SCREAMING_SNAKE_CASE : List[str] = pipeline.vae
__SCREAMING_SNAKE_CASE : Tuple = vae_encoder.config.in_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
__SCREAMING_SNAKE_CASE : List[str] = lambda lowercase_ , lowercase_ : vae_encoder.encode(lowercase_ , lowercase_ )[0].sample()
onnx_export(
lowercase_ , model_args=(
torch.randn(1 , lowercase_ , lowercase_ , lowercase_ ).to(device=lowercase_ , dtype=lowercase_ ),
False,
) , output_path=output_path / '''vae_encoder''' / '''model.onnx''' , ordered_input_names=['''sample''', '''return_dict'''] , output_names=['''latent_sample'''] , dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=lowercase_ , )
# VAE DECODER
__SCREAMING_SNAKE_CASE : Optional[int] = pipeline.vae
__SCREAMING_SNAKE_CASE : int = vae_decoder.config.latent_channels
__SCREAMING_SNAKE_CASE : Tuple = vae_decoder.config.out_channels
# forward only through the decoder part
__SCREAMING_SNAKE_CASE : Tuple = vae_encoder.decode
onnx_export(
lowercase_ , model_args=(
torch.randn(1 , lowercase_ , lowercase_ , lowercase_ ).to(device=lowercase_ , dtype=lowercase_ ),
False,
) , output_path=output_path / '''vae_decoder''' / '''model.onnx''' , ordered_input_names=['''latent_sample''', '''return_dict'''] , output_names=['''sample'''] , dynamic_axes={
'''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=lowercase_ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
__SCREAMING_SNAKE_CASE : str = pipeline.safety_checker
__SCREAMING_SNAKE_CASE : int = safety_checker.config.vision_config.num_channels
__SCREAMING_SNAKE_CASE : List[str] = safety_checker.config.vision_config.image_size
__SCREAMING_SNAKE_CASE : Any = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , lowercase_ , lowercase_ , lowercase_ , ).to(device=lowercase_ , dtype=lowercase_ ),
torch.randn(1 , lowercase_ , lowercase_ , lowercase_ ).to(device=lowercase_ , dtype=lowercase_ ),
) , output_path=output_path / '''safety_checker''' / '''model.onnx''' , ordered_input_names=['''clip_input''', '''images'''] , output_names=['''out_images''', '''has_nsfw_concepts'''] , dynamic_axes={
'''clip_input''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''images''': {0: '''batch''', 1: '''height''', 2: '''width''', 3: '''channels'''},
} , opset=lowercase_ , )
del pipeline.safety_checker
__SCREAMING_SNAKE_CASE : Dict = OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = pipeline.feature_extractor
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = None
__SCREAMING_SNAKE_CASE : Tuple = None
__SCREAMING_SNAKE_CASE : Any = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_encoder''' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_decoder''' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''text_encoder''' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / '''unet''' ) , scheduler=pipeline.scheduler , safety_checker=lowercase_ , feature_extractor=lowercase_ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(lowercase_ )
print('''ONNX pipeline saved to''' , lowercase_ )
del pipeline
del onnx_pipeline
__SCREAMING_SNAKE_CASE : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(lowercase_ , provider='''CPUExecutionProvider''' )
print('''ONNX pipeline is loadable''' )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=14,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
_lowerCamelCase = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 717 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : int , lowercase_ : int , lowercase_ : Optional[int] ):
'''simple docstring'''
if index == r:
for j in range(lowercase_ ):
print(data[j] , end=''' ''' )
print(''' ''' )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
__SCREAMING_SNAKE_CASE : str = arr[i]
combination_util(lowercase_ , lowercase_ , lowercase_ , index + 1 , lowercase_ , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(lowercase_ , lowercase_ , lowercase_ , 0 , lowercase_ , 0 )
if __name__ == "__main__":
# Driver code to check the function above
_lowerCamelCase = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 401 | 0 |
class UpperCamelCase__ :
def __init__( self : List[Any] , UpperCamelCase__ : Any ):
'''simple docstring'''
lowercase_ = arr.split(""",""" )
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
lowercase_ = [int(self.array[0] )] * len(self.array )
lowercase_ = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
lowercase_ = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
lowercase_ = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
a = input('please input some numbers:')
a = SubArray(whole_array)
a = array.solve_sub_array()
print(('the results is:', re))
| 412 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase : str = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=_UpperCAmelCase , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=_UpperCAmelCase , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=_UpperCAmelCase , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=_UpperCAmelCase , default='data/dump' , help='The dump file prefix.' )
lowerCAmelCase = parser.parse_args()
logger.info(F'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
lowerCAmelCase = BertTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
lowerCAmelCase = RobertaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['cls_token'] # `<s>`
lowerCAmelCase = tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
lowerCAmelCase = GPTaTokenizer.from_pretrained(args.tokenizer_name )
lowerCAmelCase = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
lowerCAmelCase = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(F'Loading text from {args.file_path}' )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
lowerCAmelCase = fp.readlines()
logger.info('Start encoding' )
logger.info(F'{len(_UpperCAmelCase )} examples to process.' )
lowerCAmelCase = []
lowerCAmelCase = 0
lowerCAmelCase = 1_0000
lowerCAmelCase = time.time()
for text in data:
lowerCAmelCase = F'{bos} {text.strip()} {sep}'
lowerCAmelCase = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
rslt.append(_UpperCAmelCase )
iter += 1
if iter % interval == 0:
lowerCAmelCase = time.time()
logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
lowerCAmelCase = time.time()
logger.info('Finished binarization' )
logger.info(F'{len(_UpperCAmelCase )} examples processed.' )
lowerCAmelCase = F'{args.dump_file}.{args.tokenizer_name}.pickle'
lowerCAmelCase = tokenizer.vocab_size
if vocab_size < (1 << 16):
lowerCAmelCase = [np.uintaa(_UpperCAmelCase ) for d in rslt]
else:
lowerCAmelCase = [np.intaa(_UpperCAmelCase ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F'Dump to {dp_file}' )
with open(_UpperCAmelCase , 'wb' ) as handle:
pickle.dump(rslt_ , _UpperCAmelCase , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 4 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :Optional[int] ):
'''simple docstring'''
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10)) | 703 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
__A : str = tuple[int, int]
class __UpperCamelCase :
def __init__( self :Union[str, Any] ,_UpperCamelCase :set[int] ,_UpperCamelCase :Mapping[EdgeT, int] ):
snake_case_ : set[int] = vertices
snake_case_ : dict[EdgeT, int] = {
(min(_UpperCamelCase ), max(_UpperCamelCase )): weight for edge, weight in edges.items()
}
def a__ ( self :Tuple ,_UpperCamelCase :EdgeT ,_UpperCamelCase :int ):
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
snake_case_ : str = weight
def a__ ( self :Tuple ):
snake_case_ : Graph = Graph({min(self.vertices )} ,{} )
snake_case_ : EdgeT
snake_case_ : int
snake_case_ : EdgeT
snake_case_ : int
while len(subgraph.vertices ) < len(self.vertices ):
snake_case_ : int = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
snake_case_ : Dict = edge
snake_case_ : Dict = weight
subgraph.add_edge(_UpperCamelCase ,_UpperCamelCase )
return subgraph
def UpperCAmelCase ( lowerCamelCase_ :str = "p107_network.txt" ):
'''simple docstring'''
snake_case_ : str = os.path.abspath(os.path.dirname(lowerCamelCase_ ) )
snake_case_ : str = os.path.join(lowerCamelCase_ , lowerCamelCase_ )
snake_case_ : dict[EdgeT, int] = {}
snake_case_ : list[str]
snake_case_ : int
snake_case_ : int
with open(lowerCamelCase_ ) as f:
snake_case_ : Optional[int] = f.read().strip().split("""\n""" )
snake_case_ : Any = [line.split(""",""" ) for line in data]
for edgea in range(1 , len(lowerCamelCase_ ) ):
for edgea in range(lowerCamelCase_ ):
if adjaceny_matrix[edgea][edgea] != "-":
snake_case_ : str = int(adjaceny_matrix[edgea][edgea] )
snake_case_ : Graph = Graph(set(range(len(lowerCamelCase_ ) ) ) , lowerCamelCase_ )
snake_case_ : Graph = graph.prims_algorithm()
snake_case_ : int = sum(graph.edges.values() )
snake_case_ : int = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F'{solution() = }') | 267 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
lowerCamelCase__ : List[Any] = [
{"dataset": "wikipedia", "config_name": "20220301.de"},
{"dataset": "wikipedia", "config_name": "20220301.en"},
{"dataset": "wikipedia", "config_name": "20220301.fr"},
{"dataset": "wikipedia", "config_name": "20220301.frr"},
{"dataset": "wikipedia", "config_name": "20220301.it"},
{"dataset": "wikipedia", "config_name": "20220301.simple"},
{"dataset": "snli", "config_name": "plain_text"},
{"dataset": "eli5", "config_name": "LFQA_reddit"},
{"dataset": "wiki40b", "config_name": "en"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
{"dataset": "natural_questions", "config_name": "default"},
]
def UpperCamelCase ( _lowerCAmelCase : str=True ) -> Optional[Any]:
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_UpperCAmelCase))
class _UpperCAmelCase ( _UpperCAmelCase):
__a : List[str] = None
__a : Optional[Any] = None
def __snake_case ( self , _A , _A ) -> Optional[int]:
'''simple docstring'''
with TemporaryDirectory() as tmp_dir:
_UpperCAmelCase : Optional[Any] = dataset_module_factory(UpperCAmelCase__ , cache_dir=UpperCAmelCase__ )
_UpperCAmelCase : List[Any] = import_main_class(dataset_module.module_path , dataset=UpperCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = builder_cls(
cache_dir=UpperCAmelCase__ , config_name=UpperCAmelCase__ , hash=dataset_module.hash , )
_UpperCAmelCase : List[str] = """/""".join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=UpperCAmelCase__ ).replace(os.sep , """/""" ),
config.DATASET_INFO_FILENAME,
] )
_UpperCAmelCase : Optional[Any] = cached_path(UpperCAmelCase__ , cache_dir=UpperCAmelCase__ )
self.assertTrue(os.path.exists(UpperCAmelCase__ ) )
@pytest.mark.integration
def UpperCamelCase ( _lowerCAmelCase : List[str] ) -> List[Any]:
_UpperCAmelCase : Dict = tmp_path_factory.mktemp("""test_hf_gcp""" ) / """test_wikipedia_simple"""
_UpperCAmelCase : Optional[int] = dataset_module_factory("""wikipedia""", cache_dir=_A )
_UpperCAmelCase : Optional[Any] = import_main_class(dataset_module.module_path )
_UpperCAmelCase : Optional[Any] = builder_cls(
cache_dir=_A, config_name="""20220301.frr""", hash=dataset_module.hash, )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
_UpperCAmelCase : Dict = None
builder_instance.download_and_prepare()
_UpperCAmelCase : List[Any] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def UpperCamelCase ( _lowerCAmelCase : List[str] ) -> int:
_UpperCAmelCase : List[Any] = dataset_module_factory("""wikipedia""", cache_dir=_A )
_UpperCAmelCase : Optional[Any] = import_main_class(dataset_module.module_path, dataset=_A )
_UpperCAmelCase : List[Any] = builder_cls(
cache_dir=_A, config_name="""20220301.frr""", hash=dataset_module.hash, )
_UpperCAmelCase : Optional[Any] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_A, _A )
assert "train" in ds
assert isinstance(ds["""train"""], _A )
assert next(iter(ds["""train"""] ) )
| 238 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCAmelCase_ : List[str] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
UpperCAmelCase_ : str = parser.parse_args()
UpperCAmelCase_ : List[str] = "cpu"
UpperCAmelCase_ : Dict = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"
UpperCAmelCase_ : Optional[int] = "path-to-your-trained-model"
UpperCAmelCase_ : Tuple = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
UpperCAmelCase_ : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCAmelCase_ : Tuple = pipe.to(device)
# to channels last
UpperCAmelCase_ : int = pipe.unet.to(memory_format=torch.channels_last)
UpperCAmelCase_ : List[str] = pipe.vae.to(memory_format=torch.channels_last)
UpperCAmelCase_ : Optional[Any] = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
UpperCAmelCase_ : Tuple = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
UpperCAmelCase_ : str = torch.randn(2, 4, 64, 64)
UpperCAmelCase_ : Optional[Any] = torch.rand(1) * 999
UpperCAmelCase_ : Optional[int] = torch.randn(2, 77, 768)
UpperCAmelCase_ : Tuple = (sample, timestep, encoder_hidden_status)
try:
UpperCAmelCase_ : Optional[int] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
UpperCAmelCase_ : List[str] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCAmelCase_ : Tuple = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCAmelCase_ : int = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
UpperCAmelCase_ : List[str] = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
UpperCAmelCase_ : List[str] = 666
UpperCAmelCase_ : Dict = torch.Generator(device).manual_seed(seed)
UpperCAmelCase_ : Tuple = {"generator": generator}
if args.steps is not None:
UpperCAmelCase_ : Tuple = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
UpperCAmelCase_ : List[str] = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save("generated.png")
| 491 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case = None ) -> list[list[str]]:
__lowerCAmelCase : Optional[int] = word_bank or []
# create a table
__lowerCAmelCase : int = len(__snake_case ) + 1
__lowerCAmelCase : list[list[list[str]]] = []
for _ in range(__snake_case ):
table.append([] )
# seed value
__lowerCAmelCase : List[Any] = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(__snake_case ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(__snake_case )] == word:
__lowerCAmelCase : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(__snake_case )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(__snake_case )]:
combination.reverse()
return table[len(__snake_case )]
if __name__ == "__main__":
print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
print(
all_construct(
'hexagonosaurus',
['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
)
) | 615 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = RoCBertTokenizer
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = filter_non_english
def _SCREAMING_SNAKE_CASE ( self: int) -> Optional[Any]:
"""simple docstring"""
super().setUp()
__lowerCAmelCase : Dict = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
__lowerCAmelCase : str = {}
__lowerCAmelCase : List[Any] = {}
for i, value in enumerate(_SCREAMING_SNAKE_CASE):
__lowerCAmelCase : int = i
__lowerCAmelCase : List[str] = i
__lowerCAmelCase : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"])
__lowerCAmelCase : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_shape_file"])
__lowerCAmelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_pronunciation_file"])
with open(self.vocab_file , "w" , encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
with open(self.word_shape_file , "w" , encoding="utf-8") as word_shape_writer:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE)
with open(self.word_pronunciation_file , "w" , encoding="utf-8") as word_pronunciation_writer:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Dict) -> str:
"""simple docstring"""
__lowerCAmelCase : Dict = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file)
__lowerCAmelCase : List[Any] = tokenizer.tokenize("你好[SEP]你是谁")
self.assertListEqual(_SCREAMING_SNAKE_CASE , ["你", "好", "[SEP]", "你", "是", "谁"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE) , [5, 6, 2, 5, 7, 8])
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_SCREAMING_SNAKE_CASE) , [5, 6, 2, 5, 7, 8])
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_SCREAMING_SNAKE_CASE) , [5, 6, 2, 5, 7, 8])
def _SCREAMING_SNAKE_CASE ( self: Dict) -> int:
"""simple docstring"""
__lowerCAmelCase : List[str] = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz") , ["ah", "\u535A", "\u63A8", "zz"])
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : int = RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? ") , ["hello", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def _SCREAMING_SNAKE_CASE ( self: str) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE , strip_accents=_SCREAMING_SNAKE_CASE)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hällo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["h\u00E9llo"])
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Tuple = RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE , strip_accents=_SCREAMING_SNAKE_CASE)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hallo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Dict = RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hallo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Any = RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? ") , ["HeLLo", "!", "how", "Are", "yoU", "?"])
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : str = RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE , strip_accents=_SCREAMING_SNAKE_CASE)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["HäLLo", "!", "how", "Are", "yoU", "?"])
def _SCREAMING_SNAKE_CASE ( self: Any) -> Dict:
"""simple docstring"""
__lowerCAmelCase : str = RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE , strip_accents=_SCREAMING_SNAKE_CASE)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["HaLLo", "!", "how", "Are", "yoU", "?"])
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> str:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE , never_split=["[UNK]"])
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]") , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"])
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : str = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
__lowerCAmelCase : Union[str, Any] = {}
for i, token in enumerate(_SCREAMING_SNAKE_CASE):
__lowerCAmelCase : Optional[int] = i
__lowerCAmelCase : Any = RoCBertWordpieceTokenizer(vocab=_SCREAMING_SNAKE_CASE , unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize("") , [])
self.assertListEqual(tokenizer.tokenize("unwanted running") , ["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize("unwantedX running") , ["[UNK]", "runn", "##ing"])
def _SCREAMING_SNAKE_CASE ( self: str) -> int:
"""simple docstring"""
self.assertTrue(_is_whitespace(" "))
self.assertTrue(_is_whitespace("\t"))
self.assertTrue(_is_whitespace("\r"))
self.assertTrue(_is_whitespace("\n"))
self.assertTrue(_is_whitespace("\u00A0"))
self.assertFalse(_is_whitespace("A"))
self.assertFalse(_is_whitespace("-"))
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> Dict:
"""simple docstring"""
self.assertTrue(_is_control("\u0005"))
self.assertFalse(_is_control("A"))
self.assertFalse(_is_control(" "))
self.assertFalse(_is_control("\t"))
self.assertFalse(_is_control("\r"))
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> str:
"""simple docstring"""
self.assertTrue(_is_punctuation("-"))
self.assertTrue(_is_punctuation("$"))
self.assertTrue(_is_punctuation("`"))
self.assertTrue(_is_punctuation("."))
self.assertFalse(_is_punctuation("A"))
self.assertFalse(_is_punctuation(" "))
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Dict = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(_SCREAMING_SNAKE_CASE) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]])
if self.test_rust_tokenizer:
__lowerCAmelCase : List[str] = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]])
def _SCREAMING_SNAKE_CASE ( self: int) -> List[str]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
__lowerCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
__lowerCAmelCase : List[Any] = tokenizer_r.encode_plus(
_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , return_offsets_mapping=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Optional[Any] = tokenizer_r.do_lower_case if hasattr(_SCREAMING_SNAKE_CASE , "do_lower_case") else False
__lowerCAmelCase : int = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]))
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"])
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = ["的", "人", "有"]
__lowerCAmelCase : Tuple = "".join(_SCREAMING_SNAKE_CASE)
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
__lowerCAmelCase : Any = True
__lowerCAmelCase : Dict = self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : int = self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = tokenizer_p.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = tokenizer_r.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = tokenizer_r.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = tokenizer_p.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : int = False
__lowerCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = tokenizer_r.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = tokenizer_p.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : int = tokenizer_r.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = tokenizer_p.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)
# it is expected that only the first Chinese character is not preceded by "##".
__lowerCAmelCase : Union[str, Any] = [
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_SCREAMING_SNAKE_CASE)
]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
@slow
def _SCREAMING_SNAKE_CASE ( self: Dict) -> str:
"""simple docstring"""
__lowerCAmelCase : Tuple = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file)
__lowerCAmelCase : List[Any] = tokenizer.encode("你好" , add_special_tokens=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = tokenizer.encode("你是谁" , add_special_tokens=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _SCREAMING_SNAKE_CASE ( self: str) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}"""):
__lowerCAmelCase : Dict = "你好，你是谁"
__lowerCAmelCase : Tuple = tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = tokenizer.convert_tokens_to_shape_ids(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = tokenizer.convert_tokens_to_pronunciation_ids(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : int = tokenizer.prepare_for_model(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = tokenizer.encode_plus(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE)
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE) | 615 | 1 |
'''simple docstring'''
import numpy as np
def UpperCamelCase__ ( _lowercase : List[Any] ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 523 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ = 0 , snake_case__ = -1 ) -> int:
if hi < 0:
lowerCAmelCase = len(snake_case__ )
while lo < hi:
lowerCAmelCase = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
lowerCAmelCase = mid + 1
else:
lowerCAmelCase = mid
return lo
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ = 0 , snake_case__ = -1 ) -> int:
if hi < 0:
lowerCAmelCase = len(snake_case__ )
while lo < hi:
lowerCAmelCase = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
lowerCAmelCase = mid + 1
else:
lowerCAmelCase = mid
return lo
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ = 0 , snake_case__ = -1 ) -> None:
sorted_collection.insert(bisect_left(snake_case__ , snake_case__ , snake_case__ , snake_case__ ) , snake_case__ )
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ = 0 , snake_case__ = -1 ) -> None:
sorted_collection.insert(bisect_right(snake_case__ , snake_case__ , snake_case__ , snake_case__ ) , snake_case__ )
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> int | None:
lowerCAmelCase = 0
lowerCAmelCase = len(snake_case__ ) - 1
while left <= right:
lowerCAmelCase = left + (right - left) // 2
lowerCAmelCase = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
lowerCAmelCase = midpoint - 1
else:
lowerCAmelCase = midpoint + 1
return None
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> int | None:
lowerCAmelCase = bisect.bisect_left(snake_case__ , snake_case__ )
if index != len(snake_case__ ) and sorted_collection[index] == item:
return index
return None
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> int | None:
if right < left:
return None
lowerCAmelCase = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(snake_case__ , snake_case__ , snake_case__ , midpoint - 1 )
else:
return binary_search_by_recursion(snake_case__ , snake_case__ , midpoint + 1 , snake_case__ )
if __name__ == "__main__":
lowercase__ : int = input('''Enter numbers separated by comma:\n''').strip()
lowercase__ : Tuple = sorted(int(item) for item in user_input.split(''','''))
lowercase__ : int = int(input('''Enter a single number to be found in the list:\n'''))
lowercase__ : Dict = binary_search(collection, target)
if result is None:
print(f'{target} was not found in {collection}.')
else:
print(f'{target} was found at position {result} in {collection}.')
| 312 | 0 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''' , [None, '''v2'''] )
def _A ( snake_case__ : Tuple , snake_case__ : int , snake_case__ : str ):
snake_case__ : List[Any] = hf_hub_url(repo_id=snake_case__ , path=snake_case__ , revision=snake_case__ )
assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(snake_case__ )}'''
| 713 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Any = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : int = [
"IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 694 | 0 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__lowercase : Any = logging.getLogger()
def lowercase ( ) -> Optional[int]:
'''simple docstring'''
snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument("""-f""" )
snake_case : Optional[Any] = parser.parse_args()
return args.f
class _A ( snake_case ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : str = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 ,"""run_glue_deebert.py""" )
with patch.object(SCREAMING_SNAKE_CASE_ ,"""argv""" ,SCREAMING_SNAKE_CASE_ ):
snake_case : List[str] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(SCREAMING_SNAKE_CASE_ ,0.6_66 )
@slow
@require_torch_non_multi_gpu
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Union[str, Any] = """
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
""".split()
self.run_and_check(SCREAMING_SNAKE_CASE_ )
snake_case : Any = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(SCREAMING_SNAKE_CASE_ )
snake_case : Optional[int] = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(SCREAMING_SNAKE_CASE_ )
| 36 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class _lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=64 , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
lowerCAmelCase__ : Optional[int] = parent
lowerCAmelCase__ : Tuple = batch_size
lowerCAmelCase__ : Union[str, Any] = seq_length
lowerCAmelCase__ : str = is_training
lowerCAmelCase__ : Union[str, Any] = use_input_mask
lowerCAmelCase__ : List[Any] = use_token_type_ids
lowerCAmelCase__ : int = use_labels
lowerCAmelCase__ : List[Any] = vocab_size
lowerCAmelCase__ : Optional[int] = hidden_size
lowerCAmelCase__ : List[str] = embedding_size
lowerCAmelCase__ : Optional[int] = num_hidden_layers
lowerCAmelCase__ : Optional[int] = num_attention_heads
lowerCAmelCase__ : List[str] = intermediate_size
lowerCAmelCase__ : Tuple = hidden_act
lowerCAmelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCAmelCase__ : Union[str, Any] = max_position_embeddings
lowerCAmelCase__ : List[Any] = type_vocab_size
lowerCAmelCase__ : Optional[Any] = type_sequence_label_size
lowerCAmelCase__ : List[Any] = initializer_range
lowerCAmelCase__ : Optional[Any] = num_labels
lowerCAmelCase__ : List[str] = num_choices
lowerCAmelCase__ : Any = scope
def __magic_name__( self ):
lowerCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ : str = None
if self.use_input_mask:
lowerCAmelCase__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ : Optional[Any] = None
if self.use_token_type_ids:
lowerCAmelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase__ : Dict = None
lowerCAmelCase__ : Dict = None
lowerCAmelCase__ : Optional[int] = None
if self.use_labels:
lowerCAmelCase__ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase__ : Union[str, Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__( self ):
return MegatronBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : Union[str, Any] = MegatronBertModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Union[str, Any] = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : List[Any] = MegatronBertForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Tuple = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : Optional[Any] = MegatronBertForCausalLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Union[str, Any] = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : str = MegatronBertForNextSentencePrediction(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Dict = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : str = MegatronBertForPreTraining(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : int = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , next_sentence_label=__UpperCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : str = MegatronBertForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Union[str, Any] = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : Optional[Any] = self.num_labels
lowerCAmelCase__ : Union[str, Any] = MegatronBertForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Dict = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : Optional[Any] = self.num_labels
lowerCAmelCase__ : str = MegatronBertForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Dict = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ : Optional[Any] = self.num_choices
lowerCAmelCase__ : Dict = MegatronBertForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ : int = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ : Any = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__( self ):
lowerCAmelCase__ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) ,
) : Optional[int] = config_and_inputs
lowerCAmelCase__ : int = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( _lowercase , _lowercase , unittest.TestCase ):
A__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
A__ = (
{
'feature-extraction': MegatronBertModel,
'fill-mask': MegatronBertForMaskedLM,
'question-answering': MegatronBertForQuestionAnswering,
'text-classification': MegatronBertForSequenceClassification,
'text-generation': MegatronBertForCausalLM,
'token-classification': MegatronBertForTokenClassification,
'zero-shot': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
A__ = True
# test_resize_embeddings = False
A__ = False
def __magic_name__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
lowerCAmelCase__ : List[Any] = super()._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase , return_labels=__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
lowerCAmelCase__ : Optional[int] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def __magic_name__( self ):
lowerCAmelCase__ : str = MegatronBertModelTester(self )
lowerCAmelCase__ : Dict = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def __magic_name__( self ):
self.config_tester.run_common_tests()
def __magic_name__( self ):
lowerCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__UpperCAmelCase )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[int]:
return torch.tensor(
UpperCamelCase , dtype=torch.long , device=UpperCamelCase , )
lowerCAmelCase_ = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( unittest.TestCase ):
@slow
@unittest.skip('''Model is not available.''' )
def __magic_name__( self ):
lowerCAmelCase__ : int = '''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
lowerCAmelCase__ : Union[str, Any] = os.path.join(os.environ['''MYDIR'''] , __UpperCAmelCase )
lowerCAmelCase__ : Tuple = MegatronBertModel.from_pretrained(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.half()
lowerCAmelCase__ : Optional[int] = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
lowerCAmelCase__ : List[Any] = model(__UpperCAmelCase )[0]
lowerCAmelCase__ : List[Any] = torch.Size((1, 9, 1024) )
self.assertEqual(output.shape , __UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
lowerCAmelCase__ : Union[str, Any] = output[0, ii, jj]
lowerCAmelCase__ : Optional[Any] = expected[3 * ii + jj]
lowerCAmelCase__ : List[str] = '''ii={} jj={} a={} b={}'''.format(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
self.assertTrue(math.isclose(__UpperCAmelCase , __UpperCAmelCase , rel_tol=__UpperCAmelCase , abs_tol=__UpperCAmelCase ) , msg=__UpperCAmelCase )
| 678 | 0 |
def UpperCAmelCase__ ( _A ):
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''' )
a_ = 0
a_ = str(_A )
while len(_A ) != 1:
a_ = [int(_A ) for i in num_string]
a_ = 1
for i in range(0 , len(_A ) ):
total *= numbers[i]
a_ = str(_A )
steps += 1
return steps
def UpperCAmelCase__ ( _A ):
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError('''additive_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''' )
a_ = 0
a_ = str(_A )
while len(_A ) != 1:
a_ = [int(_A ) for i in num_string]
a_ = 0
for i in range(0 , len(_A ) ):
total += numbers[i]
a_ = str(_A )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 143 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimeSeriesTransformerForPrediction''',
'''TimeSeriesTransformerModel''',
'''TimeSeriesTransformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 143 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase ( _lowerCAmelCase : list[int | str] ) -> None:
create_state_space_tree(_lowerCAmelCase, [], 0, [0 for i in range(len(_lowerCAmelCase ) )] )
def UpperCamelCase ( _lowerCAmelCase : list[int | str], _lowerCAmelCase : list[int | str], _lowerCAmelCase : int, _lowerCAmelCase : list[int], ) -> None:
if index == len(_lowerCAmelCase ):
print(_lowerCAmelCase )
return
for i in range(len(_lowerCAmelCase ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
_UpperCAmelCase : Union[str, Any] = True
create_state_space_tree(_lowerCAmelCase, _lowerCAmelCase, index + 1, _lowerCAmelCase )
current_sequence.pop()
_UpperCAmelCase : List[str] = False
lowerCamelCase__ : list[int | str] = [3, 1, 2, 4]
generate_all_permutations(sequence)
lowerCamelCase__ : list[int | str] = ["A", "B", "C"]
generate_all_permutations(sequence_a)
| 238 |
"""simple docstring"""
from __future__ import annotations
lowerCamelCase__ : Optional[int] = [True] * 1_00_00_01
lowerCamelCase__ : List[Any] = 2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
lowerCamelCase__ : Optional[Any] = False
i += 1
def UpperCamelCase ( _lowerCAmelCase : int ) -> bool:
return seive[n]
def UpperCamelCase ( _lowerCAmelCase : int ) -> bool:
return any(digit in """02468""" for digit in str(_lowerCAmelCase ) )
def UpperCamelCase ( _lowerCAmelCase : int = 1000000 ) -> list[int]:
_UpperCAmelCase : List[Any] = [2] # result already includes the number 2.
for num in range(3, limit + 1, 2 ):
if is_prime(_lowerCAmelCase ) and not contains_an_even_digit(_lowerCAmelCase ):
_UpperCAmelCase : List[Any] = str(_lowerCAmelCase )
_UpperCAmelCase : Optional[int] = [int(str_num[j:] + str_num[:j] ) for j in range(len(_lowerCAmelCase ) )]
if all(is_prime(_lowerCAmelCase ) for i in list_nums ):
result.append(_lowerCAmelCase )
return result
def UpperCamelCase ( ) -> int:
return len(find_circular_primes() )
if __name__ == "__main__":
print(F'''{len(find_circular_primes()) = }''')
| 238 | 1 |
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__( self , lowerCamelCase ) -> str:
snake_case_ = data
snake_case_ = None
snake_case_ = None
def UpperCamelCase( ) -> TreeNode:
'''simple docstring'''
print("""\n********Press N to stop entering at any point of time********\n""" )
snake_case_ = input("""Enter the value of the root node: """ ).strip().lower()
snake_case_ = queue.Queue()
snake_case_ = TreeNode(int(lowercase_ ) )
q.put(lowercase_ )
while not q.empty():
snake_case_ = q.get()
snake_case_ = f'''Enter the left node of {node_found.data}: '''
snake_case_ = input(lowercase_ ).strip().lower() or """n"""
if check == "n":
return tree_node
snake_case_ = TreeNode(int(lowercase_ ) )
snake_case_ = left_node
q.put(lowercase_ )
snake_case_ = f'''Enter the right node of {node_found.data}: '''
snake_case_ = input(lowercase_ ).strip().lower() or """n"""
if check == "n":
return tree_node
snake_case_ = TreeNode(int(lowercase_ ) )
snake_case_ = right_node
q.put(lowercase_ )
raise
def UpperCamelCase( lowercase_ ) -> None:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def UpperCamelCase( lowercase_ ) -> None:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def UpperCamelCase( lowercase_ ) -> None:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def UpperCamelCase( lowercase_ ) -> None:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
snake_case_ = queue.Queue()
q.put(lowercase_ )
while not q.empty():
snake_case_ = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def UpperCamelCase( lowercase_ ) -> None:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
snake_case_ = queue.Queue()
q.put(lowercase_ )
while not q.empty():
snake_case_ = []
while not q.empty():
snake_case_ = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(lowercase_ )
def UpperCamelCase( lowercase_ ) -> None:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
snake_case_ = []
snake_case_ = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(lowercase_ )
snake_case_ = n.left
# end of while means current node doesn't have left child
snake_case_ = stack.pop()
# start to traverse its right child
snake_case_ = n.right
def UpperCamelCase( lowercase_ ) -> None:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
snake_case_ = []
snake_case_ = node
while n or stack:
while n:
stack.append(lowercase_ )
snake_case_ = n.left
snake_case_ = stack.pop()
print(n.data , end=""",""" )
snake_case_ = n.right
def UpperCamelCase( lowercase_ ) -> None:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or not node:
return
snake_case_ , snake_case_ = [], []
snake_case_ = node
stacka.append(lowercase_ )
while stacka: # to find the reversed order of post order, store it in stack2
snake_case_ = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(lowercase_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def UpperCamelCase( lowercase_ = "" , lowercase_=50 , lowercase_="*" ) -> str:
'''simple docstring'''
if not s:
return "\n" + width * char
snake_case_ , snake_case_ = divmod(width - len(lowercase_ ) - 2 , 2 )
return f'''{left * char} {s} {(left + extra) * char}'''
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('''Binary Tree Traversals'''))
lowerCamelCase_ = build_tree()
print(prompt('''Pre Order Traversal'''))
pre_order(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal'''))
in_order(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal'''))
post_order(node)
print(prompt() + '''\n''')
print(prompt('''Level Order Traversal'''))
level_order(node)
print(prompt() + '''\n''')
print(prompt('''Actual Level Order Traversal'''))
level_order_actual(node)
print('''*''' * 50 + '''\n''')
print(prompt('''Pre Order Traversal - Iteration Version'''))
pre_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal - Iteration Version'''))
in_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal - Iteration Version'''))
post_order_iter(node)
print(prompt()) | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''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:
lowerCamelCase_ = [
'''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:
lowerCamelCase_ = [
'''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
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 161 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _lowercase ( lowerCamelCase__ , unittest.TestCase ):
_a : str = FlaxAutoencoderKL
@property
def lowercase__ ( self ):
snake_case__ : List[str] =4
snake_case__ : Dict =3
snake_case__ : Optional[int] =(3_2, 3_2)
snake_case__ : int =jax.random.PRNGKey(0 )
snake_case__ : List[Any] =jax.random.uniform(lowercase_ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def lowercase__ ( self ):
snake_case__ : Any ={
"""block_out_channels""": [3_2, 6_4],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
snake_case__ : List[str] =self.dummy_input
return init_dict, inputs_dict
| 385 |
def A ( snake_case__ : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
__snake_case = []
for temp in range(int(snake_case__ ) ):
series.append(f"1/{temp + 1}" if series else '1' )
return series
if __name__ == "__main__":
UpperCAmelCase__ : List[Any] = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| 313 | 0 |
def lowerCamelCase ( UpperCamelCase : int ) -> int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError('Input value must be a \'int\' type' )
return bin(UpperCamelCase ).count('1' )
if __name__ == "__main__":
import doctest
doctest.testmod() | 704 | A = 'Alexander Joslin'
import operator as op
from .stack import Stack
def lowerCamelCase ( UpperCamelCase : str ) -> int:
_lowerCamelCase = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
_lowerCamelCase = Stack()
_lowerCamelCase = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(UpperCamelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(UpperCamelCase )
elif i == ")":
# RULE 4
_lowerCamelCase = operator_stack.peek()
operator_stack.pop()
_lowerCamelCase = operand_stack.peek()
operand_stack.pop()
_lowerCamelCase = operand_stack.peek()
operand_stack.pop()
_lowerCamelCase = operators[opr](UpperCamelCase , UpperCamelCase )
operand_stack.push(UpperCamelCase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
A = '(5 + ((4 * 2) * (2 + 3)))'
# answer = 45
print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''') | 234 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowercase (tf.keras.layers.Layer ):
def __init__( self , A_ , A_ , A_ , A_ , A_=1 , A_=False , **A_ ) ->Any:
'''simple docstring'''
super().__init__(**__UpperCamelCase )
__lowerCAmelCase : Optional[Any] = vocab_size
__lowerCAmelCase : int = d_embed
__lowerCAmelCase : Dict = d_proj
__lowerCAmelCase : str = cutoffs + [vocab_size]
__lowerCAmelCase : Dict = [0] + self.cutoffs
__lowerCAmelCase : Optional[int] = div_val
__lowerCAmelCase : Tuple = self.cutoffs[0]
__lowerCAmelCase : Optional[Any] = len(self.cutoffs ) - 1
__lowerCAmelCase : str = self.shortlist_size + self.n_clusters
__lowerCAmelCase : Dict = keep_order
__lowerCAmelCase : int = []
__lowerCAmelCase : Optional[Any] = []
def UpperCamelCase__ ( self , A_ ) ->int:
'''simple docstring'''
if self.n_clusters > 0:
__lowerCAmelCase : List[Any] = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__UpperCamelCase , name='''cluster_weight''' )
__lowerCAmelCase : str = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__UpperCamelCase , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
__lowerCAmelCase : Any = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__UpperCamelCase , name=f"""out_projs_._{i}""" , )
self.out_projs.append(__UpperCamelCase )
else:
self.out_projs.append(__UpperCamelCase )
__lowerCAmelCase : List[str] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__UpperCamelCase , name=f"""out_layers_._{i}_._weight""" , )
__lowerCAmelCase : List[Any] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__UpperCamelCase , name=f"""out_layers_._{i}_._bias""" , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
__lowerCAmelCase, __lowerCAmelCase : int = self.cutoff_ends[i], self.cutoff_ends[i + 1]
__lowerCAmelCase : int = self.d_embed // (self.div_val**i)
__lowerCAmelCase : Tuple = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__UpperCamelCase , name=f"""out_projs_._{i}""" )
self.out_projs.append(__UpperCamelCase )
__lowerCAmelCase : Optional[Any] = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__UpperCamelCase , name=f"""out_layers_._{i}_._weight""" , )
__lowerCAmelCase : int = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__UpperCamelCase , name=f"""out_layers_._{i}_._bias""" , )
self.out_layers.append((weight, bias) )
super().build(__UpperCamelCase )
@staticmethod
def UpperCamelCase__ ( A_ , A_ , A_ , A_=None ) ->int:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = x
if proj is not None:
__lowerCAmelCase : Union[str, Any] = tf.einsum('''ibd,ed->ibe''' , __UpperCamelCase , __UpperCamelCase )
return tf.einsum('''ibd,nd->ibn''' , __UpperCamelCase , __UpperCamelCase ) + b
@staticmethod
def UpperCamelCase__ ( A_ , A_ ) ->Tuple:
'''simple docstring'''
__lowerCAmelCase : int = shape_list(__UpperCamelCase )
__lowerCAmelCase : Optional[Any] = tf.range(lp_size[0] , dtype=target.dtype )
__lowerCAmelCase : str = tf.stack([r, target] , 1 )
return tf.gather_nd(__UpperCamelCase , __UpperCamelCase )
def UpperCamelCase__ ( self , A_ , A_ , A_=True , A_=False ) ->List[Any]:
'''simple docstring'''
__lowerCAmelCase : Any = 0
if self.n_clusters == 0:
__lowerCAmelCase : Any = self._logit(__UpperCamelCase , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
__lowerCAmelCase : str = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__UpperCamelCase , logits=__UpperCamelCase )
__lowerCAmelCase : Optional[int] = tf.nn.log_softmax(__UpperCamelCase , axis=-1 )
else:
__lowerCAmelCase : List[str] = shape_list(__UpperCamelCase )
__lowerCAmelCase : Dict = []
__lowerCAmelCase : int = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
__lowerCAmelCase, __lowerCAmelCase : List[str] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
__lowerCAmelCase : Union[str, Any] = (target >= l_idx) & (target < r_idx)
__lowerCAmelCase : str = tf.where(__UpperCamelCase )
__lowerCAmelCase : List[Any] = tf.boolean_mask(__UpperCamelCase , __UpperCamelCase ) - l_idx
if self.div_val == 1:
__lowerCAmelCase : Optional[int] = self.out_layers[0][0][l_idx:r_idx]
__lowerCAmelCase : List[str] = self.out_layers[0][1][l_idx:r_idx]
else:
__lowerCAmelCase : List[str] = self.out_layers[i][0]
__lowerCAmelCase : int = self.out_layers[i][1]
if i == 0:
__lowerCAmelCase : str = tf.concat([cur_W, self.cluster_weight] , 0 )
__lowerCAmelCase : Dict = tf.concat([cur_b, self.cluster_bias] , 0 )
__lowerCAmelCase : List[str] = self._logit(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , self.out_projs[0] )
__lowerCAmelCase : int = tf.nn.log_softmax(__UpperCamelCase )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
__lowerCAmelCase : Dict = tf.boolean_mask(__UpperCamelCase , __UpperCamelCase )
__lowerCAmelCase : Optional[Any] = self._gather_logprob(__UpperCamelCase , __UpperCamelCase )
else:
__lowerCAmelCase : int = self._logit(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , self.out_projs[i] )
__lowerCAmelCase : Tuple = tf.nn.log_softmax(__UpperCamelCase )
__lowerCAmelCase : List[str] = self.cutoffs[0] + i - 1 # No probability for the head cluster
__lowerCAmelCase : Tuple = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__UpperCamelCase )
if target is not None:
__lowerCAmelCase : str = tf.boolean_mask(__UpperCamelCase , __UpperCamelCase )
__lowerCAmelCase : int = tf.boolean_mask(__UpperCamelCase , __UpperCamelCase )
__lowerCAmelCase : str = self._gather_logprob(__UpperCamelCase , __UpperCamelCase )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__UpperCamelCase , -cur_logprob , shape_list(__UpperCamelCase ) )
__lowerCAmelCase : List[str] = tf.concat(__UpperCamelCase , axis=-1 )
if target is not None:
if return_mean:
__lowerCAmelCase : Optional[int] = tf.reduce_mean(__UpperCamelCase )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__UpperCamelCase )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__UpperCamelCase , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 492 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
__A : Any = True
except (ImportError, AttributeError):
__A : str = object
def lowercase ( *_SCREAMING_SNAKE_CASE : int , **_SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
pass
__A : Any = False
__A : Optional[int] = logging.get_logger("transformers-cli/serving")
def lowercase ( _SCREAMING_SNAKE_CASE : Namespace ):
'''simple docstring'''
_UpperCAmelCase = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(_SCREAMING_SNAKE_CASE , args.host , args.port , args.workers )
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
class _a ( lowerCAmelCase):
"""simple docstring"""
@staticmethod
def lowercase__ ( __UpperCamelCase : ArgumentParser )->List[str]:
_UpperCAmelCase = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=__UpperCamelCase , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=__UpperCamelCase , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=__UpperCamelCase , default=8_8_8_8 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=__UpperCamelCase , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=__UpperCamelCase , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=__UpperCamelCase , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=__UpperCamelCase , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=__UpperCamelCase , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=__UpperCamelCase )
def __init__( self : int , __UpperCamelCase : Pipeline , __UpperCamelCase : str , __UpperCamelCase : int , __UpperCamelCase : int )->Any:
_UpperCAmelCase = pipeline
_UpperCAmelCase = host
_UpperCAmelCase = port
_UpperCAmelCase = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F'Serving model over {host}:{port}' )
_UpperCAmelCase = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=__UpperCamelCase , response_class=__UpperCamelCase , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=__UpperCamelCase , response_class=__UpperCamelCase , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=__UpperCamelCase , response_class=__UpperCamelCase , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=__UpperCamelCase , response_class=__UpperCamelCase , methods=['''POST'''] , ),
] , timeout=6_0_0 , )
def lowercase__ ( self : Optional[int] )->Union[str, Any]:
run(self._app , host=self.host , port=self.port , workers=self.workers )
def lowercase__ ( self : int )->int:
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def lowercase__ ( self : List[Any] , __UpperCamelCase : str = Body(__UpperCamelCase , embed=__UpperCamelCase ) , __UpperCamelCase : bool = Body(__UpperCamelCase , embed=__UpperCamelCase ) )->Any:
try:
_UpperCAmelCase = self._pipeline.tokenizer.tokenize(__UpperCamelCase )
if return_ids:
_UpperCAmelCase = self._pipeline.tokenizer.convert_tokens_to_ids(__UpperCamelCase )
return ServeTokenizeResult(tokens=__UpperCamelCase , tokens_ids=__UpperCamelCase )
else:
return ServeTokenizeResult(tokens=__UpperCamelCase )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(__UpperCamelCase )} )
def lowercase__ ( self : Union[str, Any] , __UpperCamelCase : List[int] = Body(__UpperCamelCase , embed=__UpperCamelCase ) , __UpperCamelCase : bool = Body(__UpperCamelCase , embed=__UpperCamelCase ) , __UpperCamelCase : bool = Body(__UpperCamelCase , embed=__UpperCamelCase ) , )->List[str]:
try:
_UpperCAmelCase = self._pipeline.tokenizer.decode(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return ServeDeTokenizeResult(model='''''' , text=__UpperCamelCase )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(__UpperCamelCase )} )
async def lowercase__ ( self : int , __UpperCamelCase : List[Any]=Body(__UpperCamelCase , embed=__UpperCamelCase ) )->Tuple:
# Check we don't have empty string
if len(__UpperCamelCase ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
_UpperCAmelCase = self._pipeline(__UpperCamelCase )
return ServeForwardResult(output=__UpperCamelCase )
except Exception as e:
raise HTTPException(5_0_0 , {'''error''': str(__UpperCamelCase )} )
| 602 | 0 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
A : Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(__UpperCAmelCase )
class lowerCamelCase ( __UpperCAmelCase ):
def __init__( self : Optional[Any] , *__snake_case : Optional[int] , **__snake_case : Any ):
'''simple docstring'''
super().__init__(*__snake_case , **__snake_case )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , __snake_case : Optional[int]=None , __snake_case : Optional[int]=None , __snake_case : Any=None ):
'''simple docstring'''
_snake_case: Optional[Any] = {}
_snake_case: Optional[int] = {}
if prompt is not None:
_snake_case: Any = prompt
if generate_kwargs is not None:
_snake_case: Optional[int] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
_snake_case: str = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
' please use only one' )
_snake_case: List[Any] = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[str] , __snake_case : Union[str, List[str], "Image.Image", List["Image.Image"]] , **__snake_case : Tuple ):
'''simple docstring'''
return super().__call__(__snake_case , **__snake_case )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , __snake_case : List[Any] , __snake_case : Any=None ):
'''simple docstring'''
_snake_case: Optional[Any] = load_image(__snake_case )
if prompt is not None:
if not isinstance(__snake_case , __snake_case ):
raise ValueError(
f'''Received an invalid text input, got - {type(__snake_case )} - but expected a single string. '''
'Note also that one single text can be provided for conditional image to text generation.' )
_snake_case: List[Any] = self.model.config.model_type
if model_type == "git":
_snake_case: Any = self.image_processor(images=__snake_case , return_tensors=self.framework )
_snake_case: Tuple = self.tokenizer(text=__snake_case , add_special_tokens=__snake_case ).input_ids
_snake_case: Optional[int] = [self.tokenizer.cls_token_id] + input_ids
_snake_case: Optional[int] = torch.tensor(__snake_case ).unsqueeze(0 )
model_inputs.update({'input_ids': input_ids} )
elif model_type == "pix2struct":
_snake_case: Dict = self.image_processor(images=__snake_case , header_text=__snake_case , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
_snake_case: List[str] = self.image_processor(images=__snake_case , return_tensors=self.framework )
_snake_case: List[Any] = self.tokenizer(__snake_case , return_tensors=self.framework )
model_inputs.update(__snake_case )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
_snake_case: Optional[int] = self.image_processor(images=__snake_case , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
_snake_case: Tuple = None
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , __snake_case : Optional[Any] , __snake_case : Any=None ):
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs['input_ids'] , __snake_case )
and all(x is None for x in model_inputs['input_ids'] )
):
_snake_case: Union[str, Any] = None
if generate_kwargs is None:
_snake_case: Optional[int] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
_snake_case: List[Any] = model_inputs.pop(self.model.main_input_name )
_snake_case: Optional[Any] = self.model.generate(__snake_case , **__snake_case , **__snake_case )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : int , __snake_case : List[str] ):
'''simple docstring'''
_snake_case: List[Any] = []
for output_ids in model_outputs:
_snake_case: List[str] = {
'generated_text': self.tokenizer.decode(
__snake_case , skip_special_tokens=__snake_case , )
}
records.append(__snake_case )
return records
| 273 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
A : Dict = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'
A : int = '\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results[\'matthews_correlation\'], 2))\n -0.25\n'
A : Dict = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase ( datasets.Metric ):
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=[
'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
] , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , __snake_case : int , __snake_case : List[Any] , __snake_case : Union[str, Any]=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ),
}
| 273 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __magic_name__ ( ):
'''simple docstring'''
lowerCamelCase_ : Any = HfArgumentParser(__lowerCAmelCase)
lowerCamelCase_ : str = parser.parse_args_into_dataclasses()[0]
lowerCamelCase_ : Union[str, Any] = TensorFlowBenchmark(args=__lowerCAmelCase)
try:
lowerCamelCase_ : Optional[Any] = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCamelCase_ : List[Any] = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
lowerCamelCase_ : Optional[int] = ''' '''.join(str(__lowerCAmelCase).split(" ")[:-1])
lowerCamelCase_ : str = ''''''
lowerCamelCase_ : str = eval(str(__lowerCAmelCase).split(" ")[-1])
lowerCamelCase_ : Dict = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(__lowerCAmelCase)
if len(__lowerCAmelCase) > 0:
lowerCamelCase_ : str = full_error_msg + begin_error_msg + str(__lowerCAmelCase)
raise ValueError(__lowerCAmelCase)
benchmark.run()
if __name__ == "__main__":
main()
| 250 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__lowerCAmelCase : Dict = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.76,
"M": 2.41,
"W": 2.36,
"F": 2.23,
"G": 2.02,
"Y": 1.97,
"P": 1.93,
"B": 1.29,
"V": 0.98,
"K": 0.77,
"J": 0.15,
"X": 0.15,
"Q": 0.10,
"Z": 0.07,
}
__lowerCAmelCase : Tuple = "ETAOINSHRDLCUMWFGYPBVKJXQZ"
__lowerCAmelCase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def UpperCAmelCase_ ( __lowerCAmelCase ) -> dict[str, int]:
__lowercase : int = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def UpperCAmelCase_ ( __lowerCAmelCase ) -> str:
return x[0]
def UpperCAmelCase_ ( __lowerCAmelCase ) -> str:
__lowercase : Dict = get_letter_count(__lowerCAmelCase )
__lowercase : dict[int, list[str]] = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(__lowerCAmelCase )
__lowercase : dict[int, str] = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=__lowerCAmelCase )
__lowercase : Tuple = ''''''.join(freq_to_letter[freq] )
__lowercase : Dict = list(freq_to_letter_str.items() )
freq_pairs.sort(key=__lowerCAmelCase , reverse=__lowerCAmelCase )
__lowercase : list[str] = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(__lowerCAmelCase )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
__lowercase : Any = get_frequency_order(__lowerCAmelCase )
__lowercase : Any = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 509 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
_snake_case = logging.get_logger(__name__)
_snake_case = OrderedDict(
[
("""align""", """EfficientNetImageProcessor"""),
("""beit""", """BeitImageProcessor"""),
("""bit""", """BitImageProcessor"""),
("""blip""", """BlipImageProcessor"""),
("""blip-2""", """BlipImageProcessor"""),
("""bridgetower""", """BridgeTowerImageProcessor"""),
("""chinese_clip""", """ChineseCLIPImageProcessor"""),
("""clip""", """CLIPImageProcessor"""),
("""clipseg""", """ViTImageProcessor"""),
("""conditional_detr""", """ConditionalDetrImageProcessor"""),
("""convnext""", """ConvNextImageProcessor"""),
("""convnextv2""", """ConvNextImageProcessor"""),
("""cvt""", """ConvNextImageProcessor"""),
("""data2vec-vision""", """BeitImageProcessor"""),
("""deformable_detr""", """DeformableDetrImageProcessor"""),
("""deit""", """DeiTImageProcessor"""),
("""deta""", """DetaImageProcessor"""),
("""detr""", """DetrImageProcessor"""),
("""dinat""", """ViTImageProcessor"""),
("""donut-swin""", """DonutImageProcessor"""),
("""dpt""", """DPTImageProcessor"""),
("""efficientformer""", """EfficientFormerImageProcessor"""),
("""efficientnet""", """EfficientNetImageProcessor"""),
("""flava""", """FlavaImageProcessor"""),
("""focalnet""", """BitImageProcessor"""),
("""git""", """CLIPImageProcessor"""),
("""glpn""", """GLPNImageProcessor"""),
("""groupvit""", """CLIPImageProcessor"""),
("""imagegpt""", """ImageGPTImageProcessor"""),
("""instructblip""", """BlipImageProcessor"""),
("""layoutlmv2""", """LayoutLMv2ImageProcessor"""),
("""layoutlmv3""", """LayoutLMv3ImageProcessor"""),
("""levit""", """LevitImageProcessor"""),
("""mask2former""", """Mask2FormerImageProcessor"""),
("""maskformer""", """MaskFormerImageProcessor"""),
("""mgp-str""", """ViTImageProcessor"""),
("""mobilenet_v1""", """MobileNetV1ImageProcessor"""),
("""mobilenet_v2""", """MobileNetV2ImageProcessor"""),
("""mobilevit""", """MobileViTImageProcessor"""),
("""mobilevit""", """MobileViTImageProcessor"""),
("""mobilevitv2""", """MobileViTImageProcessor"""),
("""nat""", """ViTImageProcessor"""),
("""oneformer""", """OneFormerImageProcessor"""),
("""owlvit""", """OwlViTImageProcessor"""),
("""perceiver""", """PerceiverImageProcessor"""),
("""pix2struct""", """Pix2StructImageProcessor"""),
("""poolformer""", """PoolFormerImageProcessor"""),
("""regnet""", """ConvNextImageProcessor"""),
("""resnet""", """ConvNextImageProcessor"""),
("""sam""", """SamImageProcessor"""),
("""segformer""", """SegformerImageProcessor"""),
("""swiftformer""", """ViTImageProcessor"""),
("""swin""", """ViTImageProcessor"""),
("""swin2sr""", """Swin2SRImageProcessor"""),
("""swinv2""", """ViTImageProcessor"""),
("""table-transformer""", """DetrImageProcessor"""),
("""timesformer""", """VideoMAEImageProcessor"""),
("""tvlt""", """TvltImageProcessor"""),
("""upernet""", """SegformerImageProcessor"""),
("""van""", """ConvNextImageProcessor"""),
("""videomae""", """VideoMAEImageProcessor"""),
("""vilt""", """ViltImageProcessor"""),
("""vit""", """ViTImageProcessor"""),
("""vit_hybrid""", """ViTHybridImageProcessor"""),
("""vit_mae""", """ViTImageProcessor"""),
("""vit_msn""", """ViTImageProcessor"""),
("""xclip""", """CLIPImageProcessor"""),
("""yolos""", """YolosImageProcessor"""),
]
)
_snake_case = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def _A ( __magic_name__ ):
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
lowercase__ = model_type_to_module_name(__magic_name__ )
lowercase__ = importlib.import_module(f'''.{module_name}''' , "transformers.models" )
try:
return getattr(__magic_name__ , __magic_name__ )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(__magic_name__ , "__name__" , __magic_name__ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowercase__ = importlib.import_module("transformers" )
if hasattr(__magic_name__ , __magic_name__ ):
return getattr(__magic_name__ , __magic_name__ )
return None
def _A ( __magic_name__ , __magic_name__ = None , __magic_name__ = False , __magic_name__ = False , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = False , **__magic_name__ , ):
lowercase__ = get_file_from_repo(
__magic_name__ , __magic_name__ , cache_dir=__magic_name__ , force_download=__magic_name__ , resume_download=__magic_name__ , proxies=__magic_name__ , use_auth_token=__magic_name__ , revision=__magic_name__ , local_files_only=__magic_name__ , )
if resolved_config_file is None:
logger.info(
"Could not locate the image processor configuration file, will try to use the model config instead." )
return {}
with open(__magic_name__ , encoding="utf-8" ) as reader:
return json.load(__magic_name__ )
class lowerCAmelCase :
def __init__( self :List[Any] ):
'''simple docstring'''
raise EnvironmentError(
"AutoImageProcessor is designed to be instantiated "
"using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method." )
@classmethod
@replace_list_option_in_docstrings(_lowercase )
def UpperCAmelCase ( cls :Tuple , _lowercase :Any , **_lowercase :Union[str, Any] ):
'''simple docstring'''
lowercase__ = kwargs.pop("config" , _lowercase )
lowercase__ = kwargs.pop("trust_remote_code" , _lowercase )
lowercase__ = True
lowercase__ , lowercase__ = ImageProcessingMixin.get_image_processor_dict(_lowercase , **_lowercase )
lowercase__ = config_dict.get("image_processor_type" , _lowercase )
lowercase__ = None
if "AutoImageProcessor" in config_dict.get("auto_map" , {} ):
lowercase__ = config_dict["auto_map"]["AutoImageProcessor"]
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
lowercase__ = config_dict.pop("feature_extractor_type" , _lowercase )
if feature_extractor_class is not None:
logger.warning(
"Could not find image processor class in the image processor config or the model config. Loading"
" based on pattern matching with the model's feature extractor configuration." )
lowercase__ = feature_extractor_class.replace("FeatureExtractor" , "ImageProcessor" )
if "AutoFeatureExtractor" in config_dict.get("auto_map" , {} ):
lowercase__ = config_dict["auto_map"]["AutoFeatureExtractor"]
lowercase__ = feature_extractor_auto_map.replace("FeatureExtractor" , "ImageProcessor" )
logger.warning(
"Could not find image processor auto map in the image processor config or the model config."
" Loading based on pattern matching with the model's feature extractor configuration." )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(_lowercase , _lowercase ):
lowercase__ = AutoConfig.from_pretrained(_lowercase , **_lowercase )
# It could be in `config.image_processor_type``
lowercase__ = getattr(_lowercase , "image_processor_type" , _lowercase )
if hasattr(_lowercase , "auto_map" ) and "AutoImageProcessor" in config.auto_map:
lowercase__ = config.auto_map["AutoImageProcessor"]
if image_processor_class is not None:
lowercase__ = image_processor_class_from_name(_lowercase )
lowercase__ = image_processor_auto_map is not None
lowercase__ = image_processor_class is not None or type(_lowercase ) in IMAGE_PROCESSOR_MAPPING
lowercase__ = resolve_trust_remote_code(
_lowercase , _lowercase , _lowercase , _lowercase )
if has_remote_code and trust_remote_code:
lowercase__ = get_class_from_dynamic_module(
_lowercase , _lowercase , **_lowercase )
lowercase__ = kwargs.pop("code_revision" , _lowercase )
if os.path.isdir(_lowercase ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(_lowercase , **_lowercase )
elif image_processor_class is not None:
return image_processor_class.from_dict(_lowercase , **_lowercase )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(_lowercase ) in IMAGE_PROCESSOR_MAPPING:
lowercase__ = IMAGE_PROCESSOR_MAPPING[type(_lowercase )]
return image_processor_class.from_dict(_lowercase , **_lowercase )
raise ValueError(
f'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
f'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def UpperCAmelCase ( _lowercase :Optional[int] , _lowercase :Dict ):
'''simple docstring'''
IMAGE_PROCESSOR_MAPPING.register(_lowercase , _lowercase )
| 611 |
# Algorithm for the pigeonhole sorting
def _A ( __magic_name__ ):
lowercase__ = min(__magic_name__ ) # min() finds the minimum value
lowercase__ = max(__magic_name__ ) # max() finds the maximum value
lowercase__ = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
lowercase__ = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__magic_name__ , __magic_name__ ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
lowercase__ = 0
for count in range(__magic_name__ ):
while holes[count] > 0:
holes[count] -= 1
lowercase__ = count + min_val
i += 1
def _A ( ):
lowercase__ = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__magic_name__ )
print("Sorted order is:" , " ".join(__magic_name__ ) )
if __name__ == "__main__":
main()
| 611 | 1 |
from __future__ import annotations
def lowercase ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative in a semiconductor""" )
elif hole_conc < 0:
raise ValueError("""Hole concentration cannot be negative in a semiconductor""" )
elif intrinsic_conc < 0:
raise ValueError(
"""Intrinsic concentration cannot be negative in a semiconductor""" )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 477 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class snake_case ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
'''simple docstring'''
snake_case_ : Optional[int] = IFImgaImgSuperResolutionPipeline
snake_case_ : int = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
snake_case_ : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""} )
snake_case_ : List[str] = PipelineTesterMixin.required_optional_params - {"""latents"""}
def UpperCamelCase_ ( self : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def UpperCamelCase_ ( self : List[Any] , lowerCAmelCase : List[Any] , lowerCAmelCase : List[str]=0) -> Optional[Any]:
"""simple docstring"""
if str(lowerCAmelCase).startswith("""mps"""):
_snake_case : Optional[Any] = torch.manual_seed(lowerCAmelCase)
else:
_snake_case : List[str] = torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)
_snake_case : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
_snake_case : str = floats_tensor((1, 3, 16, 16) , rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
_snake_case : str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""original_image""": original_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCamelCase_ ( self : str) -> Dict:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
def UpperCamelCase_ ( self : int) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""")
def UpperCamelCase_ ( self : Tuple) -> Optional[int]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1)
def UpperCamelCase_ ( self : Tuple) -> Dict:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
def UpperCamelCase_ ( self : Dict) -> Optional[int]:
"""simple docstring"""
self._test_save_load_local()
def UpperCamelCase_ ( self : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 477 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowerCamelCase__ ( unittest.TestCase ):
__UpperCAmelCase = inspect.getfile(accelerate.test_utils )
__UpperCAmelCase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_cli.py"""] )
__UpperCAmelCase = ['''accelerate''', '''launch''']
__UpperCAmelCase = Path.home() / '''.cache/huggingface/accelerate'''
__UpperCAmelCase = '''default_config.yaml'''
__UpperCAmelCase = config_folder / config_file
__UpperCAmelCase = config_folder / '''_default_config.yaml'''
__UpperCAmelCase = Path("""tests/test_configs""" )
@classmethod
def _UpperCamelCase ( cls ) -> str:
"""simple docstring"""
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def _UpperCamelCase ( cls ) -> str:
"""simple docstring"""
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
_UpperCamelCase :Optional[int] =self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
for config in sorted(self.test_config_path.glob("""**/*.yaml""" ) ):
with self.subTest(config_file=_A ):
execute_subprocess_async(
self.base_cmd + ["""--config_file""", str(_A ), self.test_file_path] , env=os.environ.copy() )
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
execute_subprocess_async(["""accelerate""", """test"""] , env=os.environ.copy() )
class lowerCamelCase__ ( unittest.TestCase ):
__UpperCAmelCase = '''test-tpu'''
__UpperCAmelCase = '''us-central1-a'''
__UpperCAmelCase = '''ls'''
__UpperCAmelCase = ['''accelerate''', '''tpu-config''']
__UpperCAmelCase = '''cd /usr/share'''
__UpperCAmelCase = '''tests/test_samples/test_command_file.sh'''
__UpperCAmelCase = '''Running gcloud compute tpus tpu-vm ssh'''
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase :Any =run_command(
self.cmd
+ ["""--command""", self.command, """--tpu_zone""", self.tpu_zone, """--tpu_name""", self.tpu_name, """--debug"""] , return_stdout=_A , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , _A , )
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
_UpperCamelCase :Optional[int] =run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command""",
self.command,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] , return_stdout=_A , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , _A , )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
_UpperCamelCase :Tuple =run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--debug"""] , return_stdout=_A )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all''' , _A , )
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase :List[str] =run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command""", self.command, """--debug"""] , return_stdout=_A , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , _A , )
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Tuple =run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--command""",
self.command,
"""--command""",
"""echo \"Hello World\"""",
"""--debug""",
] , return_stdout=_A , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all''' , _A , )
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
_UpperCamelCase :Optional[int] =run_command(
self.cmd
+ ["""--config_file""", """tests/test_configs/latest.yaml""", """--command_file""", self.command_file, """--debug"""] , return_stdout=_A , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all''' , _A , )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
_UpperCamelCase :Optional[Any] =run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command_file""",
self.command_file,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] , return_stdout=_A , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all''' , _A , )
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
_UpperCamelCase :Dict =run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--install_accelerate""", """--debug"""] , return_stdout=_A , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all''' , _A , )
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
_UpperCamelCase :Any =run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--install_accelerate""",
"""--accelerate_version""",
"""12.0.0""",
"""--debug""",
] , return_stdout=_A , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all''' , _A , )
| 711 | '''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class lowerCamelCase__ ( unittest.TestCase ):
__UpperCAmelCase = StableDiffusionLDMaDPipeline
__UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
__UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
__UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCamelCase :Tuple =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
_UpperCamelCase :Optional[Any] =DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=lowerCAmelCase__ , set_alpha_to_one=lowerCAmelCase__ , )
torch.manual_seed(0 )
_UpperCamelCase :Tuple =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=6 , out_channels=6 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_UpperCamelCase :int =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=1_000 , )
_UpperCamelCase :List[str] =CLIPTextModel(lowerCAmelCase__ )
_UpperCamelCase :List[Any] =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_UpperCamelCase :List[str] ={
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _UpperCamelCase ( self , lowerCAmelCase__ , lowerCAmelCase__=0 ) -> Tuple:
"""simple docstring"""
if str(lowerCAmelCase__ ).startswith("""mps""" ):
_UpperCamelCase :str =torch.manual_seed(lowerCAmelCase__ )
else:
_UpperCamelCase :Union[str, Any] =torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
_UpperCamelCase :str ={
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase :str ="""cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase :List[Any] =self.get_dummy_components()
_UpperCamelCase :List[Any] =StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
_UpperCamelCase :List[str] =ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase :Union[str, Any] =self.get_dummy_inputs(lowerCAmelCase__ )
_UpperCamelCase :Any =ldmad_pipe(**lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase :Tuple =output.rgb, output.depth
_UpperCamelCase :List[Any] =rgb[0, -3:, -3:, -1]
_UpperCamelCase :Union[str, Any] =depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
_UpperCamelCase :Union[str, Any] =np.array(
[0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262] )
_UpperCamelCase :Optional[int] =np.array([103.4_6727, 85.81_2004, 87.84_9236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def _UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase :List[Any] =self.get_dummy_components()
_UpperCamelCase :int =StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
_UpperCamelCase :List[str] =ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase :Dict =self.get_dummy_inputs(lowerCAmelCase__ )
_UpperCamelCase :Optional[int] =3 * [inputs["""prompt"""]]
# forward
_UpperCamelCase :List[str] =ldmad_pipe(**lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase :Tuple =output.rgb, output.depth
_UpperCamelCase :Any =rgb_slice_a[0, -3:, -3:, -1]
_UpperCamelCase :Optional[Any] =depth_slice_a[0, -3:, -1]
_UpperCamelCase :Tuple =self.get_dummy_inputs(lowerCAmelCase__ )
_UpperCamelCase :List[Any] =3 * [inputs.pop("""prompt""" )]
_UpperCamelCase :str =ldmad_pipe.tokenizer(
lowerCAmelCase__ , padding="""max_length""" , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=lowerCAmelCase__ , return_tensors="""pt""" , )
_UpperCamelCase :List[Any] =text_inputs["""input_ids"""].to(lowerCAmelCase__ )
_UpperCamelCase :Dict =ldmad_pipe.text_encoder(lowerCAmelCase__ )[0]
_UpperCamelCase :Dict =prompt_embeds
# forward
_UpperCamelCase :Tuple =ldmad_pipe(**lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase :Any =output.rgb, output.depth
_UpperCamelCase :Optional[int] =rgb_slice_a[0, -3:, -3:, -1]
_UpperCamelCase :List[Any] =depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :int ="""cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase :Tuple =self.get_dummy_components()
_UpperCamelCase :str =PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
_UpperCamelCase :List[Any] =StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
_UpperCamelCase :int =ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase :str =self.get_dummy_inputs(lowerCAmelCase__ )
_UpperCamelCase :Union[str, Any] ="""french fries"""
_UpperCamelCase :Any =ldmad_pipe(**lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase :Tuple =output.rgb, output.depth
_UpperCamelCase :List[str] =rgb[0, -3:, -3:, -1]
_UpperCamelCase :Any =depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
_UpperCamelCase :Union[str, Any] =np.array(
[0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217] )
_UpperCamelCase :List[str] =np.array([107.8_4738, 84.6_2802, 89.96_2135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCamelCase ( self , lowerCAmelCase__ , lowerCAmelCase__="cpu" , lowerCAmelCase__=torch.floataa , lowerCAmelCase__=0 ) -> Tuple:
"""simple docstring"""
_UpperCamelCase :Union[str, Any] =torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
_UpperCamelCase :Any =np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
_UpperCamelCase :int =torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ )
_UpperCamelCase :Any ={
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Optional[Any] =StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d""" )
_UpperCamelCase :Union[str, Any] =ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase :Any =self.get_inputs(lowerCAmelCase__ )
_UpperCamelCase :Tuple =ldmad_pipe(**lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase :Any =output.rgb, output.depth
_UpperCamelCase :Tuple =rgb[0, -3:, -3:, -1].flatten()
_UpperCamelCase :Optional[int] =rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
_UpperCamelCase :Any =np.array(
[0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706] )
_UpperCamelCase :int =np.array(
[0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCamelCase ( self , lowerCAmelCase__ , lowerCAmelCase__="cpu" , lowerCAmelCase__=torch.floataa , lowerCAmelCase__=0 ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :Dict =torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
_UpperCamelCase :List[Any] =np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
_UpperCamelCase :Tuple =torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ )
_UpperCamelCase :int ={
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 50,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase :Union[str, Any] =StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d""" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase :int =self.get_inputs(lowerCAmelCase__ )
_UpperCamelCase :Any =ldmad_pipe(**lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase :Optional[int] =output.rgb, output.depth
_UpperCamelCase :Tuple =0.49_5586
_UpperCamelCase :List[Any] =0.3379_5515
_UpperCamelCase :List[Any] =112.4_8518
_UpperCamelCase :str =98.48_9746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
_UpperCamelCase :int =StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c""" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase :Optional[Any] =self.get_inputs(lowerCAmelCase__ )
_UpperCamelCase :Dict =ldmad_pipe(**lowerCAmelCase__ )
_UpperCamelCase , _UpperCamelCase :Optional[int] =output.rgb, output.depth
_UpperCamelCase :Optional[Any] =0.419_4127
_UpperCamelCase :Optional[Any] =0.3537_5586
_UpperCamelCase :Any =0.563_8502
_UpperCamelCase :Tuple =0.3468_6103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3 | 512 | 0 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = '''▁'''
lowerCAmelCase__ = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class snake_case__(_UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowercase_ = BertGenerationTokenizer
lowercase_ = False
lowercase_ = True
def snake_case ( self : Dict ):
super().setUp()
lowercase__ : Optional[Any] = BertGenerationTokenizer(SCREAMING_SNAKE_CASE , keep_accents=SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case ( self : str ):
lowercase__ : List[str] = "<s>"
lowercase__ : int = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
def snake_case ( self : Any ):
lowercase__ : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "<pad>" )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 1_002 )
def snake_case ( self : Union[str, Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def snake_case ( self : str ):
lowercase__ : Tuple = BertGenerationTokenizer(SCREAMING_SNAKE_CASE , keep_accents=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = tokenizer.tokenize("This is a test" )
self.assertListEqual(SCREAMING_SNAKE_CASE , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE ) , [285, 46, 10, 170, 382] , )
lowercase__ : Optional[int] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
lowercase__ : Tuple = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE )
self.assertListEqual(
SCREAMING_SNAKE_CASE , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
lowercase__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE )
self.assertListEqual(
SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def snake_case ( self : int ):
return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
@slow
def snake_case ( self : str ):
lowercase__ : Union[str, Any] = "Hello World!"
lowercase__ : Optional[Any] = [18_536, 2_260, 101]
self.assertListEqual(SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE ) )
@slow
def snake_case ( self : Union[str, Any] ):
lowercase__ : Tuple = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
lowercase__ : Optional[int] = [
871,
419,
358,
946,
991,
2_521,
452,
358,
1_357,
387,
7_751,
3_536,
112,
985,
456,
126,
865,
938,
5_400,
5_734,
458,
1_368,
467,
786,
2_462,
5_246,
1_159,
633,
865,
4_519,
457,
582,
852,
2_557,
427,
916,
508,
405,
34_324,
497,
391,
408,
11_342,
1_244,
385,
100,
938,
985,
456,
574,
362,
12_597,
3_200,
3_129,
1_172,
]
self.assertListEqual(SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE ) )
@require_torch
@slow
def snake_case ( self : int ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
lowercase__ : Any = list(self.big_tokenizer.get_vocab().keys() )[:10]
lowercase__ : List[Any] = " ".join(SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE , return_tensors="pt" , return_token_type_ids=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence] , return_tensors="pt" , return_token_type_ids=SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = BertGenerationConfig()
lowercase__ : str = BertGenerationEncoder(SCREAMING_SNAKE_CASE )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**SCREAMING_SNAKE_CASE )
model(**SCREAMING_SNAKE_CASE )
@slow
def snake_case ( self : Tuple ):
# fmt: off
lowercase__ : str = {"input_ids": [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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], [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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE , model_name="google/bert_for_seq_generation_L-24_bbc_encoder" , revision="c817d1fd1be2ffa69431227a1fe320544943d4db" , )
| 496 |
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 __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Dict = []
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 __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : 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 __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Tuple = []
token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token") )
return token
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : List[Any] = []
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 __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : List[Any] = "imagenet-1k-id2label.json"
lowercase__ : Any = 1_000
lowercase__ : Union[str, Any] = "huggingface/label-files"
lowercase__ : Dict = num_labels
lowercase__ : Optional[int] = json.load(open(cached_download(hf_hub_url(lowerCamelCase__ , lowerCamelCase__ , repo_type="dataset" ) ) , "r" ) )
lowercase__ : List[str] = {int(lowerCamelCase__ ): v for k, v in idalabel.items()}
lowercase__ : str = idalabel
lowercase__ : Optional[int] = {v: k for k, v in idalabel.items()}
lowercase__ : str = 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__ : Union[str, Any] = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("/" , 1 )[-1][4:6] == "21":
lowercase__ : Optional[Any] = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
lowercase__ : str = [2, 2, 20]
lowercase__ : Optional[int] = [3, 12, 16]
lowercase__ : str = [192, 768, 1_024]
lowercase__ : str = CvtForImageClassification(lowerCamelCase__ )
lowercase__ : Tuple = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
lowercase__ : Dict = image_size
lowercase__ : Tuple = torch.load(lowerCamelCase__ , map_location=torch.device("cpu" ) )
lowercase__ : Any = OrderedDict()
lowercase__ : str = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
lowercase__ : Union[str, Any] = list_of_state_dict + cls_token(lowerCamelCase__ )
lowercase__ : str = list_of_state_dict + embeddings(lowerCamelCase__ )
for cnt in range(config.depth[idx] ):
lowercase__ : str = 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__ : Optional[Any] = 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__":
lowerCAmelCase__ = 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=3_8_4,
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.'''
)
lowerCAmelCase__ = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 496 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase , lowerCAmelCase=13 , lowerCAmelCase=32 , lowerCAmelCase=3 , lowerCAmelCase=4 , lowerCAmelCase=[10, 20, 30, 40] , lowerCAmelCase=[2, 2, 3, 2] , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=37 , lowerCAmelCase="gelu" , lowerCAmelCase=10 , lowerCAmelCase=0.02 , lowerCAmelCase=["stage2", "stage3", "stage4"] , lowerCAmelCase=3 , lowerCAmelCase=None , ) -> Dict:
'''simple docstring'''
_lowercase =parent
_lowercase =batch_size
_lowercase =image_size
_lowercase =num_channels
_lowercase =num_stages
_lowercase =hidden_sizes
_lowercase =depths
_lowercase =is_training
_lowercase =use_labels
_lowercase =intermediate_size
_lowercase =hidden_act
_lowercase =type_sequence_label_size
_lowercase =initializer_range
_lowercase =out_features
_lowercase =num_labels
_lowercase =scope
_lowercase =num_stages
def A__ ( self ) -> List[Any]:
'''simple docstring'''
_lowercase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowercase =None
if self.use_labels:
_lowercase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowercase =self.get_config()
return config, pixel_values, labels
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=512 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=lowerCAmelCase , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=256 , auxiliary_num_convs=1 , auxiliary_concat_input=lowerCAmelCase , loss_ignore_index=255 , num_labels=self.num_labels , )
def A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Any:
'''simple docstring'''
_lowercase =UperNetForSemanticSegmentation(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
_lowercase =model(lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
_lowercase =self.prepare_config_and_inputs()
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) =config_and_inputs
_lowercase ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
_a = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
_a = {"""image-segmentation""": UperNetForSemanticSegmentation} if is_torch_available() else {}
_a = False
_a = False
_a = False
_a = False
_a = False
_a = False
def A__ ( self ) -> List[str]:
'''simple docstring'''
_lowercase =UperNetModelTester(self )
_lowercase =ConfigTester(self , config_class=lowerCAmelCase , has_text_modality=lowerCAmelCase , hidden_size=37 )
def A__ ( self ) -> Tuple:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A__ ( self ) -> Dict:
'''simple docstring'''
return
def A__ ( self ) -> int:
'''simple docstring'''
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =model_class(lowerCAmelCase )
_lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase =[*signature.parameters.keys()]
_lowercase =['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCAmelCase )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase )
@unittest.skip(reason='UperNet does not use inputs_embeds' )
def A__ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='UperNet does not support input and output embeddings' )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='UperNet does not have a base model' )
def A__ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='UperNet does not have a base model' )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def A__ ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
def check_hidden_states_output(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
_lowercase =model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
with torch.no_grad():
_lowercase =model(**self._prepare_for_class(lowerCAmelCase , lowerCAmelCase ) )
_lowercase =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowercase =self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =True
check_hidden_states_output(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowercase =True
check_hidden_states_output(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
_lowercase =_config_zero_init(lowerCAmelCase )
_lowercase =_config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
_lowercase =model_class(config=lowerCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='UperNet does not have tied weights' )
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@slow
def A__ ( self ) -> int:
'''simple docstring'''
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase =UperNetForSemanticSegmentation.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def a ( ) -> Optional[Any]:
"""simple docstring"""
_lowercase =hf_hub_download(
repo_id='hf-internal-testing/fixtures_ade20k' , repo_type='dataset' , filename='ADE_val_00000001.jpg' )
_lowercase =Image.open(A__ ).convert('RGB' )
return image
@require_torch
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def A__ ( self ) -> List[str]:
'''simple docstring'''
_lowercase =AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny' )
_lowercase =UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny' ).to(lowerCAmelCase )
_lowercase =prepare_img()
_lowercase =processor(images=lowerCAmelCase , return_tensors='pt' ).to(lowerCAmelCase )
with torch.no_grad():
_lowercase =model(**lowerCAmelCase )
_lowercase =torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase )
_lowercase =torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] ).to(lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , lowerCAmelCase , atol=1e-4 ) )
def A__ ( self ) -> Any:
'''simple docstring'''
_lowercase =AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny' )
_lowercase =UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny' ).to(lowerCAmelCase )
_lowercase =prepare_img()
_lowercase =processor(images=lowerCAmelCase , return_tensors='pt' ).to(lowerCAmelCase )
with torch.no_grad():
_lowercase =model(**lowerCAmelCase )
_lowercase =torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase )
_lowercase =torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ).to(lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , lowerCAmelCase , atol=1e-4 ) )
| 380 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def A__ ( self ) -> List[str]:
'''simple docstring'''
_lowercase =0
_lowercase =[0]
_lowercase =[0]
_lowercase =len(lowerCAmelCase )
self.assertEqual(k.knapsack(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) , 0 )
_lowercase =[60]
_lowercase =[10]
_lowercase =len(lowerCAmelCase )
self.assertEqual(k.knapsack(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) , 0 )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
_lowercase =3
_lowercase =[1, 2, 3]
_lowercase =[3, 2, 1]
_lowercase =len(lowerCAmelCase )
self.assertEqual(k.knapsack(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) , 5 )
def A__ ( self ) -> str:
'''simple docstring'''
_lowercase =50
_lowercase =[60, 100, 120]
_lowercase =[10, 20, 30]
_lowercase =len(lowerCAmelCase )
self.assertEqual(k.knapsack(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) , 220 )
if __name__ == "__main__":
unittest.main()
| 380 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowercase = [
"""openmmlab/upernet-convnext-tiny""",
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowercase = """UperNetConfig"""
class __lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , _a : int , _a : int , _a : Union[int, Tuple[int, int]] , _a : Union[int, Tuple[int, int], str] = 0 , _a : bool = False , _a : Union[int, Tuple[int, int]] = 1 , ):
super().__init__()
UpperCamelCase__ = nn.Convad(
in_channels=_a , out_channels=_a , kernel_size=_a , padding=_a , bias=_a , dilation=_a , )
UpperCamelCase__ = nn.BatchNormad(_a )
UpperCamelCase__ = nn.ReLU()
def A_ ( self : Dict , _a : torch.Tensor ):
UpperCamelCase__ = self.conv(_a )
UpperCamelCase__ = self.batch_norm(_a )
UpperCamelCase__ = self.activation(_a )
return output
class __lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , _a : int , _a : int , _a : int ):
super().__init__()
UpperCamelCase__ = [
nn.AdaptiveAvgPoolad(_a ),
UperNetConvModule(_a , _a , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_a ) , _a )
def A_ ( self : Dict , _a : torch.Tensor ):
UpperCamelCase__ = input
for layer in self.layers:
UpperCamelCase__ = layer(_a )
return hidden_state
class __lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , _a : Tuple[int, ...] , _a : int , _a : int , _a : bool ):
super().__init__()
UpperCamelCase__ = pool_scales
UpperCamelCase__ = align_corners
UpperCamelCase__ = in_channels
UpperCamelCase__ = channels
UpperCamelCase__ = []
for i, pool_scale in enumerate(_a ):
UpperCamelCase__ = UperNetPyramidPoolingBlock(pool_scale=_a , in_channels=_a , channels=_a )
self.blocks.append(_a )
self.add_module(str(_a ) , _a )
def A_ ( self : List[str] , _a : torch.Tensor ):
UpperCamelCase__ = []
for ppm in self.blocks:
UpperCamelCase__ = ppm(_a )
UpperCamelCase__ = nn.functional.interpolate(
_a , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(_a )
return ppm_outs
class __lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , _a : Tuple , _a : List[Any] ):
super().__init__()
UpperCamelCase__ = config
UpperCamelCase__ = config.pool_scales # e.g. (1, 2, 3, 6)
UpperCamelCase__ = in_channels
UpperCamelCase__ = config.hidden_size
UpperCamelCase__ = False
UpperCamelCase__ = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
UpperCamelCase__ = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
UpperCamelCase__ = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
UpperCamelCase__ = nn.ModuleList()
UpperCamelCase__ = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
UpperCamelCase__ = UperNetConvModule(_a , self.channels , kernel_size=1 )
UpperCamelCase__ = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(_a )
self.fpn_convs.append(_a )
UpperCamelCase__ = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def A_ ( self : int ):
self.apply(self._init_weights )
def A_ ( self : str , _a : int ):
if isinstance(_a , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def A_ ( self : Tuple , _a : Dict ):
UpperCamelCase__ = inputs[-1]
UpperCamelCase__ = [x]
psp_outs.extend(self.psp_modules(_a ) )
UpperCamelCase__ = torch.cat(_a , dim=1 )
UpperCamelCase__ = self.bottleneck(_a )
return output
def A_ ( self : Optional[int] , _a : torch.Tensor ):
# build laterals
UpperCamelCase__ = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_a ) )
# build top-down path
UpperCamelCase__ = len(_a )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCamelCase__ = laterals[i - 1].shape[2:]
UpperCamelCase__ = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=_a , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
UpperCamelCase__ = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCamelCase__ = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
UpperCamelCase__ = torch.cat(_a , dim=1 )
UpperCamelCase__ = self.fpn_bottleneck(_a )
UpperCamelCase__ = self.classifier(_a )
return output
class __lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , _a : List[str] , _a : int = 2 , _a : int = 3 , _a : Union[int, Tuple[int, int]] = 1 ):
super().__init__()
UpperCamelCase__ = config
UpperCamelCase__ = config.auxiliary_in_channels
UpperCamelCase__ = config.auxiliary_channels
UpperCamelCase__ = config.auxiliary_num_convs
UpperCamelCase__ = config.auxiliary_concat_input
UpperCamelCase__ = in_index
UpperCamelCase__ = (kernel_size // 2) * dilation
UpperCamelCase__ = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=_a , padding=_a , dilation=_a ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=_a , padding=_a , dilation=_a ) )
if self.num_convs == 0:
UpperCamelCase__ = nn.Identity()
else:
UpperCamelCase__ = nn.Sequential(*_a )
if self.concat_input:
UpperCamelCase__ = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=_a , padding=kernel_size // 2 )
UpperCamelCase__ = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def A_ ( self : Tuple ):
self.apply(self._init_weights )
def A_ ( self : Tuple , _a : Optional[Any] ):
if isinstance(_a , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def A_ ( self : str , _a : torch.Tensor ):
# just take the relevant feature maps
UpperCamelCase__ = encoder_hidden_states[self.in_index]
UpperCamelCase__ = self.convs(_a )
if self.concat_input:
UpperCamelCase__ = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
UpperCamelCase__ = self.classifier(_a )
return output
class __lowercase ( A ):
'''simple docstring'''
_A : Optional[Any] = UperNetConfig
_A : List[str] = '''pixel_values'''
_A : List[Any] = True
def A_ ( self : List[str] , _a : Union[str, Any] ):
if isinstance(_a , _a ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def A_ ( self : Union[str, Any] ):
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def A_ ( self : Dict , _a : Optional[Any] , _a : Tuple=False ):
if isinstance(_a , _a ):
UpperCamelCase__ = value
lowercase = R"""
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
lowercase = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''', A, )
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : str , _a : Optional[Any] ):
super().__init__(_a )
UpperCamelCase__ = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
UpperCamelCase__ = UperNetHead(_a , in_channels=self.backbone.channels )
UpperCamelCase__ = UperNetFCNHead(_a ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=_a , config_class=_CONFIG_FOR_DOC )
def A_ ( self : Any , _a : Optional[torch.Tensor] = None , _a : Optional[bool] = None , _a : Optional[bool] = None , _a : Optional[torch.Tensor] = None , _a : Optional[bool] = None , ):
UpperCamelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCamelCase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCamelCase__ = output_attentions if output_attentions is not None else self.config.output_attentions
UpperCamelCase__ = self.backbone.forward_with_filtered_kwargs(
_a , output_hidden_states=_a , output_attentions=_a )
UpperCamelCase__ = outputs.feature_maps
UpperCamelCase__ = self.decode_head(_a )
UpperCamelCase__ = nn.functional.interpolate(_a , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=_a )
UpperCamelCase__ = None
if self.auxiliary_head is not None:
UpperCamelCase__ = self.auxiliary_head(_a )
UpperCamelCase__ = nn.functional.interpolate(
_a , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=_a )
UpperCamelCase__ = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
UpperCamelCase__ = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
UpperCamelCase__ = loss_fct(_a , _a )
UpperCamelCase__ = loss_fct(_a , _a )
UpperCamelCase__ = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
UpperCamelCase__ = (logits,) + outputs[1:]
else:
UpperCamelCase__ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_a , logits=_a , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 240 | from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCamelCase__ = 9
UpperCamelCase__ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
UpperCamelCase__ = kruskal(UpperCamelCase__, UpperCamelCase__ )
UpperCamelCase__ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(UpperCamelCase__ ) == sorted(UpperCamelCase__ )
| 240 | 1 |
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = ["image_processor", "tokenizer"]
lowercase = "AutoImageProcessor"
lowercase = "AutoTokenizer"
def __init__( self : List[str] , snake_case_ : Dict=None , snake_case_ : int=None , **snake_case_ : Tuple ):
snake_case__ : List[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , snake_case_ , )
snake_case__ : Optional[int] = kwargs.pop("""feature_extractor""" )
snake_case__ : Tuple = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(snake_case_ , snake_case_ )
snake_case__ : int = self.image_processor
snake_case__ : Tuple = False
def __call__( self : List[Any] , *snake_case_ : Any , **snake_case_ : Tuple ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_ , **snake_case_ )
snake_case__ : List[Any] = kwargs.pop("""images""" , snake_case_ )
snake_case__ : Optional[Any] = kwargs.pop("""text""" , snake_case_ )
if len(snake_case_ ) > 0:
snake_case__ : Optional[int] = args[0]
snake_case__ : Dict = args[1:]
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
snake_case__ : Dict = self.image_processor(snake_case_ , *snake_case_ , **snake_case_ )
if text is not None:
snake_case__ : str = self.tokenizer(snake_case_ , **snake_case_ )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case__ : Any = encodings["""input_ids"""]
return inputs
def lowerCamelCase ( self : Optional[int] , *snake_case_ : Any , **snake_case_ : List[str] ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowerCamelCase ( self : List[str] , *snake_case_ : Optional[int] , **snake_case_ : Dict ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@contextmanager
def lowerCamelCase ( self : List[str] ):
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your images inputs, or in a separate call.""" )
snake_case__ : Optional[int] = True
snake_case__ : int = self.tokenizer
yield
snake_case__ : Optional[Any] = self.image_processor
snake_case__ : Tuple = False
def lowerCamelCase ( self : Optional[Any] , snake_case_ : int , snake_case_ : Dict=False , snake_case_ : int=None ):
if added_vocab is None:
snake_case__ : Optional[int] = self.tokenizer.get_added_vocab()
snake_case__ : Tuple = {}
while tokens:
snake_case__ : Any = re.search(r"""<s_(.*?)>""" , snake_case_ , re.IGNORECASE )
if start_token is None:
break
snake_case__ : List[Any] = start_token.group(1 )
snake_case__ : Optional[Any] = re.search(rf"</s_{key}>" , snake_case_ , re.IGNORECASE )
snake_case__ : List[Any] = start_token.group()
if end_token is None:
snake_case__ : List[Any] = tokens.replace(snake_case_ , """""" )
else:
snake_case__ : Dict = end_token.group()
snake_case__ : Optional[int] = re.escape(snake_case_ )
snake_case__ : Dict = re.escape(snake_case_ )
snake_case__ : Optional[int] = re.search(f"{start_token_escaped}(.*?){end_token_escaped}" , snake_case_ , re.IGNORECASE )
if content is not None:
snake_case__ : Dict = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
snake_case__ : List[str] = self.tokenajson(snake_case_ , is_inner_value=snake_case_ , added_vocab=snake_case_ )
if value:
if len(snake_case_ ) == 1:
snake_case__ : Optional[Any] = value[0]
snake_case__ : str = value
else: # leaf nodes
snake_case__ : int = []
for leaf in content.split(r"""<sep/>""" ):
snake_case__ : List[str] = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
snake_case__ : Union[str, Any] = leaf[1:-2] # for categorical special tokens
output[key].append(snake_case_ )
if len(output[key] ) == 1:
snake_case__ : Any = output[key][0]
snake_case__ : Tuple = tokens[tokens.find(snake_case_ ) + len(snake_case_ ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=snake_case_ , added_vocab=snake_case_ )
if len(snake_case_ ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def lowerCamelCase ( self : Union[str, Any] ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , snake_case_ , )
return self.image_processor_class
@property
def lowerCamelCase ( self : Any ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , snake_case_ , )
return self.image_processor
| 715 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> list:
snake_case__ : List[str] = int(_lowerCAmelCase )
if n_element < 1:
snake_case__ : List[Any] = ValueError("""a should be a positive number""" )
raise my_error
snake_case__ : str = [1]
snake_case__ , snake_case__ , snake_case__ : Optional[int] = (0, 0, 0)
snake_case__ : Union[str, Any] = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
__a = input("Enter the last number (nth term) of the Hamming Number Series: ")
print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
__a = hamming(int(n))
print("-----------------------------------------------------")
print(F"The list with nth numbers is: {hamming_numbers}")
print("-----------------------------------------------------")
| 301 | 0 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE :Optional[int] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')
def _lowerCAmelCase ( lowerCAmelCase_ :np.ndarray , lowerCAmelCase_ :float , lowerCAmelCase_ :int = 16_000 )->Union[str, Any]:
'''simple docstring'''
snake_case_ = int(round(sample_rate * max_length ) )
if len(lowerCAmelCase_ ) <= sample_length:
return wav
snake_case_ = randint(0 , len(lowerCAmelCase_ ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
_SCREAMING_SNAKE_CASE = field(default=lowercase_ , metadata={'help': 'Name of a dataset from the datasets package'} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'A file containing the training audio paths and labels.'} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'A file containing the validation audio paths and labels.'} )
_SCREAMING_SNAKE_CASE = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
_SCREAMING_SNAKE_CASE = field(
default='validation' , metadata={
'help': (
'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
_SCREAMING_SNAKE_CASE = field(
default='audio' , metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''} , )
_SCREAMING_SNAKE_CASE = field(
default='label' , metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
_SCREAMING_SNAKE_CASE = field(
default=20 , metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'} , )
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
_SCREAMING_SNAKE_CASE = field(
default='facebook/wav2vec2-base' , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} , )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'} )
_SCREAMING_SNAKE_CASE = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'Name or path of preprocessor config.'} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'Whether to freeze the feature encoder layers of the model.'} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'Whether to generate an attention mask in the feature extractor.'} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
_SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def lowerCAmelCase__ ( self : Dict ) -> Dict:
"""simple docstring"""
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"The argument `--freeze_feature_extractor` is deprecated and "
"will be removed in a future version. Use `--freeze_feature_encoder`"
"instead. Setting `freeze_feature_encoder==True`." , a__ , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"The argument `--freeze_feature_extractor` is deprecated and "
"should not be used in combination with `--freeze_feature_encoder`."
"Only make use of `--freeze_feature_encoder`." )
def _lowerCAmelCase ( )->List[Any]:
'''simple docstring'''
snake_case_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_ , snake_case_ , snake_case_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_ , snake_case_ , snake_case_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_audio_classification" , lowerCAmelCase_ , lowerCAmelCase_ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ = training_args.get_process_log_level()
logger.setLevel(lowerCAmelCase_ )
transformers.utils.logging.set_verbosity(lowerCAmelCase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
snake_case_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"Use --overwrite_output_dir to train from scratch." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Initialize our dataset and prepare it for the audio classification task.
snake_case_ = DatasetDict()
snake_case_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F'''--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
"Make sure to set `--audio_column_name` to the correct audio column - one of "
F'''{', '.join(raw_datasets['train'].column_names )}.''' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F'''--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
"Make sure to set `--label_column_name` to the correct text column - one of "
F'''{', '.join(raw_datasets['train'].column_names )}.''' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
snake_case_ = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
snake_case_ = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
snake_case_ = feature_extractor.model_input_names[0]
def train_transforms(lowerCAmelCase_ :Union[str, Any] ):
snake_case_ = []
for audio in batch[data_args.audio_column_name]:
snake_case_ = random_subsample(
audio["array"] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(lowerCAmelCase_ )
snake_case_ = feature_extractor(lowerCAmelCase_ , sampling_rate=feature_extractor.sampling_rate )
snake_case_ = {model_input_name: inputs.get(lowerCAmelCase_ )}
snake_case_ = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(lowerCAmelCase_ :Dict ):
snake_case_ = [audio["array"] for audio in batch[data_args.audio_column_name]]
snake_case_ = feature_extractor(lowerCAmelCase_ , sampling_rate=feature_extractor.sampling_rate )
snake_case_ = {model_input_name: inputs.get(lowerCAmelCase_ )}
snake_case_ = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
snake_case_ = raw_datasets["train"].features[data_args.label_column_name].names
snake_case_ , snake_case_ = {}, {}
for i, label in enumerate(lowerCAmelCase_ ):
snake_case_ = str(lowerCAmelCase_ )
snake_case_ = label
# Load the accuracy metric from the datasets package
snake_case_ = evaluate.load("accuracy" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(lowerCAmelCase_ :Tuple ):
snake_case_ = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=lowerCAmelCase_ , references=eval_pred.label_ids )
snake_case_ = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(lowerCAmelCase_ ) , labelaid=lowerCAmelCase_ , idalabel=lowerCAmelCase_ , finetuning_task="audio-classification" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
snake_case_ = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowerCAmelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
snake_case_ = (
raw_datasets["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(lowerCAmelCase_ , output_all_columns=lowerCAmelCase_ )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
snake_case_ = (
raw_datasets["eval"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(lowerCAmelCase_ , output_all_columns=lowerCAmelCase_ )
# Initialize our trainer
snake_case_ = Trainer(
model=lowerCAmelCase_ , args=lowerCAmelCase_ , train_dataset=raw_datasets["train"] if training_args.do_train else None , eval_dataset=raw_datasets["eval"] if training_args.do_eval else None , compute_metrics=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ , )
# Training
if training_args.do_train:
snake_case_ = None
if training_args.resume_from_checkpoint is not None:
snake_case_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ = last_checkpoint
snake_case_ = trainer.train(resume_from_checkpoint=lowerCAmelCase_ )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
snake_case_ = trainer.evaluate()
trainer.log_metrics("eval" , lowerCAmelCase_ )
trainer.save_metrics("eval" , lowerCAmelCase_ )
# Write model card and (optionally) push to hub
snake_case_ = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "audio-classification",
"dataset": data_args.dataset_name,
"tags": ["audio-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCAmelCase_ )
else:
trainer.create_model_card(**lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 283 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : Tuple = "t5"
lowerCAmelCase_ : int = ["past_key_values"]
lowerCAmelCase_ : List[Any] = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self , a__=32_128 , a__=512 , a__=64 , a__=2_048 , a__=6 , a__=None , a__=8 , a__=32 , a__=128 , a__=0.1 , a__=1e-6 , a__=1.0 , a__="relu" , a__=True , a__=True , a__=0 , a__=1 , **a__ , ) -> str:
'''simple docstring'''
snake_case_ = vocab_size
snake_case_ = d_model
snake_case_ = d_kv
snake_case_ = d_ff
snake_case_ = num_layers
snake_case_ = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
snake_case_ = num_heads
snake_case_ = relative_attention_num_buckets
snake_case_ = relative_attention_max_distance
snake_case_ = dropout_rate
snake_case_ = layer_norm_epsilon
snake_case_ = initializer_factor
snake_case_ = feed_forward_proj
snake_case_ = use_cache
snake_case_ = self.feed_forward_proj.split("-" )
snake_case_ = act_info[-1]
snake_case_ = act_info[0] == "gated"
if len(a__ ) > 1 and act_info[0] != "gated" or len(a__ ) > 2:
raise ValueError(
F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"'gated-gelu' or 'relu'" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
snake_case_ = "gelu_new"
super().__init__(
pad_token_id=a__ , eos_token_id=a__ , is_encoder_decoder=a__ , **a__ , )
class _snake_case ( lowercase_ ):
@property
def lowerCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
snake_case_ = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
snake_case_ = "past_encoder_sequence + sequence"
snake_case_ = {0: "batch"}
snake_case_ = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
snake_case_ = {0: "batch", 1: "decoder_sequence"}
snake_case_ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(a__ , direction="inputs" )
return common_inputs
@property
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
return 13
| 400 | 0 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"microsoft/conditional-detr-resnet-50": (
"https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
),
}
class SCREAMING_SNAKE_CASE__ ( __A ):
_lowerCAmelCase = "conditional_detr"
_lowerCAmelCase = ["past_key_values"]
_lowerCAmelCase = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__(self , _lowercase=True , _lowercase=None , _lowercase=3 , _lowercase=300 , _lowercase=6 , _lowercase=2048 , _lowercase=8 , _lowercase=6 , _lowercase=2048 , _lowercase=8 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=True , _lowercase="relu" , _lowercase=256 , _lowercase=0.1 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.02 , _lowercase=1.0 , _lowercase=False , _lowercase="sine" , _lowercase="resnet50" , _lowercase=True , _lowercase=False , _lowercase=2 , _lowercase=5 , _lowercase=2 , _lowercase=1 , _lowercase=1 , _lowercase=2 , _lowercase=5 , _lowercase=2 , _lowercase=0.25 , **_lowercase , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can\'t specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
__a : Union[str, Any] = CONFIG_MAPPING['resnet'](out_features=["""stage4"""] )
elif isinstance(_lowercase , _lowercase ):
__a : int = backbone_config.get("""model_type""" )
__a : Any = CONFIG_MAPPING[backbone_model_type]
__a : Tuple = config_class.from_dict(_lowercase )
__a : Optional[int] = use_timm_backbone
__a : Dict = backbone_config
__a : List[str] = num_channels
__a : Dict = num_queries
__a : Dict = d_model
__a : str = encoder_ffn_dim
__a : Optional[Any] = encoder_layers
__a : str = encoder_attention_heads
__a : Optional[int] = decoder_ffn_dim
__a : Optional[int] = decoder_layers
__a : List[Any] = decoder_attention_heads
__a : Dict = dropout
__a : Any = attention_dropout
__a : Optional[int] = activation_dropout
__a : Dict = activation_function
__a : Optional[int] = init_std
__a : Dict = init_xavier_std
__a : Any = encoder_layerdrop
__a : str = decoder_layerdrop
__a : int = encoder_layers
__a : str = auxiliary_loss
__a : Optional[int] = position_embedding_type
__a : List[str] = backbone
__a : int = use_pretrained_backbone
__a : Dict = dilation
# Hungarian matcher
__a : List[str] = class_cost
__a : str = bbox_cost
__a : Optional[Any] = giou_cost
# Loss coefficients
__a : Tuple = mask_loss_coefficient
__a : Optional[int] = dice_loss_coefficient
__a : List[str] = cls_loss_coefficient
__a : Union[str, Any] = bbox_loss_coefficient
__a : Union[str, Any] = giou_loss_coefficient
__a : Optional[Any] = focal_alpha
super().__init__(is_encoder_decoder=_lowercase , **_lowercase )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self.d_model
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Union[str, Any] = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
__a : List[Any] = self.backbone_config.to_dict()
__a : Union[str, Any] = self.__class__.model_type
return output
class SCREAMING_SNAKE_CASE__ ( __A ):
_lowerCAmelCase = version.parse("1.11" )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return 1e-5
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return 12
| 703 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def __magic_name__ ( _lowerCamelCase : np.ndarray , _lowerCamelCase : float ):
# For applying gaussian function for each element in matrix.
__a : int = math.sqrt(_lowerCamelCase )
__a : Any = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigma) ** 2) * 0.5 )
def __magic_name__ ( _lowerCamelCase : np.ndarray , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
__a : Any = kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def __magic_name__ ( _lowerCamelCase : int , _lowerCamelCase : float ):
# Creates a gaussian kernel of given dimension.
__a : int = np.zeros((kernel_size, kernel_size) )
for i in range(0 , _lowerCamelCase ):
for j in range(0 , _lowerCamelCase ):
__a : Any = math.sqrt(
abs(i - kernel_size // 2 ) ** 2 + abs(j - kernel_size // 2 ) ** 2 )
return vec_gaussian(_lowerCamelCase , _lowerCamelCase )
def __magic_name__ ( _lowerCamelCase : np.ndarray , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : int , ):
__a : Tuple = np.zeros(img.shape )
__a : Optional[int] = get_gauss_kernel(_lowerCamelCase , _lowerCamelCase )
__a , __a : int = 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 ):
__a : List[str] = get_slice(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__a : Any = img_s - img_s[kernel_size // 2, kernel_size // 2]
__a : Optional[Any] = vec_gaussian(_lowerCamelCase , _lowerCamelCase )
__a : Optional[Any] = np.multiply(_lowerCamelCase , _lowerCamelCase )
__a : Any = np.multiply(_lowerCamelCase , _lowerCamelCase )
__a : Tuple = np.sum(_lowerCamelCase ) / np.sum(_lowerCamelCase )
__a : Optional[Any] = val
return imga
def __magic_name__ ( _lowerCamelCase : list ):
__a : Optional[Any] = args[1] if args[1:] else """../image_data/lena.jpg"""
__a : Union[str, Any] = float(args[2] ) if args[2:] else 1.0
__a : Optional[int] = float(args[3] ) if args[3:] else 1.0
if args[4:]:
__a : Any = int(args[4] )
__a : Any = kernel_size + abs(kernel_size % 2 - 1 )
else:
__a : Optional[int] = 5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
lowercase__ , lowercase__ , lowercase__ , lowercase__ = parse_args(sys.argv)
lowercase__ = cva.imread(filename, 0)
cva.imshow("input image", img)
lowercase__ = img / 255
lowercase__ = out.astype("float32")
lowercase__ = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
lowercase__ = out * 255
lowercase__ = np.uinta(out)
cva.imshow("output image", out)
cva.waitKey(0)
cva.destroyAllWindows()
| 63 | 0 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _snake_case ( _snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ = CTRLTokenizer
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
a :List[Any] = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
a :List[Any] = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
a :List[Any] = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
a :Tuple = {'''unk_token''': '''<unk>'''}
a :Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
a :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_lowerCamelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self , **_lowerCamelCase ):
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
a :str = '''adapt react readapt apt'''
a :Union[str, Any] = '''adapt react readapt apt'''
return input_text, output_text
def SCREAMING_SNAKE_CASE__ ( self ):
a :Tuple = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
a :Union[str, Any] = '''adapt react readapt apt'''
a :Tuple = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
a :List[Any] = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
a :Any = tokens + [tokenizer.unk_token]
a :Union[str, Any] = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
| 445 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _snake_case ( datasets.BeamBasedBuilder ):
def SCREAMING_SNAKE_CASE__ ( self ):
return datasets.DatasetInfo(
features=datasets.Features({'''content''': datasets.Value('''string''' )} ) , supervised_keys=_lowerCamelCase , )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_dummy_examples()} )]
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase ):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_lowerCamelCase )
class _snake_case ( datasets.BeamBasedBuilder ):
def SCREAMING_SNAKE_CASE__ ( self ):
return datasets.DatasetInfo(
features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) , supervised_keys=_lowerCamelCase , )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase ):
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_nested_examples()} )
]
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase ):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_lowerCamelCase )
def __lowerCamelCase ( ):
"""simple docstring"""
return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
def __lowerCamelCase ( ):
"""simple docstring"""
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
class _snake_case ( _snake_case ):
@require_beam
def SCREAMING_SNAKE_CASE__ ( self ):
a :Dict = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
a :Optional[Any] = DummyBeamDataset(cache_dir=_lowerCamelCase , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_lowerCamelCase , builder.name , '''default''' , '''0.0.0''' , F'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
a :str = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , _lowerCamelCase )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , _lowerCamelCase )
self.assertDictEqual(dset['''train'''][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_lowerCamelCase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def SCREAMING_SNAKE_CASE__ ( self ):
import apache_beam as beam
a :Any = beam.io.parquetio.WriteToParquet
a :Any = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
a :Union[str, Any] = DummyBeamDataset(cache_dir=_lowerCamelCase , beam_runner='''DirectRunner''' )
with patch('''apache_beam.io.parquetio.WriteToParquet''' ) as write_parquet_mock:
a :str = partial(_lowerCamelCase , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
_lowerCamelCase , builder.name , '''default''' , '''0.0.0''' , F'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
_lowerCamelCase , builder.name , '''default''' , '''0.0.0''' , F'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
a :List[str] = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , _lowerCamelCase )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , _lowerCamelCase )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['''train''']['''content'''] ) , sorted(['''foo''', '''bar''', '''foobar'''] ) )
self.assertTrue(
os.path.exists(os.path.join(_lowerCamelCase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def SCREAMING_SNAKE_CASE__ ( self ):
with tempfile.TemporaryDirectory() as tmp_cache_dir:
a :Dict = DummyBeamDataset(cache_dir=_lowerCamelCase )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def SCREAMING_SNAKE_CASE__ ( self ):
a :str = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
a :List[Any] = NestedBeamDataset(cache_dir=_lowerCamelCase , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_lowerCamelCase , builder.name , '''default''' , '''0.0.0''' , F'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) )
a :Any = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , _lowerCamelCase )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , _lowerCamelCase )
self.assertDictEqual(dset['''train'''][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_lowerCamelCase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
| 445 | 1 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = DownBlockaD # noqa F405
a__ = """down"""
def _lowercase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = ResnetDownsampleBlockaD # noqa F405
a__ = """down"""
def _lowercase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = AttnDownBlockaD # noqa F405
a__ = """down"""
def _lowercase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = CrossAttnDownBlockaD # noqa F405
a__ = """down"""
def _lowercase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__magic_name__ , __magic_name__ = super().prepare_init_args_and_inputs_for_common()
__magic_name__ = 32
return init_dict, inputs_dict
def _lowercase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__magic_name__ = [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = SimpleCrossAttnDownBlockaD # noqa F405
a__ = """down"""
@property
def _lowercase ( self : Dict ) -> Any:
"""simple docstring"""
return super().get_dummy_input(include_encoder_hidden_states=UpperCamelCase__ )
def _lowercase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ , __magic_name__ = super().prepare_init_args_and_inputs_for_common()
__magic_name__ = 32
return init_dict, inputs_dict
@unittest.skipIf(torch_device == """mps""" , """MPS result is not consistent""" )
def _lowercase ( self : Any ) -> List[Any]:
"""simple docstring"""
__magic_name__ = [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = SkipDownBlockaD # noqa F405
a__ = """down"""
@property
def _lowercase ( self : List[Any] ) -> str:
"""simple docstring"""
return super().get_dummy_input(include_skip_sample=UpperCamelCase__ )
def _lowercase ( self : Dict ) -> str:
"""simple docstring"""
__magic_name__ = [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = AttnSkipDownBlockaD # noqa F405
a__ = """down"""
@property
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return super().get_dummy_input(include_skip_sample=UpperCamelCase__ )
def _lowercase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = DownEncoderBlockaD # noqa F405
a__ = """down"""
@property
def _lowercase ( self : Any ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_temb=UpperCamelCase__ )
def _lowercase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__magic_name__ = {
"""in_channels""": 32,
"""out_channels""": 32,
}
__magic_name__ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ = [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = AttnDownEncoderBlockaD # noqa F405
a__ = """down"""
@property
def _lowercase ( self : List[str] ) -> str:
"""simple docstring"""
return super().get_dummy_input(include_temb=UpperCamelCase__ )
def _lowercase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__magic_name__ = {
"""in_channels""": 32,
"""out_channels""": 32,
}
__magic_name__ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__magic_name__ = [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = UNetMidBlockaD # noqa F405
a__ = """mid"""
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = {
"""in_channels""": 32,
"""temb_channels""": 128,
}
__magic_name__ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : int ) -> Any:
"""simple docstring"""
__magic_name__ = [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = UNetMidBlockaDCrossAttn # noqa F405
a__ = """mid"""
def _lowercase ( self : Any ) -> Any:
"""simple docstring"""
__magic_name__ , __magic_name__ = super().prepare_init_args_and_inputs_for_common()
__magic_name__ = 32
return init_dict, inputs_dict
def _lowercase ( self : Any ) -> List[str]:
"""simple docstring"""
__magic_name__ = [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = UNetMidBlockaDSimpleCrossAttn # noqa F405
a__ = """mid"""
@property
def _lowercase ( self : List[str] ) -> Tuple:
"""simple docstring"""
return super().get_dummy_input(include_encoder_hidden_states=UpperCamelCase__ )
def _lowercase ( self : Optional[int] ) -> int:
"""simple docstring"""
__magic_name__ , __magic_name__ = super().prepare_init_args_and_inputs_for_common()
__magic_name__ = 32
return init_dict, inputs_dict
def _lowercase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ = [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = UpBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : int ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase__ )
def _lowercase ( self : Tuple ) -> int:
"""simple docstring"""
__magic_name__ = [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = ResnetUpsampleBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase__ )
def _lowercase ( self : str ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = CrossAttnUpBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : Any ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase__ )
def _lowercase ( self : Dict ) -> Any:
"""simple docstring"""
__magic_name__ , __magic_name__ = super().prepare_init_args_and_inputs_for_common()
__magic_name__ = 32
return init_dict, inputs_dict
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = SimpleCrossAttnUpBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase__ , include_encoder_hidden_states=UpperCamelCase__ )
def _lowercase ( self : Dict ) -> List[Any]:
"""simple docstring"""
__magic_name__ , __magic_name__ = super().prepare_init_args_and_inputs_for_common()
__magic_name__ = 32
return init_dict, inputs_dict
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__magic_name__ = [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = AttnUpBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : Any ) -> Optional[int]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase__ )
@unittest.skipIf(torch_device == """mps""" , """MPS result is not consistent""" )
def _lowercase ( self : Dict ) -> str:
"""simple docstring"""
__magic_name__ = [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = SkipUpBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : Dict ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase__ )
def _lowercase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__magic_name__ = [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = AttnSkipUpBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : Any ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_res_hidden_states_tuple=UpperCamelCase__ )
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ = [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = UpDecoderBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : str ) -> List[Any]:
"""simple docstring"""
return super().get_dummy_input(include_temb=UpperCamelCase__ )
def _lowercase ( self : Dict ) -> List[Any]:
"""simple docstring"""
__magic_name__ = {"""in_channels""": 32, """out_channels""": 32}
__magic_name__ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
__magic_name__ = [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
super().test_output(UpperCamelCase__ )
class UpperCAmelCase_ ( _A , unittest.TestCase ):
'''simple docstring'''
a__ = AttnUpDecoderBlockaD # noqa F405
a__ = """up"""
@property
def _lowercase ( self : Tuple ) -> Tuple:
"""simple docstring"""
return super().get_dummy_input(include_temb=UpperCamelCase__ )
def _lowercase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = {"""in_channels""": 32, """out_channels""": 32}
__magic_name__ = self.dummy_input
return init_dict, inputs_dict
def _lowercase ( self : Any ) -> List[str]:
"""simple docstring"""
__magic_name__ = [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
super().test_output(UpperCamelCase__ )
| 76 |
def a__ ( A_ ):
'''simple docstring'''
return " ".join(
"""""".join(word[::-1] ) if len(A_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('Hey wollef sroirraw'))
| 76 | 1 |
"""simple docstring"""
from __future__ import annotations
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Optional[Any] , __lowercase :list[list[int]] ):
__lowerCamelCase : List[str] =TypeError(
'''Matrices must be formed from a list of zero or more lists containing at '''
'''least one and the same number of values, each of which must be of type '''
'''int or float.''' )
if len(__lowercase ) != 0:
__lowerCamelCase : Tuple =len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(__lowercase ) != cols:
raise error
for value in row:
if not isinstance(__lowercase , (int, float) ):
raise error
__lowerCamelCase : Optional[int] =rows
else:
__lowerCamelCase : List[str] =[]
def __lowercase ( self :List[Any] ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def __lowercase ( self :str ):
return len(self.rows )
@property
def __lowercase ( self :int ):
return len(self.rows[0] )
@property
def __lowercase ( self :Optional[int] ):
return (self.num_rows, self.num_columns)
@property
def __lowercase ( self :List[Any] ):
return self.order[0] == self.order[1]
def __lowercase ( self :Tuple ):
__lowerCamelCase : Union[str, Any] =[
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(__lowercase )
def __lowercase ( self :str ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def __lowercase ( self :Tuple ):
return bool(self.determinant() )
def __lowercase ( self :Tuple , __lowercase :int , __lowercase :int ):
__lowerCamelCase : str =[
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(__lowercase ).determinant()
def __lowercase ( self :List[str] , __lowercase :int , __lowercase :int ):
if (row + column) % 2 == 0:
return self.get_minor(__lowercase , __lowercase )
return -1 * self.get_minor(__lowercase , __lowercase )
def __lowercase ( self :Any ):
return Matrix(
[
[self.get_minor(__lowercase , __lowercase ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def __lowercase ( self :List[Any] ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def __lowercase ( self :Tuple ):
__lowerCamelCase : str =[
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(__lowercase )
def __lowercase ( self :Union[str, Any] ):
__lowerCamelCase : Dict =self.determinant()
if not determinant:
raise TypeError('''Only matrices with a non-zero determinant have an inverse''' )
return self.adjugate() * (1 / determinant)
def __repr__( self :Tuple ):
return str(self.rows )
def __str__( self :Optional[int] ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'''[''' + '''. '''.join([str(__lowercase ) for value in row] ) + '''.]'''
for row in self.rows
] )
+ "]"
)
def __lowercase ( self :List[str] , __lowercase :list[int] , __lowercase :int | None = None ):
__lowerCamelCase : Tuple =TypeError('''Row must be a list containing all ints and/or floats''' )
if not isinstance(__lowercase , __lowercase ):
raise type_error
for value in row:
if not isinstance(__lowercase , (int, float) ):
raise type_error
if len(__lowercase ) != self.num_columns:
raise ValueError(
'''Row must be equal in length to the other rows in the matrix''' )
if position is None:
self.rows.append(__lowercase )
else:
__lowerCamelCase : Optional[int] =self.rows[0:position] + [row] + self.rows[position:]
def __lowercase ( self :Optional[int] , __lowercase :list[int] , __lowercase :int | None = None ):
__lowerCamelCase : Any =TypeError(
'''Column must be a list containing all ints and/or floats''' )
if not isinstance(__lowercase , __lowercase ):
raise type_error
for value in column:
if not isinstance(__lowercase , (int, float) ):
raise type_error
if len(__lowercase ) != self.num_rows:
raise ValueError(
'''Column must be equal in length to the other columns in the matrix''' )
if position is None:
__lowerCamelCase : Optional[int] =[self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
__lowerCamelCase : Dict =[
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self :List[str] , __lowercase :object ):
if not isinstance(__lowercase , __lowercase ):
return NotImplemented
return self.rows == other.rows
def __ne__( self :List[str] , __lowercase :object ):
return not self == other
def __neg__( self :Tuple ):
return self * -1
def __add__( self :List[str] , __lowercase :Matrix ):
if self.order != other.order:
raise ValueError('''Addition requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self :Tuple , __lowercase :Matrix ):
if self.order != other.order:
raise ValueError('''Subtraction requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self :int , __lowercase :Matrix | int | float ):
if isinstance(__lowercase , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(__lowercase , __lowercase ):
if self.num_columns != other.num_rows:
raise ValueError(
'''The number of columns in the first matrix must '''
'''be equal to the number of rows in the second''' )
return Matrix(
[
[Matrix.dot_product(__lowercase , __lowercase ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'''A Matrix can only be multiplied by an int, float, or another matrix''' )
def __pow__( self :int , __lowercase :int ):
if not isinstance(__lowercase , __lowercase ):
raise TypeError('''A Matrix can only be raised to the power of an int''' )
if not self.is_square:
raise ValueError('''Only square matrices can be raised to a power''' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'''Only invertable matrices can be raised to a negative power''' )
__lowerCamelCase : Dict =self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def __lowercase ( cls :str , __lowercase :list[int] , __lowercase :list[int] ):
return sum(row[i] * column[i] for i in range(len(__lowercase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 179 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : bytes , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : int =F'{sampling_rate}'
__lowerCamelCase : Union[str, Any] ='''1'''
__lowerCamelCase : List[Any] ='''f32le'''
__lowerCamelCase : Optional[int] =[
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(SCREAMING_SNAKE_CASE , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
__lowerCamelCase : Union[str, Any] =ffmpeg_process.communicate(SCREAMING_SNAKE_CASE )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
__lowerCamelCase : Any =output_stream[0]
__lowerCamelCase : Tuple =np.frombuffer(SCREAMING_SNAKE_CASE , np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : str = "f32le" , ):
'''simple docstring'''
__lowerCamelCase : int =F'{sampling_rate}'
__lowerCamelCase : Union[str, Any] ='''1'''
if format_for_conversion == "s16le":
__lowerCamelCase : List[Any] =2
elif format_for_conversion == "f32le":
__lowerCamelCase : List[Any] =4
else:
raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )
__lowerCamelCase : Union[str, Any] =platform.system()
if system == "Linux":
__lowerCamelCase : Tuple ='''alsa'''
__lowerCamelCase : int ='''default'''
elif system == "Darwin":
__lowerCamelCase : Any ='''avfoundation'''
__lowerCamelCase : List[str] =''':0'''
elif system == "Windows":
__lowerCamelCase : Union[str, Any] ='''dshow'''
__lowerCamelCase : Dict ='''default'''
__lowerCamelCase : Any =[
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
__lowerCamelCase : Tuple =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
__lowerCamelCase : int =_ffmpeg_stream(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for item in iterator:
yield item
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : Optional[int] = None , SCREAMING_SNAKE_CASE : Optional[Union[Tuple[float, float], float]] = None , SCREAMING_SNAKE_CASE : str = "f32le" , ):
'''simple docstring'''
if stream_chunk_s is not None:
__lowerCamelCase : Optional[int] =stream_chunk_s
else:
__lowerCamelCase : Tuple =chunk_length_s
__lowerCamelCase : Union[str, Any] =ffmpeg_microphone(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , format_for_conversion=SCREAMING_SNAKE_CASE )
if format_for_conversion == "s16le":
__lowerCamelCase : int =np.intaa
__lowerCamelCase : Optional[int] =2
elif format_for_conversion == "f32le":
__lowerCamelCase : Dict =np.floataa
__lowerCamelCase : Tuple =4
else:
raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )
if stride_length_s is None:
__lowerCamelCase : Tuple =chunk_length_s / 6
__lowerCamelCase : str =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(SCREAMING_SNAKE_CASE , (int, float) ):
__lowerCamelCase : List[str] =[stride_length_s, stride_length_s]
__lowerCamelCase : List[str] =int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
__lowerCamelCase : int =int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
__lowerCamelCase : Optional[int] =datetime.datetime.now()
__lowerCamelCase : List[Any] =datetime.timedelta(seconds=SCREAMING_SNAKE_CASE )
for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , stride=(stride_left, stride_right) , stream=SCREAMING_SNAKE_CASE ):
# Put everything back in numpy scale
__lowerCamelCase : Optional[int] =np.frombuffer(item['''raw'''] , dtype=SCREAMING_SNAKE_CASE )
__lowerCamelCase : Dict =(
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
__lowerCamelCase : Optional[Any] =sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Tuple[int, int] , SCREAMING_SNAKE_CASE : bool = False ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] =B''''''
__lowerCamelCase , __lowerCamelCase : Dict =stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
__lowerCamelCase : List[str] =0
for raw in iterator:
acc += raw
if stream and len(SCREAMING_SNAKE_CASE ) < chunk_len:
__lowerCamelCase : List[str] =(_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(SCREAMING_SNAKE_CASE ) >= chunk_len:
# We are flushing the accumulator
__lowerCamelCase : Optional[Any] =(_stride_left, stride_right)
__lowerCamelCase : str ={'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
__lowerCamelCase : Optional[Any] =False
yield item
__lowerCamelCase : Dict =stride_left
__lowerCamelCase : str =acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(SCREAMING_SNAKE_CASE ) > stride_left:
__lowerCamelCase : List[str] ={'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
__lowerCamelCase : Tuple =False
yield item
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : Dict =2**24 # 16Mo
try:
with subprocess.Popen(SCREAMING_SNAKE_CASE , stdout=subprocess.PIPE , bufsize=SCREAMING_SNAKE_CASE ) as ffmpeg_process:
while True:
__lowerCamelCase : Dict =ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 179 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class lowerCamelCase_ ( _A ):
'''simple docstring'''
a__ = "dpt"
def __init__( self : Optional[int] , __lowerCamelCase : List[Any]=7_68 , __lowerCamelCase : Tuple=12 , __lowerCamelCase : List[str]=12 , __lowerCamelCase : Optional[Any]=30_72 , __lowerCamelCase : List[str]="gelu" , __lowerCamelCase : Tuple=0.0 , __lowerCamelCase : Optional[int]=0.0 , __lowerCamelCase : Optional[int]=0.02 , __lowerCamelCase : Optional[int]=1e-12 , __lowerCamelCase : List[Any]=3_84 , __lowerCamelCase : Optional[int]=16 , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : Tuple=False , __lowerCamelCase : int=True , __lowerCamelCase : Tuple=[2, 5, 8, 11] , __lowerCamelCase : Tuple="project" , __lowerCamelCase : List[str]=[4, 2, 1, 0.5] , __lowerCamelCase : Tuple=[96, 1_92, 3_84, 7_68] , __lowerCamelCase : Optional[Any]=2_56 , __lowerCamelCase : str=-1 , __lowerCamelCase : Dict=False , __lowerCamelCase : List[str]=True , __lowerCamelCase : Union[str, Any]=0.4 , __lowerCamelCase : Any=2_55 , __lowerCamelCase : Union[str, Any]=0.1 , __lowerCamelCase : Optional[Any]=[1, 10_24, 24, 24] , __lowerCamelCase : Optional[int]=[0, 1] , __lowerCamelCase : List[str]=None , **__lowerCamelCase : Dict , ) -> Optional[int]:
super().__init__(**__lowerCamelCase )
A : str = hidden_size
A : int = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("Initializing the config with a `BiT` backbone." )
A : str = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
}
A : Optional[int] = BitConfig(**__lowerCamelCase )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
logger.info("Initializing the config with a `BiT` backbone." )
A : Union[str, Any] = BitConfig(**__lowerCamelCase )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
A : Union[str, Any] = backbone_config
else:
raise ValueError(
F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )
A : int = backbone_featmap_shape
A : Tuple = neck_ignore_stages
if readout_type != "project":
raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode." )
else:
A : int = None
A : List[str] = None
A : Any = []
A : int = num_hidden_layers
A : str = num_attention_heads
A : Union[str, Any] = intermediate_size
A : List[Any] = hidden_act
A : str = hidden_dropout_prob
A : Dict = attention_probs_dropout_prob
A : str = initializer_range
A : List[Any] = layer_norm_eps
A : Union[str, Any] = image_size
A : Tuple = patch_size
A : str = num_channels
A : Dict = qkv_bias
A : Union[str, Any] = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']" )
A : List[Any] = readout_type
A : List[Any] = reassemble_factors
A : Union[str, Any] = neck_hidden_sizes
A : Tuple = fusion_hidden_size
A : Union[str, Any] = head_in_index
A : Dict = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
A : List[Any] = use_auxiliary_head
A : Optional[Any] = auxiliary_loss_weight
A : Optional[Any] = semantic_loss_ignore_index
A : Any = semantic_classifier_dropout
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[int]:
A : Optional[int] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
A : Tuple = self.backbone_config.to_dict()
A : Tuple = self.__class__.model_type
return output | 17 |
from collections.abc import Sequence
def UpperCAmelCase ( _lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
A : Dict = nums[0]
for i in range(1 , len(_lowerCamelCase ) ):
A : Tuple = nums[i]
A : List[Any] = max(_lowerCamelCase , ans + num , _lowerCamelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
__SCREAMING_SNAKE_CASE = int(input("""Enter number of elements : """).strip())
__SCREAMING_SNAKE_CASE = list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
print(max_subsequence_sum(array)) | 17 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
def __UpperCAmelCase ( snake_case_ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
"""simple docstring"""
if isinstance(snake_case_ , np.ndarray ):
return list(tensor.shape )
_lowerCAmelCase = tf.shape(snake_case_ )
if tensor.shape == tf.TensorShape(snake_case_ ):
return dynamic
_lowerCAmelCase = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(snake_case_ )]
def __UpperCAmelCase ( snake_case_ : tf.Tensor , snake_case_ : Optional[int] = None , snake_case_ : Optional[str] = None ) -> tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9 , axis=snake_case_ , name=snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : Union[str, Any] , snake_case_ : Optional[Any] , snake_case_ : str=1e-5 , snake_case_ : Any=-1 ) -> List[str]:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case_ , snake_case_ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
_lowerCAmelCase , _lowerCAmelCase = tf.nn.moments(snake_case_ , axes=[axis] , keepdims=snake_case_ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
_lowerCAmelCase = [1] * inputs.shape.rank
_lowerCAmelCase = shape_list(snake_case_ )[axis]
_lowerCAmelCase = tf.reshape(snake_case_ , snake_case_ )
_lowerCAmelCase = tf.reshape(snake_case_ , snake_case_ )
# Compute layer normalization using the batch_normalization
# function.
_lowerCAmelCase = tf.nn.batch_normalization(
snake_case_ , snake_case_ , snake_case_ , offset=snake_case_ , scale=snake_case_ , variance_epsilon=snake_case_ , )
return outputs
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : List[Any]=0 , snake_case_ : Union[str, Any]=-1 ) -> List[str]:
"""simple docstring"""
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
_lowerCAmelCase = tf.shape(snake_case_ )
_lowerCAmelCase = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
_lowerCAmelCase = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(snake_case_ , snake_case_ )
def __UpperCAmelCase ( snake_case_ : tf.Tensor ) -> tf.Tensor:
"""simple docstring"""
if not isinstance(snake_case_ , tf.Tensor ):
_lowerCAmelCase = tf.convert_to_tensor(snake_case_ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
_lowerCAmelCase = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
_lowerCAmelCase = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
_lowerCAmelCase = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __UpperCAmelCase ( snake_case_ : tf.Tensor , snake_case_ : int , snake_case_ : str = "input_ids" ) -> None:
"""simple docstring"""
tf.debugging.assert_less(
snake_case_ , tf.cast(snake_case_ , dtype=tensor.dtype ) , message=(
F"""The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case_ )}) must be smaller than the embedding """
F"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
) , )
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : Optional[int] , snake_case_ : str ) -> str:
"""simple docstring"""
_lowerCAmelCase = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
_lowerCAmelCase = [x for x in data if len(snake_case_ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
F"""bytes: {bad_attributes}""" )
_lowerCAmelCase = np.asarray(snake_case_ )
_lowerCAmelCase = 1
_lowerCAmelCase = np.array_split(snake_case_ , snake_case_ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
_lowerCAmelCase = np.array_split(snake_case_ , snake_case_ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(snake_case_ ):
_lowerCAmelCase = chunk_data
else:
_lowerCAmelCase = data
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : List[str] ) -> List[Any]:
"""simple docstring"""
if name in group.attrs:
_lowerCAmelCase = [n.decode("""utf8""" ) if hasattr(snake_case_ , """decode""" ) else n for n in group.attrs[name]]
else:
_lowerCAmelCase = []
_lowerCAmelCase = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(snake_case_ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def __UpperCAmelCase ( snake_case_ : str ) -> str:
"""simple docstring"""
def _expand_single_ad_tensor(snake_case_ : Dict ):
if isinstance(snake_case_ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(snake_case_ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , snake_case_ ) | 156 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = (KDPMaDiscreteScheduler,)
__UpperCamelCase = 10
def A__ (self , **lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = {
"""num_train_timesteps""": 1_100,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**lowerCamelCase )
return config
def A__ (self ):
'''simple docstring'''
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=lowerCamelCase )
def A__ (self ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCamelCase , beta_end=lowerCamelCase )
def A__ (self ):
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCamelCase )
def A__ (self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
_lowerCAmelCase = scheduler_class(**lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_lowerCAmelCase = sample.to(lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = scheduler.scale_model_input(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = output.prev_sample
_lowerCAmelCase = torch.sum(torch.abs(lowerCamelCase ) )
_lowerCAmelCase = torch.mean(torch.abs(lowerCamelCase ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6934e-07 ) < 1e-2
assert abs(result_mean.item() - 6.1112e-10 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.693428650170972e-07 ) < 1e-2
assert abs(result_mean.item() - 0.0002 ) < 1e-3
def A__ (self ):
'''simple docstring'''
if torch_device == "mps":
return
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_lowerCAmelCase = sample.to(lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase = scheduler.scale_model_input(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = output.prev_sample
_lowerCAmelCase = torch.sum(torch.abs(lowerCamelCase ) )
_lowerCAmelCase = torch.mean(torch.abs(lowerCamelCase ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
def A__ (self ):
'''simple docstring'''
if torch_device == "mps":
return
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps , device=lowerCamelCase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter.to(lowerCamelCase ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_lowerCAmelCase = scheduler.scale_model_input(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = output.prev_sample
_lowerCAmelCase = torch.sum(torch.abs(lowerCamelCase ) )
_lowerCAmelCase = torch.mean(torch.abs(lowerCamelCase ) )
if str(lowerCamelCase ).startswith("""cpu""" ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3 | 156 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->int:
_lowerCamelCase : Tuple = len(SCREAMING_SNAKE_CASE_ ) // 2
# choose the middle 3 elements
_lowerCamelCase : List[str] = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 558 | """simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
SCREAMING_SNAKE_CASE__ : List[Any] =16
SCREAMING_SNAKE_CASE__ : Union[str, Any] =32
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 16 ) ->List[str]:
_lowerCamelCase : Dict = AutoTokenizer.from_pretrained('''bert-base-cased''' )
_lowerCamelCase : Any = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(SCREAMING_SNAKE_CASE_ ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : str = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_lowerCamelCase : Optional[int] = datasets.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCamelCase : List[str] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(SCREAMING_SNAKE_CASE_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_lowerCamelCase : List[str] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_lowerCamelCase : Any = 16
elif accelerator.mixed_precision != "no":
_lowerCamelCase : List[str] = 8
else:
_lowerCamelCase : int = None
return tokenizer.pad(
SCREAMING_SNAKE_CASE_ , padding='''longest''' , max_length=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' , )
# Instantiate dataloaders.
_lowerCamelCase : Any = DataLoader(
tokenized_datasets['''train'''] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : List[Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
SCREAMING_SNAKE_CASE__ : Tuple =mocked_dataloaders # noqa: F811
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Union[str, Any]:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , SCREAMING_SNAKE_CASE_ ) == "1":
_lowerCamelCase : str = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
_lowerCamelCase : List[Any] = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
_lowerCamelCase : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCamelCase : List[str] = config['''lr''']
_lowerCamelCase : Tuple = int(config['''num_epochs'''] )
_lowerCamelCase : Tuple = int(config['''seed'''] )
_lowerCamelCase : str = int(config['''batch_size'''] )
set_seed(SCREAMING_SNAKE_CASE_ )
_lowerCamelCase, _lowerCamelCase : List[str] = get_dataloaders(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : Union[str, Any] = evaluate.load('''glue''' , '''mrpc''' )
# If the batch size is too big we use gradient accumulation
_lowerCamelCase : List[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_lowerCamelCase : Optional[Any] = batch_size // MAX_GPU_BATCH_SIZE
_lowerCamelCase : Union[str, Any] = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCamelCase : int = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=SCREAMING_SNAKE_CASE_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_lowerCamelCase : Dict = model.to(accelerator.device )
# Instantiate optimizer
_lowerCamelCase : List[Any] = AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE_ )
# Instantiate scheduler
_lowerCamelCase : List[str] = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE_ , num_warmup_steps=100 , num_training_steps=(len(SCREAMING_SNAKE_CASE_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Tuple = accelerator.prepare(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
_lowerCamelCase : Tuple = os.path.split(SCREAMING_SNAKE_CASE_ )[-1].split('''.''' )[0]
accelerator.init_trackers(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE_ ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
_lowerCamelCase : Any = 0
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_lowerCamelCase : Tuple = model(**SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : Tuple = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
_lowerCamelCase : List[Any] = loss / gradient_accumulation_steps
accelerator.backward(SCREAMING_SNAKE_CASE_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
_lowerCamelCase : Optional[Any] = model(**SCREAMING_SNAKE_CASE_ )
_lowerCamelCase : int = outputs.logits.argmax(dim=-1 )
_lowerCamelCase, _lowerCamelCase : int = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ , )
_lowerCamelCase : Optional[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , SCREAMING_SNAKE_CASE_ )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'''accuracy''': eval_metric['''accuracy'''],
'''f1''': eval_metric['''f1'''],
'''train_loss''': total_loss.item() / len(SCREAMING_SNAKE_CASE_ ),
'''epoch''': epoch,
} , step=SCREAMING_SNAKE_CASE_ , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def UpperCamelCase ( ) ->Optional[Any]:
_lowerCamelCase : List[str] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=SCREAMING_SNAKE_CASE_ , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
_lowerCamelCase : str = parser.parse_args()
_lowerCamelCase : Optional[int] = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 558 | 1 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] , __magic_name__ : list[int] , __magic_name__ : int ) -> tuple[float, list[float]]:
"""simple docstring"""
lowercase__ = list(range(len(__magic_name__ ) ) )
lowercase__ = [v / w for v, w in zip(__magic_name__ , __magic_name__ )]
index.sort(key=lambda __magic_name__ : ratio[i] , reverse=__magic_name__ )
lowercase__ = 0
lowercase__ = [0] * len(__magic_name__ )
for i in index:
if weight[i] <= capacity:
lowercase__ = 1
max_value += value[i]
capacity -= weight[i]
else:
lowercase__ = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 15 | """simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
__lowerCAmelCase : List[str] =[
"""cross_validation.py""",
"""gradient_accumulation.py""",
"""local_sgd.py""",
"""multi_process_metrics.py""",
"""memory.py""",
"""automatic_gradient_accumulation.py""",
"""fsdp_with_peak_mem_tracking.py""",
"""deepspeed_with_config_support.py""",
"""megatron_lm_gpt_pretraining.py""",
]
class _A ( unittest.TestCase ):
def A__ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None ):
"""simple docstring"""
lowercase = None
lowercase = os.path.abspath(os.path.join("""examples""" , """by_feature""" ) )
lowercase = os.path.abspath("""examples""" )
for item in os.listdir(__lowerCAmelCase ):
if item not in EXCLUDE_EXAMPLES:
lowercase = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.isfile(__lowerCAmelCase ) and ".py" in item_path:
with self.subTest(
tested_script=__lowerCAmelCase , feature_script=__lowerCAmelCase , tested_section="""main()""" if parser_only else """training_function()""" , ):
lowercase = compare_against_test(
os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowercase = """\n""".join(__lowerCAmelCase )
if special_strings is not None:
for string in special_strings:
lowercase = diff.replace(__lowerCAmelCase , """""" )
self.assertEqual(__lowerCAmelCase , """""" )
def A__ ( self ):
"""simple docstring"""
self.one_complete_example("""complete_nlp_example.py""" , __lowerCAmelCase )
self.one_complete_example("""complete_nlp_example.py""" , __lowerCAmelCase )
def A__ ( self ):
"""simple docstring"""
lowercase = os.path.abspath(os.path.join("""examples""" , """cv_example.py""" ) )
lowercase = [
""" """ * 16 + """{\n\n""",
""" """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
""" """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
""" """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
""" """ * 20 + """\"epoch\": epoch,\n\n""",
""" """ * 16 + """},\n\n""",
""" """ * 16 + """step=epoch,\n""",
""" """ * 12,
""" """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
]
self.one_complete_example("""complete_cv_example.py""" , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
self.one_complete_example("""complete_cv_example.py""" , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
@mock.patch.dict(os.environ , {'TESTING_MOCKED_DATALOADERS': '1'} )
class _A ( lowerCAmelCase ):
snake_case__ : Any = False
@classmethod
def A__ ( cls ):
"""simple docstring"""
super().setUpClass()
lowercase = tempfile.mkdtemp()
lowercase = os.path.join(cls._tmpdir , """default_config.yml""" )
write_basic_config(save_location=cls.configPath )
lowercase = ["""accelerate""", """launch""", """--config_file""", cls.configPath]
@classmethod
def A__ ( cls ):
"""simple docstring"""
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def A__ ( self ):
"""simple docstring"""
lowercase = f'\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , """epoch_0""" ) ) )
def A__ ( self ):
"""simple docstring"""
lowercase = f'\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n '.split()
lowercase = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , """step_2""" ) ) )
def A__ ( self ):
"""simple docstring"""
lowercase = f'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0" )}\n '.split()
lowercase = run_command(self._launch_args + testargs , return_stdout=__lowerCAmelCase )
self.assertNotIn("""epoch 0:""" , __lowerCAmelCase )
self.assertIn("""epoch 1:""" , __lowerCAmelCase )
def A__ ( self ):
"""simple docstring"""
lowercase = f'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , "step_2" )}\n '.split()
lowercase = run_command(self._launch_args + testargs , return_stdout=__lowerCAmelCase )
if torch.cuda.is_available():
lowercase = torch.cuda.device_count()
else:
lowercase = 1
if num_processes > 1:
self.assertNotIn("""epoch 0:""" , __lowerCAmelCase )
self.assertIn("""epoch 1:""" , __lowerCAmelCase )
else:
self.assertIn("""epoch 0:""" , __lowerCAmelCase )
self.assertIn("""epoch 1:""" , __lowerCAmelCase )
@slow
def A__ ( self ):
"""simple docstring"""
lowercase = """
examples/by_feature/cross_validation.py
--num_folds 2
""".split()
with mock.patch.dict(os.environ , {"""TESTING_MOCKED_DATALOADERS""": """0"""} ):
lowercase = run_command(self._launch_args + testargs , return_stdout=__lowerCAmelCase )
lowercase = re.findall("""({.+})""" , __lowerCAmelCase )
lowercase = [r for r in results if """accuracy""" in r][-1]
lowercase = ast.literal_eval(__lowerCAmelCase )
self.assertGreaterEqual(results["""accuracy"""] , 0.7_5 )
def A__ ( self ):
"""simple docstring"""
lowercase = ["""examples/by_feature/multi_process_metrics.py"""]
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {"""WANDB_MODE""": """offline"""} )
def A__ ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdir:
lowercase = f'\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , """tracking""" ) ) )
def A__ ( self ):
"""simple docstring"""
lowercase = ["""examples/by_feature/gradient_accumulation.py"""]
run_command(self._launch_args + testargs )
def A__ ( self ):
"""simple docstring"""
lowercase = ["""examples/by_feature/local_sgd.py"""]
run_command(self._launch_args + testargs )
| 359 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowercase_ = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowercase_ = typing.Union[np.floataa, int, float] # noqa: UP007
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(__UpperCAmelCase) - np.asarray(__UpperCAmelCase)) ** 2))
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(__UpperCAmelCase , __UpperCAmelCase)) ** (1 / 2)
if __name__ == "__main__":
def lowerCAmelCase ():
"""simple docstring"""
from timeit import timeit
print('''Without Numpy''')
print(
timeit(
'''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' , number=10_000 , globals=globals() , ))
print('''With Numpy''')
print(
timeit(
'''euclidean_distance([1, 2, 3], [4, 5, 6])''' , number=10_000 , globals=globals() , ))
benchmark()
| 713 |
'''simple docstring'''
from __future__ import annotations
lowercase_ = [True] * 1_000_001
lowercase_ = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
lowercase_ = False
i += 1
def lowerCAmelCase (__A):
"""simple docstring"""
return seive[n]
def lowerCAmelCase (__A):
"""simple docstring"""
return any(digit in '''02468''' for digit in str(__A))
def lowerCAmelCase (__A = 1_000_000):
"""simple docstring"""
_a = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2):
if is_prime(__A) and not contains_an_even_digit(__A):
_a = str(__A)
_a = [int(str_num[j:] + str_num[:j]) for j in range(len(__A))]
if all(is_prime(__A) for i in list_nums):
result.append(__A)
return result
def lowerCAmelCase ():
"""simple docstring"""
return len(find_circular_primes())
if __name__ == "__main__":
print(F"""{len(find_circular_primes()) = }""")
| 352 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase_ = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 326 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _lowercase :
'''simple docstring'''
def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=13 ,lowerCamelCase_=7 ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=99 ,lowerCamelCase_=32 ,lowerCamelCase_=2 ,lowerCamelCase_=4 ,lowerCamelCase_=37 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.1 ,lowerCamelCase_=0.1 ,lowerCamelCase_=512 ,lowerCamelCase_=16 ,lowerCamelCase_=2 ,lowerCamelCase_=0.02 ,lowerCamelCase_=3 ,lowerCamelCase_=4 ,lowerCamelCase_=None ,) -> str:
'''simple docstring'''
UpperCAmelCase__ : Any = parent
UpperCAmelCase__ : str = 13
UpperCAmelCase__ : Any = 7
UpperCAmelCase__ : str = True
UpperCAmelCase__ : List[Any] = True
UpperCAmelCase__ : Tuple = True
UpperCAmelCase__ : List[str] = True
UpperCAmelCase__ : List[Any] = 99
UpperCAmelCase__ : str = 32
UpperCAmelCase__ : Dict = 2
UpperCAmelCase__ : Union[str, Any] = 4
UpperCAmelCase__ : Dict = 37
UpperCAmelCase__ : Dict = '''gelu'''
UpperCAmelCase__ : List[str] = 0.1
UpperCAmelCase__ : List[str] = 0.1
UpperCAmelCase__ : Optional[int] = 512
UpperCAmelCase__ : Any = 16
UpperCAmelCase__ : List[Any] = 2
UpperCAmelCase__ : Optional[Any] = 0.02
UpperCAmelCase__ : Optional[int] = 3
UpperCAmelCase__ : Optional[int] = 4
UpperCAmelCase__ : Optional[int] = None
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_input_mask:
UpperCAmelCase__ : int = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : str = None
if self.use_token_type_ids:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
UpperCAmelCase__ : List[str] = None
UpperCAmelCase__ : Any = None
UpperCAmelCase__ : Dict = None
if self.use_labels:
UpperCAmelCase__ : Optional[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size] ,self.num_choices )
UpperCAmelCase__ : Union[str, Any] = RoFormerConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,return_dict=lowerCamelCase_ ,)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> int:
'''simple docstring'''
UpperCAmelCase__ : List[str] = TFRoFormerModel(config=lowerCamelCase_ )
UpperCAmelCase__ : str = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
UpperCAmelCase__ : str = [input_ids, input_mask]
UpperCAmelCase__ : Any = model(lowerCamelCase_ )
UpperCAmelCase__ : List[Any] = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = True
UpperCAmelCase__ : Any = TFRoFormerForCausalLM(config=lowerCamelCase_ )
UpperCAmelCase__ : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : int = model(lowerCamelCase_ )['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) ,[self.batch_size, self.seq_length, self.vocab_size] )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = TFRoFormerForMaskedLM(config=lowerCamelCase_ )
UpperCAmelCase__ : Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : Dict = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.num_labels
UpperCAmelCase__ : List[Any] = TFRoFormerForSequenceClassification(config=lowerCamelCase_ )
UpperCAmelCase__ : List[Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : Union[str, Any] = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : int = self.num_choices
UpperCAmelCase__ : Optional[int] = TFRoFormerForMultipleChoice(config=lowerCamelCase_ )
UpperCAmelCase__ : Tuple = tf.tile(tf.expand_dims(lowerCamelCase_ ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase__ : Optional[Any] = tf.tile(tf.expand_dims(lowerCamelCase_ ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase__ : Dict = tf.tile(tf.expand_dims(lowerCamelCase_ ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase__ : Optional[int] = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
UpperCAmelCase__ : str = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : Dict = self.num_labels
UpperCAmelCase__ : Optional[Any] = TFRoFormerForTokenClassification(config=lowerCamelCase_ )
UpperCAmelCase__ : Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : Optional[int] = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = TFRoFormerForQuestionAnswering(config=lowerCamelCase_ )
UpperCAmelCase__ : Any = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : int = model(lowerCamelCase_ )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) : List[Any] = config_and_inputs
UpperCAmelCase__ : Optional[int] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class _lowercase ( lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCAmelCase_ : int = (
{
'''feature-extraction''': TFRoFormerModel,
'''fill-mask''': TFRoFormerForMaskedLM,
'''question-answering''': TFRoFormerForQuestionAnswering,
'''text-classification''': TFRoFormerForSequenceClassification,
'''text-generation''': TFRoFormerForCausalLM,
'''token-classification''': TFRoFormerForTokenClassification,
'''zero-shot''': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCAmelCase_ : Optional[int] = False
UpperCAmelCase_ : Optional[int] = False
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Any:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = TFRoFormerModelTester(self )
UpperCAmelCase__ : int = ConfigTester(self ,config_class=lowerCamelCase_ ,hidden_size=37 )
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
@slow
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : List[str] = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
self.assertIsNotNone(lowerCamelCase_ )
@require_tf
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
UpperCAmelCase__ : Dict = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ : Union[str, Any] = model(lowerCamelCase_ )[0]
# TODO Replace vocab size
UpperCAmelCase__ : List[str] = 50000
UpperCAmelCase__ : int = [1, 6, vocab_size]
self.assertEqual(output.shape ,lowerCamelCase_ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
UpperCAmelCase__ : Tuple = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] ,lowerCamelCase_ ,atol=1e-4 )
@require_tf
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : int = 1E-4
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = tf.constant([[4, 10]] )
UpperCAmelCase__ : List[str] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 ,embedding_dim=6 )
UpperCAmelCase__ : Optional[Any] = emba(input_ids.shape )
UpperCAmelCase__ : List[str] = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(lowerCamelCase_ ,lowerCamelCase_ ,atol=self.tolerance )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Any = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
UpperCAmelCase__ : Dict = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 ,embedding_dim=512 )
emba([2, 16, 512] )
UpperCAmelCase__ : Optional[int] = emba.weight[:3, :5]
tf.debugging.assert_near(lowerCamelCase_ ,lowerCamelCase_ ,atol=self.tolerance )
@require_tf
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = 1E-4
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = tf.reshape(tf.range(2 * 12 * 16 * 64 ,dtype=tf.floataa ) ,shape=(2, 12, 16, 64) ) / 100
UpperCAmelCase__ : int = -tf.reshape(tf.range(2 * 12 * 16 * 64 ,dtype=tf.floataa ) ,shape=(2, 12, 16, 64) ) / 100
UpperCAmelCase__ : Optional[Any] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 ,embedding_dim=64 )
UpperCAmelCase__ : int = embed_positions([2, 16, 768] )[None, None, :, :]
UpperCAmelCase__ , UpperCAmelCase__ : Dict = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase__ : Any = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
UpperCAmelCase__ : List[str] = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] ,lowerCamelCase_ ,atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] ,lowerCamelCase_ ,atol=self.tolerance )
| 614 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a__ ( unittest.TestCase ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
lowerCAmelCase__ = torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
lowerCAmelCase__ = get_activation('''gelu''' )
self.assertTrue(torch.allclose(gelu_python(lowerCamelCase_ ) , torch_builtin(lowerCamelCase_ ) ) )
self.assertFalse(torch.allclose(gelu_python(lowerCamelCase_ ) , gelu_new(lowerCamelCase_ ) ) )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
lowerCAmelCase__ = torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
lowerCAmelCase__ = get_activation('''gelu''' )
lowerCAmelCase__ = get_activation('''gelu_10''' )
lowerCAmelCase__ = torch_builtin(lowerCamelCase_ )
lowerCAmelCase__ = geluaa(lowerCamelCase_ )
lowerCAmelCase__ = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(lowerCamelCase_ ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
get_activation('''gelu''' )
get_activation('''gelu_10''' )
get_activation('''gelu_fast''' )
get_activation('''gelu_new''' )
get_activation('''gelu_python''' )
get_activation('''gelu_pytorch_tanh''' )
get_activation('''linear''' )
get_activation('''mish''' )
get_activation('''quick_gelu''' )
get_activation('''relu''' )
get_activation('''sigmoid''' )
get_activation('''silu''' )
get_activation('''swish''' )
get_activation('''tanh''' )
with self.assertRaises(lowerCamelCase_ ):
get_activation('''bogus''' )
with self.assertRaises(lowerCamelCase_ ):
get_activation(lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
lowerCAmelCase__ = get_activation('''gelu''' )
lowerCAmelCase__ = 1
lowerCAmelCase__ = get_activation('''gelu''' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(lowerCamelCase_ ):
lowerCAmelCase__ = acta.a | 706 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__UpperCAmelCase = {
'''vocab_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
),
},
'''tokenizer_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
'''roberta-base-openai-detector''': (
'''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
),
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
),
},
}
__UpperCAmelCase = {
'''roberta-base''': 512,
'''roberta-large''': 512,
'''roberta-large-mnli''': 512,
'''distilroberta-base''': 512,
'''roberta-base-openai-detector''': 512,
'''roberta-large-openai-detector''': 512,
}
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : Optional[Any] = VOCAB_FILES_NAMES
lowercase__ : int = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : List[Any] = ["input_ids", "attention_mask"]
lowercase__ : Dict = RobertaTokenizer
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_="replace" , lowerCamelCase_="<s>" , lowerCamelCase_="</s>" , lowerCamelCase_="</s>" , lowerCamelCase_="<s>" , lowerCamelCase_="<unk>" , lowerCamelCase_="<pad>" , lowerCamelCase_="<mask>" , lowerCamelCase_=False , lowerCamelCase_=True , **lowerCamelCase_ , ) -> Tuple:
super().__init__(
lowerCamelCase_ , lowerCamelCase_ , tokenizer_file=lowerCamelCase_ , errors=lowerCamelCase_ , bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , add_prefix_space=lowerCamelCase_ , trim_offsets=lowerCamelCase_ , **lowerCamelCase_ , )
lowerCAmelCase__ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , lowerCamelCase_ ) != add_prefix_space:
lowerCAmelCase__ = getattr(lowerCamelCase_ , pre_tok_state.pop('''type''' ) )
lowerCAmelCase__ = add_prefix_space
lowerCAmelCase__ = pre_tok_class(**lowerCamelCase_ )
lowerCAmelCase__ = add_prefix_space
lowerCAmelCase__ = '''post_processor'''
lowerCAmelCase__ = getattr(self.backend_tokenizer , lowerCamelCase_ , lowerCamelCase_ )
if tokenizer_component_instance:
lowerCAmelCase__ = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowerCAmelCase__ = tuple(state['''sep'''] )
if "cls" in state:
lowerCAmelCase__ = tuple(state['''cls'''] )
lowerCAmelCase__ = False
if state.get('''add_prefix_space''' , lowerCamelCase_ ) != add_prefix_space:
lowerCAmelCase__ = add_prefix_space
lowerCAmelCase__ = True
if state.get('''trim_offsets''' , lowerCamelCase_ ) != trim_offsets:
lowerCAmelCase__ = trim_offsets
lowerCAmelCase__ = True
if changes_to_apply:
lowerCAmelCase__ = getattr(lowerCamelCase_ , state.pop('''type''' ) )
lowerCAmelCase__ = component_class(**lowerCamelCase_ )
setattr(self.backend_tokenizer , lowerCamelCase_ , lowerCamelCase_ )
@property
def __SCREAMING_SNAKE_CASE ( self ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Optional[Any]:
lowerCAmelCase__ = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else value
lowerCAmelCase__ = value
def __SCREAMING_SNAKE_CASE ( self , *lowerCamelCase_ , **lowerCamelCase_ ) -> BatchEncoding:
lowerCAmelCase__ = kwargs.get('''is_split_into_words''' , lowerCamelCase_ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , *lowerCamelCase_ , **lowerCamelCase_ ) -> BatchEncoding:
lowerCAmelCase__ = kwargs.get('''is_split_into_words''' , lowerCamelCase_ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*lowerCamelCase_ , **lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> Tuple[str]:
lowerCAmelCase__ = self._tokenizer.model.save(lowerCamelCase_ , name=lowerCamelCase_ )
return tuple(lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_=None ) -> Tuple:
lowerCAmelCase__ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> List[int]:
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 98 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class A__(a_ ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[str]:
a_ : Optional[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_lowercase , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(_lowercase , """num_attention_heads""" ) )
self.parent.assertTrue(hasattr(_lowercase , """num_encoder_blocks""" ) )
class A__:
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=13 , _lowercase=64 , _lowercase=3 , _lowercase=4 , _lowercase=[2, 2, 2, 2] , _lowercase=[8, 4, 2, 1] , _lowercase=[16, 32, 64, 128] , _lowercase=[1, 4, 8, 16] , _lowercase=[1, 2, 4, 8] , _lowercase=True , _lowercase=True , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.0_2 , _lowercase=3 , _lowercase=None , ) -> Any:
a_ : List[str] = parent
a_ : int = batch_size
a_ : Dict = image_size
a_ : Any = num_channels
a_ : Optional[int] = num_encoder_blocks
a_ : Optional[Any] = sr_ratios
a_ : List[str] = depths
a_ : int = hidden_sizes
a_ : List[Any] = downsampling_rates
a_ : List[Any] = num_attention_heads
a_ : Any = is_training
a_ : List[Any] = use_labels
a_ : List[Any] = hidden_act
a_ : Dict = hidden_dropout_prob
a_ : Any = attention_probs_dropout_prob
a_ : List[str] = initializer_range
a_ : Dict = num_labels
a_ : Union[str, Any] = scope
def UpperCamelCase__ ( self ) -> List[str]:
a_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a_ : int = None
if self.use_labels:
a_ : List[Any] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
a_ : Dict = self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ ( self ) -> Any:
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> List[Any]:
a_ : Optional[Any] = SegformerModel(config=_lowercase )
model.to(_lowercase )
model.eval()
a_ : List[str] = model(_lowercase )
a_ : int = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> Optional[int]:
a_ : Any = self.num_labels
a_ : Union[str, Any] = SegformerForSemanticSegmentation(_lowercase )
model.to(_lowercase )
model.eval()
a_ : str = model(_lowercase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
a_ : Optional[Any] = model(_lowercase , labels=_lowercase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCamelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> List[Any]:
a_ : int = 1
a_ : Union[str, Any] = SegformerForSemanticSegmentation(config=_lowercase )
model.to(_lowercase )
model.eval()
a_ : int = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(_lowercase )
a_ : Optional[int] = model(_lowercase , labels=_lowercase )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCamelCase__ ( self ) -> Optional[int]:
a_ : Union[str, Any] = self.prepare_config_and_inputs()
a_ , a_ , a_ : Optional[int] = config_and_inputs
a_ : int = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class A__(a_, a_, unittest.TestCase ):
"""simple docstring"""
_A : Dict = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
_A : List[Any] = (
{
'''feature-extraction''': SegformerModel,
'''image-classification''': SegformerForImageClassification,
'''image-segmentation''': SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_A : Optional[int] = True
_A : Optional[Any] = False
_A : int = False
_A : Optional[int] = False
def UpperCamelCase__ ( self ) -> Dict:
a_ : str = SegformerModelTester(self )
a_ : Optional[int] = SegformerConfigTester(self , config_class=_lowercase )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ) -> List[Any]:
a_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def UpperCamelCase__ ( self ) -> int:
a_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*_lowercase )
def UpperCamelCase__ ( self ) -> Optional[int]:
a_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*_lowercase )
@unittest.skip("""SegFormer does not use inputs_embeds""" )
def UpperCamelCase__ ( self ) -> Optional[int]:
pass
@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods""" )
def UpperCamelCase__ ( self ) -> List[Any]:
pass
def UpperCamelCase__ ( self ) -> Dict:
a_ , a_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ : int = model_class(_lowercase )
a_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a_ : Dict = [*signature.parameters.keys()]
a_ : List[str] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowercase )
def UpperCamelCase__ ( self ) -> Optional[int]:
a_ , a_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
a_ : Optional[int] = True
for model_class in self.all_model_classes:
a_ : List[Any] = True
a_ : Optional[Any] = False
a_ : Tuple = True
a_ : Any = model_class(_lowercase )
model.to(_lowercase )
model.eval()
with torch.no_grad():
a_ : Dict = model(**self._prepare_for_class(_lowercase , _lowercase ) )
a_ : Union[str, Any] = outputs.attentions
a_ : Optional[int] = sum(self.model_tester.depths )
self.assertEqual(len(_lowercase ) , _lowercase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
a_ : Dict = True
a_ : List[str] = model_class(_lowercase )
model.to(_lowercase )
model.eval()
with torch.no_grad():
a_ : int = model(**self._prepare_for_class(_lowercase , _lowercase ) )
a_ : Optional[Any] = outputs.attentions
self.assertEqual(len(_lowercase ) , _lowercase )
# verify the first attentions (first block, first layer)
a_ : List[str] = (self.model_tester.image_size // 4) ** 2
a_ : int = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
a_ : int = (self.model_tester.image_size // 32) ** 2
a_ : Optional[int] = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
a_ : Dict = len(_lowercase )
# Check attention is always last and order is fine
a_ : List[str] = True
a_ : List[Any] = True
a_ : int = model_class(_lowercase )
model.to(_lowercase )
model.eval()
with torch.no_grad():
a_ : int = model(**self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(out_len + 1 , len(_lowercase ) )
a_ : Optional[int] = outputs.attentions
self.assertEqual(len(_lowercase ) , _lowercase )
# verify the first attentions (first block, first layer)
a_ : int = (self.model_tester.image_size // 4) ** 2
a_ : Union[str, Any] = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def UpperCamelCase__ ( self ) -> Optional[int]:
def check_hidden_states_output(_lowercase , _lowercase , _lowercase ):
a_ : Tuple = model_class(_lowercase )
model.to(_lowercase )
model.eval()
with torch.no_grad():
a_ : Tuple = model(**self._prepare_for_class(_lowercase , _lowercase ) )
a_ : Optional[Any] = outputs.hidden_states
a_ : Optional[Any] = self.model_tester.num_encoder_blocks
self.assertEqual(len(_lowercase ) , _lowercase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
a_ , a_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ : List[Any] = True
check_hidden_states_output(_lowercase , _lowercase , _lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a_ : Dict = True
check_hidden_states_output(_lowercase , _lowercase , _lowercase )
def UpperCamelCase__ ( self ) -> Optional[Any]:
if not self.model_tester.is_training:
return
a_ , a_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
a_ : List[Any] = True
for model_class in self.all_model_classes:
if model_class in get_values(_lowercase ):
continue
a_ : Union[str, Any] = model_class(_lowercase )
model.to(_lowercase )
model.train()
a_ : List[str] = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
a_ : List[Any] = model(**_lowercase ).loss
loss.backward()
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
pass
@slow
def UpperCamelCase__ ( self ) -> Optional[Any]:
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ : Union[str, Any] = SegformerModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
def _UpperCAmelCase ( ):
'''simple docstring'''
a_ : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
return image
@require_torch
class A__(unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCamelCase__ ( self ) -> List[str]:
# only resize + normalize
a_ : Tuple = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=_lowercase , align=_lowercase , do_random_crop=_lowercase )
a_ : Dict = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
_lowercase )
a_ : str = prepare_img()
a_ : str = image_processor(images=_lowercase , return_tensors="""pt""" )
a_ : List[str] = encoded_inputs.pixel_values.to(_lowercase )
with torch.no_grad():
a_ : Union[str, Any] = model(_lowercase )
a_ : Tuple = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , _lowercase )
a_ : Any = torch.tensor(
[
[[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
[[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
[[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
] ).to(_lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , _lowercase , atol=1e-4 ) )
@slow
def UpperCamelCase__ ( self ) -> Optional[Any]:
# only resize + normalize
a_ : Optional[Any] = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=_lowercase , align=_lowercase , do_random_crop=_lowercase )
a_ : int = SegformerForSemanticSegmentation.from_pretrained(
"""nvidia/segformer-b1-finetuned-cityscapes-1024-1024""" ).to(_lowercase )
a_ : Any = prepare_img()
a_ : List[Any] = image_processor(images=_lowercase , return_tensors="""pt""" )
a_ : Optional[int] = encoded_inputs.pixel_values.to(_lowercase )
with torch.no_grad():
a_ : List[str] = model(_lowercase )
a_ : Tuple = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , _lowercase )
a_ : Any = torch.tensor(
[
[[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
[[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
[[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
] ).to(_lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , _lowercase , atol=1e-1 ) )
@slow
def UpperCamelCase__ ( self ) -> List[str]:
# only resize + normalize
a_ : Tuple = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=_lowercase , align=_lowercase , do_random_crop=_lowercase )
a_ : str = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
_lowercase )
a_ : int = prepare_img()
a_ : Any = image_processor(images=_lowercase , return_tensors="""pt""" )
a_ : Optional[int] = encoded_inputs.pixel_values.to(_lowercase )
with torch.no_grad():
a_ : Any = model(_lowercase )
a_ : str = outputs.logits.detach().cpu()
a_ : int = image_processor.post_process_semantic_segmentation(outputs=_lowercase , target_sizes=[(500, 300)] )
a_ : Any = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , _lowercase )
a_ : Tuple = image_processor.post_process_semantic_segmentation(outputs=_lowercase )
a_ : Optional[Any] = torch.Size((128, 128) )
self.assertEqual(segmentation[0].shape , _lowercase )
| 540 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : Optional[Any] = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(a__ , a__)
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ , a_ : List[Any] = emb.weight.shape
a_ : Dict = nn.Linear(a__ , a__ , bias=a__)
a_ : str = emb.weight.data
return lin_layer
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : Optional[int] = torch.load(a__ , map_location="""cpu""")
a_ : int = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
a_ : Dict = mam_aaa["""model"""]
remove_ignore_keys_(a__)
a_ : List[str] = state_dict["""encoder.embed_tokens.weight"""].shape[0]
a_ : Union[str, Any] = MaMaaaConfig(
vocab_size=a__ , max_position_embeddings=1_0_2_4 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
a_ : Union[str, Any] = state_dict["""decoder.embed_tokens.weight"""]
a_ : List[Any] = MaMaaaForConditionalGeneration(a__)
model.model.load_state_dict(a__ , strict=a__)
a_ : Any = make_linear_from_emb(model.model.shared)
return model
if __name__ == "__main__":
__snake_case : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
__snake_case : int = parser.parse_args()
__snake_case : Any = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 540 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__lowerCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
def __init__( self : Any ,*_a : Optional[Any] ,**_a : Union[str, Any] ):
'''simple docstring'''
warnings.warn(
'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use FlavaImageProcessor instead.' ,_a ,)
super().__init__(*_a ,**_a )
| 721 |
'''simple docstring'''
import math
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : List[str] ,_a : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
_a : List[Any] = n
_a : int = [
[math.inf for j in range(0 ,_a )] for i in range(0 ,_a )
] # adjacency matrix for weight
_a : List[Any] = [
[math.inf for j in range(0 ,_a )] for i in range(0 ,_a )
] # dp[i][j] stores minimum distance from i to j
def __lowercase ( self : List[str] ,_a : Optional[Any] ,_a : List[Any] ,_a : Union[str, Any] ):
'''simple docstring'''
_a : str = w
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
for k in range(0 ,self.n ):
for i in range(0 ,self.n ):
for j in range(0 ,self.n ):
_a : Optional[Any] = min(self.dp[i][j] ,self.dp[i][k] + self.dp[k][j] )
def __lowercase ( self : Union[str, Any] ,_a : Optional[int] ,_a : int ):
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
__lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 319 | 0 |
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = []
for data in source_data:
for i, el in enumerate(lowerCamelCase_ ):
if len(lowerCamelCase_ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowerCamelCase_ ) )
return data_lists
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = []
for dlist, weight in zip(lowerCamelCase_ , lowerCamelCase_ ):
lowercase__ = min(lowerCamelCase_ )
lowercase__ = max(lowerCamelCase_ )
lowercase__ = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
lowercase__ = F"""Invalid weight of {weight:f} provided"""
raise ValueError(lowerCamelCase_ )
score_lists.append(lowerCamelCase_ )
return score_lists
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(lowerCamelCase_ ):
lowercase__ = final_scores[j] + ele
return final_scores
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = get_data(lowerCamelCase_ )
lowercase__ = calculate_each_score(lowerCamelCase_ , lowerCamelCase_ )
lowercase__ = generate_final_scores(lowerCamelCase_ )
# append scores to source data
for i, ele in enumerate(lowerCamelCase_ ):
source_data[i].append(lowerCamelCase_ )
return source_data
| 183 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[int] = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
'processing_clap': ['ClapProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : str = [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapModel',
'ClapPreTrainedModel',
'ClapTextModel',
'ClapTextModelWithProjection',
'ClapAudioModel',
'ClapAudioModelWithProjection',
]
A__ : List[Any] = ['ClapFeatureExtractor']
if TYPE_CHECKING:
from .configuration_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioConfig,
ClapConfig,
ClapTextConfig,
)
from .processing_clap import ClapProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clap import ClapFeatureExtractor
from .modeling_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioModel,
ClapAudioModelWithProjection,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
else:
import sys
A__ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 183 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class UpperCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase_ = "nat"
lowerCamelCase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self :List[Any] , __A :Optional[Any]=4 , __A :Any=3 , __A :Optional[int]=64 , __A :Optional[int]=[3, 4, 6, 5] , __A :Union[str, Any]=[2, 4, 8, 16] , __A :Optional[Any]=7 , __A :Optional[Any]=3.0 , __A :List[Any]=True , __A :int=0.0 , __A :Dict=0.0 , __A :Optional[Any]=0.1 , __A :str="gelu" , __A :Optional[Any]=0.0_2 , __A :Optional[int]=1E-5 , __A :Optional[int]=0.0 , __A :Optional[Any]=None , __A :Union[str, Any]=None , **__A :Union[str, Any] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**__A )
SCREAMING_SNAKE_CASE__ = patch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = embed_dim
SCREAMING_SNAKE_CASE__ = depths
SCREAMING_SNAKE_CASE__ = len(__A )
SCREAMING_SNAKE_CASE__ = num_heads
SCREAMING_SNAKE_CASE__ = kernel_size
SCREAMING_SNAKE_CASE__ = mlp_ratio
SCREAMING_SNAKE_CASE__ = qkv_bias
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = drop_path_rate
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE__ = int(embed_dim * 2 ** (len(__A ) - 1) )
SCREAMING_SNAKE_CASE__ = layer_scale_init_value
SCREAMING_SNAKE_CASE__ = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(__A ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_aligned_output_features_output_indices(
out_features=__A , out_indices=__A , stage_names=self.stage_names ) | 59 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class UpperCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase_ = "nat"
lowerCamelCase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self :List[Any] , __A :Optional[Any]=4 , __A :Any=3 , __A :Optional[int]=64 , __A :Optional[int]=[3, 4, 6, 5] , __A :Union[str, Any]=[2, 4, 8, 16] , __A :Optional[Any]=7 , __A :Optional[Any]=3.0 , __A :List[Any]=True , __A :int=0.0 , __A :Dict=0.0 , __A :Optional[Any]=0.1 , __A :str="gelu" , __A :Optional[Any]=0.0_2 , __A :Optional[int]=1E-5 , __A :Optional[int]=0.0 , __A :Optional[Any]=None , __A :Union[str, Any]=None , **__A :Union[str, Any] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**__A )
SCREAMING_SNAKE_CASE__ = patch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = embed_dim
SCREAMING_SNAKE_CASE__ = depths
SCREAMING_SNAKE_CASE__ = len(__A )
SCREAMING_SNAKE_CASE__ = num_heads
SCREAMING_SNAKE_CASE__ = kernel_size
SCREAMING_SNAKE_CASE__ = mlp_ratio
SCREAMING_SNAKE_CASE__ = qkv_bias
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = drop_path_rate
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE__ = int(embed_dim * 2 ** (len(__A ) - 1) )
SCREAMING_SNAKE_CASE__ = layer_scale_init_value
SCREAMING_SNAKE_CASE__ = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(__A ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_aligned_output_features_output_indices(
out_features=__A , out_indices=__A , stage_names=self.stage_names ) | 59 | 1 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 1_00 * 2**20, 9_00 * 2**20] )
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , """IN_MEMORY_MAX_SIZE""" , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
SCREAMING_SNAKE_CASE : List[Any] = dataset_size < in_memory_max_size
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : List[Any] = is_small_dataset(lowerCamelCase_ )
assert result == expected
| 379 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
__UpperCAmelCase = """CompVis/stable-diffusion-v1-1"""
__UpperCAmelCase = """CompVis/stable-diffusion-v1-2"""
__UpperCAmelCase = """CompVis/stable-diffusion-v1-3"""
__UpperCAmelCase = """CompVis/stable-diffusion-v1-4"""
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : AutoencoderKL , lowerCamelCase_ : CLIPTextModel , lowerCamelCase_ : CLIPTokenizer , lowerCamelCase_ : UNetaDConditionModel , lowerCamelCase_ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowerCamelCase_ : StableDiffusionSafetyChecker , lowerCamelCase_ : CLIPImageProcessor , lowerCamelCase_ : bool = True , ):
'''simple docstring'''
super()._init_()
SCREAMING_SNAKE_CASE : Tuple = StableDiffusionPipeline.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = StableDiffusionPipeline.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = StableDiffusionPipeline.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = StableDiffusionPipeline(
vae=lowerCamelCase_ , text_encoder=lowerCamelCase_ , tokenizer=lowerCamelCase_ , unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , safety_checker=lowerCamelCase_ , feature_extractor=lowerCamelCase_ , requires_safety_checker=lowerCamelCase_ , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea )
@property
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return {k: getattr(self , lowerCamelCase_ ) for k in self.config.keys() if not k.startswith("""_""" )}
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Optional[Union[str, int]] = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
SCREAMING_SNAKE_CASE : Optional[int] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
self.enable_attention_slicing(lowerCamelCase_ )
@torch.no_grad()
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union[str, List[str]] , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 50 , lowerCamelCase_ : float = 7.5 , lowerCamelCase_ : Optional[Union[str, List[str]]] = None , lowerCamelCase_ : Optional[int] = 1 , lowerCamelCase_ : float = 0.0 , lowerCamelCase_ : Optional[torch.Generator] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , lowerCamelCase_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase_ : int = 1 , **lowerCamelCase_ : Union[str, Any] , ):
'''simple docstring'''
return self.pipea(
prompt=lowerCamelCase_ , height=lowerCamelCase_ , width=lowerCamelCase_ , num_inference_steps=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , negative_prompt=lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , latents=lowerCamelCase_ , output_type=lowerCamelCase_ , return_dict=lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=lowerCamelCase_ , **lowerCamelCase_ , )
@torch.no_grad()
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : Union[str, List[str]] , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 50 , lowerCamelCase_ : float = 7.5 , lowerCamelCase_ : Optional[Union[str, List[str]]] = None , lowerCamelCase_ : Optional[int] = 1 , lowerCamelCase_ : float = 0.0 , lowerCamelCase_ : Optional[torch.Generator] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , lowerCamelCase_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase_ : int = 1 , **lowerCamelCase_ : Union[str, Any] , ):
'''simple docstring'''
return self.pipea(
prompt=lowerCamelCase_ , height=lowerCamelCase_ , width=lowerCamelCase_ , num_inference_steps=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , negative_prompt=lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , latents=lowerCamelCase_ , output_type=lowerCamelCase_ , return_dict=lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=lowerCamelCase_ , **lowerCamelCase_ , )
@torch.no_grad()
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union[str, List[str]] , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 50 , lowerCamelCase_ : float = 7.5 , lowerCamelCase_ : Optional[Union[str, List[str]]] = None , lowerCamelCase_ : Optional[int] = 1 , lowerCamelCase_ : float = 0.0 , lowerCamelCase_ : Optional[torch.Generator] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , lowerCamelCase_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase_ : int = 1 , **lowerCamelCase_ : str , ):
'''simple docstring'''
return self.pipea(
prompt=lowerCamelCase_ , height=lowerCamelCase_ , width=lowerCamelCase_ , num_inference_steps=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , negative_prompt=lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , latents=lowerCamelCase_ , output_type=lowerCamelCase_ , return_dict=lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=lowerCamelCase_ , **lowerCamelCase_ , )
@torch.no_grad()
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Union[str, List[str]] , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 50 , lowerCamelCase_ : float = 7.5 , lowerCamelCase_ : Optional[Union[str, List[str]]] = None , lowerCamelCase_ : Optional[int] = 1 , lowerCamelCase_ : float = 0.0 , lowerCamelCase_ : Optional[torch.Generator] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , lowerCamelCase_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase_ : int = 1 , **lowerCamelCase_ : Dict , ):
'''simple docstring'''
return self.pipea(
prompt=lowerCamelCase_ , height=lowerCamelCase_ , width=lowerCamelCase_ , num_inference_steps=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , negative_prompt=lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , latents=lowerCamelCase_ , output_type=lowerCamelCase_ , return_dict=lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=lowerCamelCase_ , **lowerCamelCase_ , )
@torch.no_grad()
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : Union[str, List[str]] , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 5_12 , lowerCamelCase_ : int = 50 , lowerCamelCase_ : float = 7.5 , lowerCamelCase_ : Optional[Union[str, List[str]]] = None , lowerCamelCase_ : Optional[int] = 1 , lowerCamelCase_ : float = 0.0 , lowerCamelCase_ : Optional[torch.Generator] = None , lowerCamelCase_ : Optional[torch.FloatTensor] = None , lowerCamelCase_ : Optional[str] = "pil" , lowerCamelCase_ : bool = True , lowerCamelCase_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase_ : int = 1 , **lowerCamelCase_ : Any , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = """cuda""" if torch.cuda.is_available() else """cpu"""
self.to(lowerCamelCase_ )
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` must be divisible by 8 but are {height} and {width}.''' )
# Get first result from Stable Diffusion Checkpoint v1.1
SCREAMING_SNAKE_CASE : List[str] = self.textaimg_sda_a(
prompt=lowerCamelCase_ , height=lowerCamelCase_ , width=lowerCamelCase_ , num_inference_steps=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , negative_prompt=lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , latents=lowerCamelCase_ , output_type=lowerCamelCase_ , return_dict=lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=lowerCamelCase_ , **lowerCamelCase_ , )
# Get first result from Stable Diffusion Checkpoint v1.2
SCREAMING_SNAKE_CASE : Union[str, Any] = self.textaimg_sda_a(
prompt=lowerCamelCase_ , height=lowerCamelCase_ , width=lowerCamelCase_ , num_inference_steps=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , negative_prompt=lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , latents=lowerCamelCase_ , output_type=lowerCamelCase_ , return_dict=lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=lowerCamelCase_ , **lowerCamelCase_ , )
# Get first result from Stable Diffusion Checkpoint v1.3
SCREAMING_SNAKE_CASE : int = self.textaimg_sda_a(
prompt=lowerCamelCase_ , height=lowerCamelCase_ , width=lowerCamelCase_ , num_inference_steps=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , negative_prompt=lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , latents=lowerCamelCase_ , output_type=lowerCamelCase_ , return_dict=lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=lowerCamelCase_ , **lowerCamelCase_ , )
# Get first result from Stable Diffusion Checkpoint v1.4
SCREAMING_SNAKE_CASE : Optional[int] = self.textaimg_sda_a(
prompt=lowerCamelCase_ , height=lowerCamelCase_ , width=lowerCamelCase_ , num_inference_steps=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , negative_prompt=lowerCamelCase_ , num_images_per_prompt=lowerCamelCase_ , eta=lowerCamelCase_ , generator=lowerCamelCase_ , latents=lowerCamelCase_ , output_type=lowerCamelCase_ , return_dict=lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=lowerCamelCase_ , **lowerCamelCase_ , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]] )
| 379 | 1 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A_ : Dict = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
A_ : Optional[Any] = logging.getLogger()
def UpperCamelCase () -> Optional[Any]:
A__ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""-f""" )
A__ : Tuple = parser.parse_args()
return args.f
def UpperCamelCase (lowercase_: Optional[int] , lowercase_: int="eval" ) -> Dict:
A__ : List[Any] = os.path.join(lowercase_ , f"""{split}_results.json""" )
if os.path.exists(lowercase_ ):
with open(lowercase_ , """r""" ) as f:
return json.load(lowercase_ )
raise ValueError(f"""can't find {path}""" )
A_ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _a (__magic_name__ ):
'''simple docstring'''
def __A ( self ):
A__ : Tuple = self.get_auto_remove_tmp_dir()
A__ : str = F"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(A__ , """argv""" , A__ ):
run_flax_glue.main()
A__ : Tuple = get_results(A__ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.7_5 )
@slow
def __A ( self ):
A__ : List[str] = self.get_auto_remove_tmp_dir()
A__ : Tuple = F"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , """argv""" , A__ ):
run_clm_flax.main()
A__ : int = get_results(A__ )
self.assertLess(result["""eval_perplexity"""] , 100 )
@slow
def __A ( self ):
A__ : Tuple = self.get_auto_remove_tmp_dir()
A__ : Union[str, Any] = F"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(A__ , """argv""" , A__ ):
run_summarization_flax.main()
A__ : List[Any] = get_results(A__ , split="""test""" )
self.assertGreaterEqual(result["""test_rouge1"""] , 10 )
self.assertGreaterEqual(result["""test_rouge2"""] , 2 )
self.assertGreaterEqual(result["""test_rougeL"""] , 7 )
self.assertGreaterEqual(result["""test_rougeLsum"""] , 7 )
@slow
def __A ( self ):
A__ : int = self.get_auto_remove_tmp_dir()
A__ : int = F"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(A__ , """argv""" , A__ ):
run_mlm_flax.main()
A__ : str = get_results(A__ )
self.assertLess(result["""eval_perplexity"""] , 42 )
@slow
def __A ( self ):
A__ : List[str] = self.get_auto_remove_tmp_dir()
A__ : Union[str, Any] = F"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , """argv""" , A__ ):
run_ta_mlm_flax.main()
A__ : str = get_results(A__ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.4_2 )
@slow
def __A ( self ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
A__ : Any = 7 if get_gpu_count() > 1 else 2
A__ : List[Any] = self.get_auto_remove_tmp_dir()
A__ : int = F"""
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(A__ , """argv""" , A__ ):
run_flax_ner.main()
A__ : Union[str, Any] = get_results(A__ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.7_5 )
self.assertGreaterEqual(result["""eval_f1"""] , 0.3 )
@slow
def __A ( self ):
A__ : Optional[Any] = self.get_auto_remove_tmp_dir()
A__ : Tuple = F"""
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(A__ , """argv""" , A__ ):
run_qa.main()
A__ : Tuple = get_results(A__ )
self.assertGreaterEqual(result["""eval_f1"""] , 30 )
self.assertGreaterEqual(result["""eval_exact"""] , 30 )
| 718 |
def UpperCamelCase (lowercase_: str , lowercase_: str ) -> bool:
A__ : Union[str, Any] = len(lowercase_ )
A__ : List[Any] = len(lowercase_ )
A__ : List[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
A__ : str = True
for i in range(lowercase_ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
A__ : int = True
if a[i].islower():
A__ : Dict = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=True ) -> Tuple:
model.train()
_lowercase : List[str] = model(SCREAMING_SNAKE_CASE )
_lowercase : Union[str, Any] = F.mse_loss(SCREAMING_SNAKE_CASE , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(SCREAMING_SNAKE_CASE )
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ) -> List[Any]:
set_seed(42 )
_lowercase : List[str] = RegressionModel()
_lowercase : Any = deepcopy(SCREAMING_SNAKE_CASE )
_lowercase : List[Any] = RegressionDataset(length=80 )
_lowercase : Any = DataLoader(SCREAMING_SNAKE_CASE , batch_size=16 )
model.to(accelerator.device )
if sched:
_lowercase : int = AdamW(params=model.parameters() , lr=1E-3 )
_lowercase : List[str] = AdamW(params=ddp_model.parameters() , lr=1E-3 )
_lowercase : List[Any] = LambdaLR(SCREAMING_SNAKE_CASE , lr_lambda=lambda SCREAMING_SNAKE_CASE : epoch**0.65 )
_lowercase : Any = LambdaLR(SCREAMING_SNAKE_CASE , lr_lambda=lambda SCREAMING_SNAKE_CASE : epoch**0.65 )
# Make a copy of `model`
if sched:
_lowercase , _lowercase , _lowercase , _lowercase : Union[str, Any] = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
_lowercase , _lowercase : Any = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> int:
# Test when on a single CPU or GPU that the context manager does nothing
_lowercase , _lowercase , _lowercase : List[str] = get_training_setup(SCREAMING_SNAKE_CASE )
# Use a single batch
_lowercase , _lowercase : List[Any] = next(iter(SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
_lowercase , _lowercase : List[Any] = accelerator.gather((ddp_input, ddp_target) )
_lowercase , _lowercase : Dict = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(SCREAMING_SNAKE_CASE ):
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_337 + iteration )
_lowercase : List[Any] = ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> Tuple:
# Test on distributed setup that context manager behaves properly
_lowercase , _lowercase , _lowercase : Dict = get_training_setup(SCREAMING_SNAKE_CASE )
# Use a single batch
_lowercase , _lowercase : str = next(iter(SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
_lowercase , _lowercase : str = accelerator.gather((ddp_input, ddp_target) )
_lowercase , _lowercase : List[Any] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(SCREAMING_SNAKE_CASE ):
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_337 + iteration )
_lowercase : int = ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]
def __magic_name__ ( SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False ) -> List[str]:
_lowercase : List[str] = Accelerator(
split_batches=SCREAMING_SNAKE_CASE , dispatch_batches=SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
_lowercase , _lowercase , _lowercase : Any = get_training_setup(SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(SCREAMING_SNAKE_CASE ):
_lowercase , _lowercase : int = batch.values()
# Gather the distributed inputs and targs for the base model
_lowercase , _lowercase : Tuple = accelerator.gather((ddp_input, ddp_target) )
_lowercase , _lowercase : List[str] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(SCREAMING_SNAKE_CASE ):
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(SCREAMING_SNAKE_CASE ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_337 + iteration )
_lowercase : Optional[int] = ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE ) )]
GradientState._reset_state()
def __magic_name__ ( SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False ) -> Union[str, Any]:
_lowercase : Dict = Accelerator(
split_batches=SCREAMING_SNAKE_CASE , dispatch_batches=SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase : int = get_training_setup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(SCREAMING_SNAKE_CASE ):
_lowercase , _lowercase : str = batch.values()
# Gather the distributed inputs and targs for the base model
_lowercase , _lowercase : Dict = accelerator.gather((ddp_input, ddp_target) )
_lowercase , _lowercase : Tuple = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(SCREAMING_SNAKE_CASE ):
step_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
_lowercase : Optional[int] = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE ))
if accelerator.num_processes > 1:
check_model_parameters(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_337 + iteration )
GradientState._reset_state()
def __magic_name__ ( ) -> Any:
_lowercase : Any = Accelerator()
_lowercase : List[Any] = RegressionDataset(length=80 )
_lowercase : List[str] = DataLoader(SCREAMING_SNAKE_CASE , batch_size=16 )
_lowercase : List[str] = RegressionDataset(length=96 )
_lowercase : Union[str, Any] = DataLoader(SCREAMING_SNAKE_CASE , batch_size=16 )
_lowercase , _lowercase : Tuple = accelerator.prepare(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(SCREAMING_SNAKE_CASE )
if iteration < len(SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(SCREAMING_SNAKE_CASE )
if batch_num < len(SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def __magic_name__ ( ) -> Dict:
_lowercase : List[str] = Accelerator()
_lowercase : List[Any] = accelerator.state
if state.local_process_index == 0:
print('**Test `accumulate` gradient accumulation with dataloader break**' )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print('**Test NOOP `no_sync` context manager**' )
test_noop_sync(SCREAMING_SNAKE_CASE )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print('**Test Distributed `no_sync` context manager**' )
test_distributed_sync(SCREAMING_SNAKE_CASE )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
'**Test `accumulate` gradient accumulation, ' , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version('<' , '2.0' ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
'**Test `accumulate` gradient accumulation with optimizer and scheduler, ' , '`split_batches=False`, `dispatch_batches=False`**' , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
'**Test `accumulate` gradient accumulation with optimizer and scheduler, ' , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 66 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger()
@dataclass
class __magic_name__ :
__A : nn.Module
__A : List[nn.Module] = field(default_factory=__UpperCAmelCase )
__A : list = field(default_factory=__UpperCAmelCase )
def __snake_case ( self : List[str] , snake_case__ : List[str] , snake_case__ : Tensor , snake_case__ : Tensor ):
'''simple docstring'''
lowercase :List[str] = len(list(m.modules() ) ) == 1 or isinstance(snake_case__ , nn.Convad ) or isinstance(snake_case__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case__ )
def __call__( self : int , snake_case__ : Tensor ):
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case__ )
[x.remove() for x in self.handles]
return self
@property
def __snake_case ( self : int ):
'''simple docstring'''
return list(filter(lambda snake_case__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class __magic_name__ :
__A : nn.Module
__A : nn.Module
__A : int = 0
__A : List = field(default_factory=__UpperCAmelCase )
__A : List = field(default_factory=__UpperCAmelCase )
def __call__( self : Dict , snake_case__ : Tensor ):
'''simple docstring'''
lowercase :Dict = Tracker(self.dest )(snake_case__ ).parametrized
lowercase :Optional[Any] = Tracker(self.src )(snake_case__ ).parametrized
lowercase :List[str] = list(filter(lambda snake_case__ : type(snake_case__ ) not in self.src_skip , snake_case__ ) )
lowercase :Tuple = list(filter(lambda snake_case__ : type(snake_case__ ) not in self.dest_skip , snake_case__ ) )
if len(snake_case__ ) != len(snake_case__ ):
raise Exception(
f"""Numbers of operations are different. Source module has {len(snake_case__ )} operations while"""
f""" destination module has {len(snake_case__ )}.""" )
for dest_m, src_m in zip(snake_case__ , snake_case__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f"""Transfered from={src_m} to={dest_m}""" )
def lowerCamelCase (a_ :str , a_ :ResNetConfig , a_ :Path , a_ :bool = True) -> Optional[Any]:
print(F"""Converting {name}...""")
with torch.no_grad():
lowercase :Union[str, Any] = timm.create_model(a_ , pretrained=a_).eval()
lowercase :Tuple = ResNetForImageClassification(a_).eval()
lowercase :int = ModuleTransfer(src=a_ , dest=a_)
lowercase :List[Any] = torch.randn((1, 3, 224, 224))
module_transfer(a_)
assert torch.allclose(from_model(a_) , our_model(a_).logits), "The model logits don't match the original one."
lowercase :List[Any] = F"""resnet{'-'.join(name.split('resnet'))}"""
print(a_)
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add model''' , use_temp_dir=a_ , )
# we can use the convnext one
lowercase :Any = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''')
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add image processor''' , use_temp_dir=a_ , )
print(F"""Pushed {checkpoint_name}""")
def lowerCamelCase (a_ :Path , a_ :str = None , a_ :bool = True) -> int:
lowercase :Optional[Any] = '''imagenet-1k-id2label.json'''
lowercase :Union[str, Any] = 1000
lowercase :Any = (1, num_labels)
lowercase :Tuple = '''huggingface/label-files'''
lowercase :List[str] = num_labels
lowercase :Union[str, Any] = json.load(open(hf_hub_download(a_ , a_ , repo_type='''dataset''') , '''r'''))
lowercase :Any = {int(a_): v for k, v in idalabel.items()}
lowercase :str = idalabel
lowercase :Any = {v: k for k, v in idalabel.items()}
lowercase :Union[str, Any] = partial(a_ , num_labels=a_ , idalabel=a_ , labelaid=a_)
lowercase :Optional[int] = {
'''resnet18''': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type='''basic'''),
'''resnet26''': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck'''),
'''resnet34''': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type='''basic'''),
'''resnet50''': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck'''),
'''resnet101''': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck'''),
'''resnet152''': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck'''),
}
if model_name:
convert_weight_and_push(a_ , names_to_config[model_name] , a_ , a_)
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(a_ , a_ , a_ , a_)
return config, expected_shape
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 677 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Union[str, Any] , __A: Dict , __A: Tuple=13 , __A: Union[str, Any]=7 , __A: Dict=True , __A: Dict=True , __A: Dict=True , __A: Tuple=True , __A: List[str]=99 , __A: Tuple=32 , __A: Union[str, Any]=2 , __A: List[str]=4 , __A: Tuple=37 , __A: int="gelu" , __A: Union[str, Any]=0.1 , __A: int=0.1 , __A: Any=512 , __A: int=16 , __A: str=2 , __A: int=0.0_2 , __A: int=3 , __A: Dict=4 , __A: Union[str, Any]=None , __A: int=0 , ):
'''simple docstring'''
a__ = parent
a__ = batch_size
a__ = seq_length
a__ = is_training
a__ = use_input_mask
a__ = use_token_type_ids
a__ = use_labels
a__ = vocab_size
a__ = hidden_size
a__ = num_hidden_layers
a__ = num_attention_heads
a__ = intermediate_size
a__ = hidden_act
a__ = hidden_dropout_prob
a__ = attention_probs_dropout_prob
a__ = max_position_embeddings
a__ = type_vocab_size
a__ = type_sequence_label_size
a__ = initializer_range
a__ = num_labels
a__ = num_choices
a__ = scope
a__ = projection_dim
def lowercase ( self: Union[str, Any] ):
'''simple docstring'''
a__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
a__ = random_attention_mask([self.batch_size, self.seq_length] )
a__ = None
if self.use_token_type_ids:
a__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__ = None
a__ = None
a__ = None
if self.use_labels:
a__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ = ids_tensor([self.batch_size] , self.num_choices )
a__ = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__A , initializer_range=self.initializer_range , )
a__ = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase ( self: Optional[int] , __A: str , __A: Tuple , __A: List[Any] , __A: Any , __A: int , __A: List[str] , __A: Optional[Any] ):
'''simple docstring'''
a__ = TFDPRContextEncoder(config=__A )
a__ = model(__A , attention_mask=__A , token_type_ids=__A )
a__ = model(__A , token_type_ids=__A )
a__ = model(__A )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def lowercase ( self: Tuple , __A: str , __A: int , __A: Tuple , __A: Union[str, Any] , __A: Any , __A: List[str] , __A: Optional[int] ):
'''simple docstring'''
a__ = TFDPRQuestionEncoder(config=__A )
a__ = model(__A , attention_mask=__A , token_type_ids=__A )
a__ = model(__A , token_type_ids=__A )
a__ = model(__A )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def lowercase ( self: str , __A: List[str] , __A: Union[str, Any] , __A: Any , __A: Optional[Any] , __A: int , __A: Dict , __A: int ):
'''simple docstring'''
a__ = TFDPRReader(config=__A )
a__ = model(__A , attention_mask=__A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def lowercase ( self: Tuple ):
'''simple docstring'''
a__ = self.prepare_config_and_inputs()
(
(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,(
a__
) ,
) = config_and_inputs
a__ = {'''input_ids''': input_ids}
return config, inputs_dict
@require_tf
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE =(
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
_SCREAMING_SNAKE_CASE ={'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
def lowercase ( self: List[str] ):
'''simple docstring'''
a__ = TFDPRModelTester(self )
a__ = ConfigTester(self , config_class=__A , hidden_size=37 )
def lowercase ( self: str ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase ( self: Dict ):
'''simple docstring'''
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__A )
def lowercase ( self: Optional[int] ):
'''simple docstring'''
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__A )
def lowercase ( self: Optional[Any] ):
'''simple docstring'''
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__A )
@slow
def lowercase ( self: Optional[int] ):
'''simple docstring'''
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ = TFDPRContextEncoder.from_pretrained(__A )
self.assertIsNotNone(__A )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ = TFDPRContextEncoder.from_pretrained(__A )
self.assertIsNotNone(__A )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ = TFDPRQuestionEncoder.from_pretrained(__A )
self.assertIsNotNone(__A )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ = TFDPRReader.from_pretrained(__A )
self.assertIsNotNone(__A )
@require_tf
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase ( self: str ):
'''simple docstring'''
a__ = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''' )
a__ = tf.constant(
[[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]] ) # [CLS] hello, is my dog cute? [SEP]
a__ = model(__A )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
a__ = tf.constant(
[
[
0.0_3_2_3_6_2_5_3,
0.1_2_7_5_3_3_3_5,
0.1_6_8_1_8_5_0_9,
0.0_0_2_7_9_7_8_6,
0.3_8_9_6_9_3_3,
0.2_4_2_6_4_9_4_5,
0.2_1_7_8_9_7_1,
-0.0_2_3_3_5_2_2_7,
-0.0_8_4_8_1_9_5_9,
-0.1_4_3_2_4_1_1_7,
]
] )
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 200 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : List[Any] = logging.get_logger(__name__)
__a : Tuple = {
'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='align_text_model'
def __init__( self: Tuple , __A: Optional[int]=30522 , __A: Tuple=768 , __A: Any=12 , __A: Any=12 , __A: Dict=3072 , __A: Tuple="gelu" , __A: Union[str, Any]=0.1 , __A: List[str]=0.1 , __A: Optional[int]=512 , __A: Tuple=2 , __A: str=0.0_2 , __A: int=1e-12 , __A: Optional[int]=0 , __A: Optional[int]="absolute" , __A: List[Any]=True , **__A: Tuple , ):
'''simple docstring'''
super().__init__(**__A )
a__ = vocab_size
a__ = hidden_size
a__ = num_hidden_layers
a__ = num_attention_heads
a__ = hidden_act
a__ = intermediate_size
a__ = hidden_dropout_prob
a__ = attention_probs_dropout_prob
a__ = max_position_embeddings
a__ = type_vocab_size
a__ = initializer_range
a__ = layer_norm_eps
a__ = position_embedding_type
a__ = use_cache
a__ = pad_token_id
@classmethod
def lowercase ( cls: Any , __A: Union[str, os.PathLike] , **__A: Tuple ):
'''simple docstring'''
cls._set_token_in_kwargs(__A )
a__ ,a__ = cls.get_config_dict(__A , **__A )
# get the text config dict if we are loading from AlignConfig
if config_dict.get('''model_type''' ) == "align":
a__ = 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 ( __snake_case ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='align_vision_model'
def __init__( self: Dict , __A: int = 3 , __A: int = 600 , __A: float = 2.0 , __A: float = 3.1 , __A: int = 8 , __A: List[int] = [3, 3, 5, 3, 5, 5, 3] , __A: List[int] = [32, 16, 24, 40, 80, 112, 192] , __A: List[int] = [16, 24, 40, 80, 112, 192, 320] , __A: List[int] = [] , __A: List[int] = [1, 2, 2, 2, 1, 2, 1] , __A: List[int] = [1, 2, 2, 3, 3, 4, 1] , __A: List[int] = [1, 6, 6, 6, 6, 6, 6] , __A: float = 0.2_5 , __A: str = "swish" , __A: int = 2560 , __A: str = "mean" , __A: float = 0.0_2 , __A: float = 0.0_0_1 , __A: float = 0.9_9 , __A: float = 0.2 , **__A: List[Any] , ):
'''simple docstring'''
super().__init__(**__A )
a__ = num_channels
a__ = image_size
a__ = width_coefficient
a__ = depth_coefficient
a__ = depth_divisor
a__ = kernel_sizes
a__ = in_channels
a__ = out_channels
a__ = depthwise_padding
a__ = strides
a__ = num_block_repeats
a__ = expand_ratios
a__ = squeeze_expansion_ratio
a__ = hidden_act
a__ = hidden_dim
a__ = pooling_type
a__ = initializer_range
a__ = batch_norm_eps
a__ = batch_norm_momentum
a__ = drop_connect_rate
a__ = sum(__A ) * 4
@classmethod
def lowercase ( cls: Dict , __A: Union[str, os.PathLike] , **__A: List[Any] ):
'''simple docstring'''
cls._set_token_in_kwargs(__A )
a__ ,a__ = cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get('''model_type''' ) == "align":
a__ = 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 ( __snake_case ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='align'
_SCREAMING_SNAKE_CASE =True
def __init__( self: Optional[int] , __A: Optional[int]=None , __A: Dict=None , __A: List[str]=640 , __A: Optional[int]=1.0 , __A: str=0.0_2 , **__A: List[str] , ):
'''simple docstring'''
super().__init__(**__A )
if text_config is None:
a__ = {}
logger.info('''text_config is None. Initializing the AlignTextConfig with default values.''' )
if vision_config is None:
a__ = {}
logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.''' )
a__ = AlignTextConfig(**__A )
a__ = AlignVisionConfig(**__A )
a__ = projection_dim
a__ = temperature_init_value
a__ = initializer_range
@classmethod
def lowercase ( cls: Dict , __A: AlignTextConfig , __A: AlignVisionConfig , **__A: str ):
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__A )
def lowercase ( self: Any ):
'''simple docstring'''
a__ = copy.deepcopy(self.__dict__ )
a__ = self.text_config.to_dict()
a__ = self.vision_config.to_dict()
a__ = self.__class__.model_type
return output
| 200 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
}
__UpperCAmelCase = {
'''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
'''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}
__UpperCAmelCase = {
'''ctrl''': 256,
}
__UpperCAmelCase = {
'''Pregnancy''': 168_629,
'''Christianity''': 7_675,
'''Explain''': 106_423,
'''Fitness''': 63_440,
'''Saving''': 63_163,
'''Ask''': 27_171,
'''Ass''': 95_985,
'''Joke''': 163_509,
'''Questions''': 45_622,
'''Thoughts''': 49_605,
'''Retail''': 52_342,
'''Feminism''': 164_338,
'''Writing''': 11_992,
'''Atheism''': 192_263,
'''Netflix''': 48_616,
'''Computing''': 39_639,
'''Opinion''': 43_213,
'''Alone''': 44_967,
'''Funny''': 58_917,
'''Gaming''': 40_358,
'''Human''': 4_088,
'''India''': 1_331,
'''Joker''': 77_138,
'''Diet''': 36_206,
'''Legal''': 11_859,
'''Norman''': 4_939,
'''Tip''': 72_689,
'''Weight''': 52_343,
'''Movies''': 46_273,
'''Running''': 23_425,
'''Science''': 2_090,
'''Horror''': 37_793,
'''Confession''': 60_572,
'''Finance''': 12_250,
'''Politics''': 16_360,
'''Scary''': 191_985,
'''Support''': 12_654,
'''Technologies''': 32_516,
'''Teenage''': 66_160,
'''Event''': 32_769,
'''Learned''': 67_460,
'''Notion''': 182_770,
'''Wikipedia''': 37_583,
'''Books''': 6_665,
'''Extract''': 76_050,
'''Confessions''': 102_701,
'''Conspiracy''': 75_932,
'''Links''': 63_674,
'''Narcissus''': 150_425,
'''Relationship''': 54_766,
'''Relationships''': 134_796,
'''Reviews''': 41_671,
'''News''': 4_256,
'''Translation''': 26_820,
'''multilingual''': 128_406,
}
def _snake_case ( A ) -> Optional[int]:
lowerCAmelCase__ = set()
lowerCAmelCase__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCAmelCase__ = char
lowerCAmelCase__ = set(A )
return pairs
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : List[Any] = VOCAB_FILES_NAMES
lowercase__ : Dict = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Dict = CONTROL_CODES
def __init__( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_="<unk>" , **lowerCamelCase_ ) -> List[Any]:
super().__init__(unk_token=lowerCamelCase_ , **lowerCamelCase_ )
with open(lowerCamelCase_ , encoding='''utf-8''' ) as vocab_handle:
lowerCAmelCase__ = json.load(lowerCamelCase_ )
lowerCAmelCase__ = {v: k for k, v in self.encoder.items()}
with open(lowerCamelCase_ , encoding='''utf-8''' ) as merges_handle:
lowerCAmelCase__ = merges_handle.read().split('''\n''' )[1:-1]
lowerCAmelCase__ = [tuple(merge.split() ) for merge in merges]
lowerCAmelCase__ = dict(zip(lowerCamelCase_ , range(len(lowerCamelCase_ ) ) ) )
lowerCAmelCase__ = {}
@property
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
return len(self.encoder )
def __SCREAMING_SNAKE_CASE ( self ) -> int:
return dict(self.encoder , **self.added_tokens_encoder )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> List[Any]:
if token in self.cache:
return self.cache[token]
lowerCAmelCase__ = tuple(lowerCamelCase_ )
lowerCAmelCase__ = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
lowerCAmelCase__ = get_pairs(lowerCamelCase_ )
if not pairs:
return token
while True:
lowerCAmelCase__ = min(lowerCamelCase_ , key=lambda lowerCamelCase_ : self.bpe_ranks.get(lowerCamelCase_ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase__ , lowerCAmelCase__ = bigram
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
while i < len(lowerCamelCase_ ):
try:
lowerCAmelCase__ = word.index(lowerCamelCase_ , lowerCamelCase_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase__ = j
if word[i] == first and i < len(lowerCamelCase_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase__ = tuple(lowerCamelCase_ )
lowerCAmelCase__ = new_word
if len(lowerCamelCase_ ) == 1:
break
else:
lowerCAmelCase__ = get_pairs(lowerCamelCase_ )
lowerCAmelCase__ = '''@@ '''.join(lowerCamelCase_ )
lowerCAmelCase__ = word[:-4]
lowerCAmelCase__ = word
return word
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> int:
lowerCAmelCase__ = []
lowerCAmelCase__ = re.findall(r'''\S+\n?''' , lowerCamelCase_ )
for token in words:
split_tokens.extend(list(self.bpe(lowerCamelCase_ ).split(''' ''' ) ) )
return split_tokens
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> int:
return self.encoder.get(lowerCamelCase_ , self.encoder.get(self.unk_token ) )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Union[str, Any]:
return self.decoder.get(lowerCamelCase_ , self.unk_token )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Tuple:
lowerCAmelCase__ = ''' '''.join(lowerCamelCase_ ).replace('''@@ ''' , '''''' ).strip()
return out_string
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> Tuple[str]:
if not os.path.isdir(lowerCamelCase_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCAmelCase__ = os.path.join(
lowerCamelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCAmelCase__ = os.path.join(
lowerCamelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCamelCase_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase_ , ensure_ascii=lowerCamelCase_ ) + '''\n''' )
lowerCAmelCase__ = 0
with open(lowerCamelCase_ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase_ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
lowerCAmelCase__ = token_index
writer.write(''' '''.join(lowerCamelCase_ ) + '''\n''' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far) | 90 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def A__ ( A_ ) -> str:
_lowercase = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(A_ , A_ )
def A__ ( A_ ) -> Optional[Any]:
_lowercase , _lowercase = emb.weight.shape
_lowercase = nn.Linear(A_ , A_ , bias=A_ )
_lowercase = emb.weight.data
return lin_layer
def A__ ( A_ , A_=None ) -> int:
_lowercase = {}
for old_key in state_dict.keys():
_lowercase = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
_lowercase = key.replace("moe_layer.experts.0" , F"""ffn.experts.expert_{expert_idx}""" )
else:
_lowercase = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
_lowercase = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
_lowercase = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
_lowercase = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
_lowercase = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
_lowercase = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
_lowercase = key.replace("final_layer_norm" , "ff_layer_norm" )
_lowercase = state_dict[old_key]
return new_dict
def A__ ( A_ , A_ , A_ , A_ , A_ = WEIGHTS_NAME ) -> Any:
_lowercase = []
_lowercase = 0
os.makedirs(A_ , exist_ok=A_ )
for expert in range(A_ ):
_lowercase = switch_checkpoint_path + F"""-rank-{expert}.pt"""
if os.path.isfile(A_ ):
_lowercase = torch.load(A_ )["model"]
remove_ignore_keys_(A_ )
_lowercase = rename_fairseq_keys(A_ , A_ )
_lowercase = os.path.join(
A_ , weights_name.replace(".bin" , F"""-{len(A_ )+1:05d}-of-???.bin""" ) )
torch.save(A_ , A_ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(A_ )[0]].dtype )
# Add the last block
_lowercase = os.path.join(A_ , weights_name.replace(".bin" , F"""-{len(A_ )+1:05d}-of-???.bin""" ) )
_lowercase = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(A_ )
_lowercase = rename_fairseq_keys(A_ , A_ )
_lowercase = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(A_ ) == 1:
_lowercase = os.path.join(A_ , A_ )
torch.save(A_ , A_ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(A_ , A_ )
# Otherwise, let's build the index
_lowercase = {}
for idx, shard in enumerate(A_ ):
_lowercase = weights_name.replace(".bin" , F"""-{idx+1:05d}-of-{len(A_ ):05d}.bin""" )
_lowercase = os.path.join(A_ , weights_name.replace(".bin" , F"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(A_ , os.path.join(A_ , A_ ) )
for key in shard:
_lowercase = shard_file
# Add the metadata
_lowercase = {"total_size": total_size}
_lowercase = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(A_ , A_ ) , "w" , encoding="utf-8" ) as f:
_lowercase = json.dumps(A_ , indent=2 , sort_keys=A_ ) + "\n"
f.write(A_ )
return metadata, index
if __name__ == "__main__":
__magic_name__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--nllb_moe_checkpoint_path''',
default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
type=str,
required=False,
help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
)
parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
type=str,
required=False,
help='''Path to the output pytorch model.''',
)
__magic_name__ : Union[str, Any] = parser.parse_args()
__magic_name__ , __magic_name__ : str = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
128,
args.dtype,
)
__magic_name__ : List[str] = NllbMoeConfig.from_pretrained(
'''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
)
config.save_pretrained(args.pytorch_dump_folder_path)
__magic_name__ : Tuple = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('''Done''')
model.save_pretrained(args.pytorch_dump_folder_path)
| 497 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class a ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : int = 'wavlm'
def __init__( self : Union[str, Any] , lowerCamelCase__ : str=32 , lowerCamelCase__ : int=768 , lowerCamelCase__ : List[Any]=12 , lowerCamelCase__ : Dict=12 , lowerCamelCase__ : List[Any]=3_072 , lowerCamelCase__ : Optional[Any]="gelu" , lowerCamelCase__ : Optional[Any]=0.1 , lowerCamelCase__ : Any=0.1 , lowerCamelCase__ : Optional[Any]=0.1 , lowerCamelCase__ : List[Any]=0.0 , lowerCamelCase__ : Union[str, Any]=0.1 , lowerCamelCase__ : Tuple=0.1 , lowerCamelCase__ : List[Any]=0.0_2 , lowerCamelCase__ : Any=1e-5 , lowerCamelCase__ : Dict="group" , lowerCamelCase__ : Optional[int]="gelu" , lowerCamelCase__ : Dict=(512, 512, 512, 512, 512, 512, 512) , lowerCamelCase__ : List[Any]=(5, 2, 2, 2, 2, 2, 2) , lowerCamelCase__ : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) , lowerCamelCase__ : Any=False , lowerCamelCase__ : List[str]=128 , lowerCamelCase__ : Optional[Any]=16 , lowerCamelCase__ : Union[str, Any]=320 , lowerCamelCase__ : Optional[int]=800 , lowerCamelCase__ : int=False , lowerCamelCase__ : Tuple=True , lowerCamelCase__ : List[Any]=0.0_5 , lowerCamelCase__ : Optional[Any]=10 , lowerCamelCase__ : int=2 , lowerCamelCase__ : str=0.0 , lowerCamelCase__ : Optional[Any]=10 , lowerCamelCase__ : Optional[Any]=320 , lowerCamelCase__ : Any=2 , lowerCamelCase__ : Tuple=0.1 , lowerCamelCase__ : Dict=100 , lowerCamelCase__ : Any=256 , lowerCamelCase__ : Dict=256 , lowerCamelCase__ : Optional[Any]=0.1 , lowerCamelCase__ : Optional[int]="mean" , lowerCamelCase__ : List[Any]=False , lowerCamelCase__ : Tuple=False , lowerCamelCase__ : Tuple=256 , lowerCamelCase__ : Union[str, Any]=(512, 512, 512, 512, 1_500) , lowerCamelCase__ : List[str]=(5, 3, 3, 1, 1) , lowerCamelCase__ : str=(1, 2, 3, 1, 1) , lowerCamelCase__ : Any=512 , lowerCamelCase__ : List[Any]=80 , lowerCamelCase__ : Any=0 , lowerCamelCase__ : str=1 , lowerCamelCase__ : List[Any]=2 , lowerCamelCase__ : Optional[int]=False , lowerCamelCase__ : Optional[Any]=3 , lowerCamelCase__ : Dict=2 , lowerCamelCase__ : Optional[Any]=3 , lowerCamelCase__ : Tuple=None , **lowerCamelCase__ : Dict , ) -> Tuple:
"""simple docstring"""
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
__lowercase = hidden_size
__lowercase = feat_extract_norm
__lowercase = feat_extract_activation
__lowercase = list(lowerCamelCase__ )
__lowercase = list(lowerCamelCase__ )
__lowercase = list(lowerCamelCase__ )
__lowercase = conv_bias
__lowercase = num_buckets
__lowercase = max_bucket_distance
__lowercase = num_conv_pos_embeddings
__lowercase = num_conv_pos_embedding_groups
__lowercase = len(self.conv_dim )
__lowercase = num_hidden_layers
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = num_attention_heads
__lowercase = hidden_dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = feat_proj_dropout
__lowercase = final_dropout
__lowercase = layerdrop
__lowercase = layer_norm_eps
__lowercase = initializer_range
__lowercase = num_ctc_classes
__lowercase = vocab_size
__lowercase = do_stable_layer_norm
__lowercase = use_weighted_layer_sum
__lowercase = classifier_proj_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)`, but is `len(config.conv_dim) ='''
f' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowercase = apply_spec_augment
__lowercase = mask_time_prob
__lowercase = mask_time_length
__lowercase = mask_time_min_masks
__lowercase = mask_feature_prob
__lowercase = mask_feature_length
# parameters for pretraining with codevector quantized representations
__lowercase = num_codevectors_per_group
__lowercase = num_codevector_groups
__lowercase = contrastive_logits_temperature
__lowercase = num_negatives
__lowercase = codevector_dim
__lowercase = proj_codevector_dim
__lowercase = diversity_loss_weight
# ctc loss
__lowercase = ctc_loss_reduction
__lowercase = ctc_zero_infinity
# adapter
__lowercase = add_adapter
__lowercase = adapter_kernel_size
__lowercase = adapter_stride
__lowercase = num_adapter_layers
__lowercase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowercase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowercase = list(lowerCamelCase__ )
__lowercase = list(lowerCamelCase__ )
__lowercase = list(lowerCamelCase__ )
__lowercase = xvector_output_dim
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 362 |
from __future__ import annotations
def _A( UpperCamelCase__ : list[int] , UpperCamelCase__ : list[int] , UpperCamelCase__ : int ) -> tuple[float, list[float]]:
'''simple docstring'''
__lowercase = list(range(len(UpperCamelCase__ ) ) )
__lowercase = [v / w for v, w in zip(UpperCamelCase__ , UpperCamelCase__ )]
index.sort(key=lambda UpperCamelCase__ : ratio[i] , reverse=UpperCamelCase__ )
__lowercase = 0
__lowercase = [0] * len(UpperCamelCase__ )
for i in index:
if weight[i] <= capacity:
__lowercase = 1
max_value += value[i]
capacity -= weight[i]
else:
__lowercase = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 362 | 1 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ = logging.get_logger(__name__)
class _snake_case ( _a ):
_A : Tuple = '''linear'''
_A : List[Any] = '''cosine'''
_A : List[Any] = '''cosine_with_restarts'''
_A : List[Any] = '''polynomial'''
_A : Any = '''constant'''
_A : Optional[Any] = '''constant_with_warmup'''
_A : Tuple = '''piecewise_constant'''
def A_ ( snake_case , snake_case = -1 ):
return LambdaLR(snake_case , lambda snake_case : 1 , last_epoch=snake_case )
def A_ ( snake_case , snake_case , snake_case = -1 ):
def lr_lambda(snake_case ):
if current_step < num_warmup_steps:
return float(snake_case ) / float(max(1.0 , snake_case ) )
return 1.0
return LambdaLR(snake_case , snake_case , last_epoch=snake_case )
def A_ ( snake_case , snake_case , snake_case = -1 ):
SCREAMING_SNAKE_CASE:str = {}
SCREAMING_SNAKE_CASE:int = step_rules.split("," )
for rule_str in rule_list[:-1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:Optional[Any] = rule_str.split(":" )
SCREAMING_SNAKE_CASE:Any = int(snake_case )
SCREAMING_SNAKE_CASE:Optional[Any] = float(snake_case )
SCREAMING_SNAKE_CASE:Optional[int] = value
SCREAMING_SNAKE_CASE:int = float(rule_list[-1] )
def create_rules_function(snake_case , snake_case ):
def rule_func(snake_case ) -> float:
SCREAMING_SNAKE_CASE:List[str] = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(snake_case ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
SCREAMING_SNAKE_CASE:Optional[int] = create_rules_function(snake_case , snake_case )
return LambdaLR(snake_case , snake_case , last_epoch=snake_case )
def A_ ( snake_case , snake_case , snake_case , snake_case=-1 ):
def lr_lambda(snake_case ):
if current_step < num_warmup_steps:
return float(snake_case ) / float(max(1 , snake_case ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(snake_case , snake_case , snake_case )
def A_ ( snake_case , snake_case , snake_case , snake_case = 0.5 , snake_case = -1 ):
def lr_lambda(snake_case ):
if current_step < num_warmup_steps:
return float(snake_case ) / float(max(1 , snake_case ) )
SCREAMING_SNAKE_CASE:Dict = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(snake_case ) * 2.0 * progress )) )
return LambdaLR(snake_case , snake_case , snake_case )
def A_ ( snake_case , snake_case , snake_case , snake_case = 1 , snake_case = -1 ):
def lr_lambda(snake_case ):
if current_step < num_warmup_steps:
return float(snake_case ) / float(max(1 , snake_case ) )
SCREAMING_SNAKE_CASE:int = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(snake_case ) * progress) % 1.0) )) )
return LambdaLR(snake_case , snake_case , snake_case )
def A_ ( snake_case , snake_case , snake_case , snake_case=1e-7 , snake_case=1.0 , snake_case=-1 ):
SCREAMING_SNAKE_CASE:int = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''' )
def lr_lambda(snake_case ):
if current_step < num_warmup_steps:
return float(snake_case ) / float(max(1 , snake_case ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
SCREAMING_SNAKE_CASE:List[Any] = lr_init - lr_end
SCREAMING_SNAKE_CASE:List[Any] = num_training_steps - num_warmup_steps
SCREAMING_SNAKE_CASE:Tuple = 1 - (current_step - num_warmup_steps) / decay_steps
SCREAMING_SNAKE_CASE:int = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(snake_case , snake_case , snake_case )
A_ = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def A_ ( snake_case , snake_case , snake_case = None , snake_case = None , snake_case = None , snake_case = 1 , snake_case = 1.0 , snake_case = -1 , ):
SCREAMING_SNAKE_CASE:Optional[Any] = SchedulerType(snake_case )
SCREAMING_SNAKE_CASE:Tuple = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(snake_case , last_epoch=snake_case )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(snake_case , step_rules=snake_case , last_epoch=snake_case )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.''' )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(snake_case , num_warmup_steps=snake_case , last_epoch=snake_case )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.''' )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
snake_case , num_warmup_steps=snake_case , num_training_steps=snake_case , num_cycles=snake_case , last_epoch=snake_case , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
snake_case , num_warmup_steps=snake_case , num_training_steps=snake_case , power=snake_case , last_epoch=snake_case , )
return schedule_func(
snake_case , num_warmup_steps=snake_case , num_training_steps=snake_case , last_epoch=snake_case )
| 143 |
'''simple docstring'''
def A_ ( snake_case = 600851475143 ):
try:
SCREAMING_SNAKE_CASE:str = int(snake_case )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
SCREAMING_SNAKE_CASE:Optional[Any] = 1
SCREAMING_SNAKE_CASE:Optional[Any] = 2
while i * i <= n:
while n % i == 0:
SCREAMING_SNAKE_CASE:List[Any] = i
n //= i
i += 1
if n > 1:
SCREAMING_SNAKE_CASE:List[str] = n
return int(snake_case )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 143 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor | 717 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
raise ValueError(
"Warning: upper bound of deterministic test is exceeded. "
"Pass allow_probable=True to allow probabilistic test. "
"A return value of True indicates a probable prime." )
# array bounds provided by analysis
a_ : Optional[Any] = [
2_047,
1_373_653,
25_326_001,
3_215_031_751,
2_152_302_898_747,
3_474_749_660_383,
341_550_071_728_321,
1,
3_825_123_056_546_413_051,
1,
1,
318_665_857_834_031_151_167_461,
3_317_044_064_679_887_385_961_981,
]
a_ : Optional[int] = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(SCREAMING_SNAKE_CASE__, 1 ):
if n < _p:
# then we have our last prime to check
a_ : List[Any] = primes[:idx]
break
a_ , a_ : Tuple = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
a_ : str = False
for r in range(SCREAMING_SNAKE_CASE__ ):
a_ : List[str] = pow(SCREAMING_SNAKE_CASE__, d * 2**r, SCREAMING_SNAKE_CASE__ )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
a_ : List[Any] = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def lowerCAmelCase_ ( ) -> None:
assert not miller_rabin(561 )
assert miller_rabin(563 )
# 2047
assert not miller_rabin(838_201 )
assert miller_rabin(838_207 )
# 1_373_653
assert not miller_rabin(17_316_001 )
assert miller_rabin(17_316_017 )
# 25_326_001
assert not miller_rabin(3_078_386_641 )
assert miller_rabin(3_078_386_653 )
# 3_215_031_751
assert not miller_rabin(1_713_045_574_801 )
assert miller_rabin(1_713_045_574_819 )
# 2_152_302_898_747
assert not miller_rabin(2_779_799_728_307 )
assert miller_rabin(2_779_799_728_327 )
# 3_474_749_660_383
assert not miller_rabin(113_850_023_909_441 )
assert miller_rabin(113_850_023_909_527 )
# 341_550_071_728_321
assert not miller_rabin(1_275_041_018_848_804_351 )
assert miller_rabin(1_275_041_018_848_804_391 )
# 3_825_123_056_546_413_051
assert not miller_rabin(79_666_464_458_507_787_791_867 )
assert miller_rabin(79_666_464_458_507_787_791_951 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(552_840_677_446_647_897_660_333 )
assert miller_rabin(552_840_677_446_647_897_660_359 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin() | 370 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def SCREAMING_SNAKE_CASE_ ( snake_case : Tuple = 8 )-> str:
_lowerCamelCase = ascii_letters + digits + punctuation
return "".join(secrets.choice(snake_case ) for _ in range(snake_case ) )
def SCREAMING_SNAKE_CASE_ ( snake_case : Any , snake_case : Dict )-> Union[str, Any]:
i -= len(snake_case )
_lowerCamelCase = i // 3
_lowerCamelCase = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
_lowerCamelCase = (
chars_incl
+ random(snake_case , quotient + remainder )
+ random(snake_case , snake_case )
+ random(snake_case , snake_case )
)
_lowerCamelCase = list(snake_case )
shuffle(snake_case )
return "".join(snake_case )
# random is a generalised function for letters, characters and numbers
def SCREAMING_SNAKE_CASE_ ( snake_case : List[Any] , snake_case : List[str] )-> Union[str, Any]:
return "".join(secrets.choice(snake_case ) for _ in range(snake_case ) )
def SCREAMING_SNAKE_CASE_ ( snake_case : List[Any] , snake_case : Dict )-> Optional[Any]:
pass # Put your code here...
def SCREAMING_SNAKE_CASE_ ( snake_case : Optional[int] , snake_case : List[str] )-> int:
pass # Put your code here...
def SCREAMING_SNAKE_CASE_ ( snake_case : Optional[Any] , snake_case : Tuple )-> int:
pass # Put your code here...
def SCREAMING_SNAKE_CASE_ ( snake_case : Optional[int] , snake_case : Optional[Any] = 8 )-> Optional[int]:
if len(snake_case ) < min_length:
# Your Password must be at least 8 characters long
return False
_lowerCamelCase = any(char in ascii_uppercase for char in password )
_lowerCamelCase = any(char in ascii_lowercase for char in password )
_lowerCamelCase = any(char in digits for char in password )
_lowerCamelCase = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def SCREAMING_SNAKE_CASE_ ( )-> str:
_lowerCamelCase = int(input('Please indicate the max length of your password: ' ).strip() )
_lowerCamelCase = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' , password_generator(snake_case ) )
print(
'Alternative Password generated:' , alternative_password_generator(snake_case , snake_case ) , )
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 650 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : int , _lowerCAmelCase : str , _lowerCAmelCase : List[str]=13 , _lowerCAmelCase : List[str]=7 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=99 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[int]=5 , _lowerCAmelCase : Any=4 , _lowerCAmelCase : Tuple=37 , _lowerCAmelCase : str="gelu" , _lowerCAmelCase : List[str]=0.1 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Union[str, Any]=512 , _lowerCAmelCase : Dict=16 , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : Optional[int]=0.02 , _lowerCAmelCase : int=3 , _lowerCAmelCase : Dict=4 , _lowerCAmelCase : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _a ( self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = DistilBertModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> List[str]:
"""simple docstring"""
__lowercase = DistilBertForMaskedLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertForQuestionAnswering(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , start_positions=_lowerCAmelCase , end_positions=_lowerCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _a ( self : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForSequenceClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForTokenClassification(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _a ( self : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ) -> str:
"""simple docstring"""
__lowercase = self.num_choices
__lowercase = DistilBertForMultipleChoice(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
((__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase)) = config_and_inputs
__lowercase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
__snake_case :Dict = (
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case :Tuple = True
__snake_case :Tuple = True
__snake_case :List[str] = True
__snake_case :Optional[int] = True
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = DistilBertModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , dim=37 )
def _a ( self : Dict ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*_lowerCAmelCase )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*_lowerCAmelCase )
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*_lowerCAmelCase )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*_lowerCAmelCase )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*_lowerCAmelCase )
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*_lowerCAmelCase )
@slow
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = DistilBertModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@slow
@require_torch_gpu
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
__lowercase = True
__lowercase = model_class(config=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = torch.jit.trace(
_lowerCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """traced_model.pt""" ) )
__lowercase = torch.jit.load(os.path.join(_lowerCAmelCase , """traced_model.pt""" ) , map_location=_lowerCAmelCase )
loaded(inputs_dict["""input_ids"""].to(_lowerCAmelCase ) , inputs_dict["""attention_mask"""].to(_lowerCAmelCase ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__lowercase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__lowercase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__lowercase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCAmelCase , atol=1e-4 ) )
| 80 | 0 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCamelCase ( a ) -> str:
'''simple docstring'''
__magic_name__ = args.pruning_method
__magic_name__ = args.threshold
__magic_name__ = args.model_name_or_path.rstrip('''/''' )
__magic_name__ = args.target_model_path
print(F'''Load fine-pruned model from {model_name_or_path}''' )
__magic_name__ = torch.load(os.path.join(a , '''pytorch_model.bin''' ) )
__magic_name__ = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__magic_name__ = tensor
print(F'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
__magic_name__ = tensor
print(F'''Copied layer {name}''' )
elif "bias" in name:
__magic_name__ = tensor
print(F'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
__magic_name__ = MagnitudeBinarizer.apply(inputs=a , threshold=a )
__magic_name__ = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__magic_name__ = name[:-6]
__magic_name__ = model[F'''{prefix_}mask_scores''']
__magic_name__ = TopKBinarizer.apply(a , a )
__magic_name__ = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__magic_name__ = name[:-6]
__magic_name__ = model[F'''{prefix_}mask_scores''']
__magic_name__ = ThresholdBinarizer.apply(a , a , a )
__magic_name__ = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__magic_name__ = name[:-6]
__magic_name__ = model[F'''{prefix_}mask_scores''']
__magic_name__ , __magic_name__ = -0.1, 1.1
__magic_name__ = torch.sigmoid(a )
__magic_name__ = s * (r - l) + l
__magic_name__ = s_bar.clamp(min=0.0 , max=1.0 )
__magic_name__ = tensor * mask
print(F'''Pruned layer {name}''' )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
__magic_name__ = os.path.join(
os.path.dirname(a ) , F'''bertarized_{os.path.basename(a )}''' )
if not os.path.isdir(a ):
shutil.copytree(a , a )
print(F'''\nCreated folder {target_model_path}''' )
torch.save(a , os.path.join(a , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
_lowerCAmelCase = parser.parse_args()
main(args)
| 245 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def UpperCamelCase ( a=None , a=None ) -> Union[str, Any]:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=a )
@dataclass
class _SCREAMING_SNAKE_CASE :
__SCREAMING_SNAKE_CASE :List[str] = list_field(
default=[] ,metadata={
"""help""": (
"""Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
""" of all available models"""
)
} ,)
__SCREAMING_SNAKE_CASE :List[int] = list_field(
default=[8] ,metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""} )
__SCREAMING_SNAKE_CASE :List[int] = list_field(
default=[8, 32, 128, 512] ,metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""} ,)
__SCREAMING_SNAKE_CASE :bool = field(
default=__a ,metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""} ,)
__SCREAMING_SNAKE_CASE :bool = field(
default=__a ,metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""} ,)
__SCREAMING_SNAKE_CASE :bool = field(
default=__a ,metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""} )
__SCREAMING_SNAKE_CASE :bool = field(default=__a ,metadata={"""help""": """Use FP16 to accelerate inference."""} )
__SCREAMING_SNAKE_CASE :bool = field(default=__a ,metadata={"""help""": """Benchmark training of model"""} )
__SCREAMING_SNAKE_CASE :bool = field(default=__a ,metadata={"""help""": """Verbose memory tracing"""} )
__SCREAMING_SNAKE_CASE :bool = field(
default=__a ,metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""} ,)
__SCREAMING_SNAKE_CASE :bool = field(
default=__a ,metadata={
"""help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
} ,)
__SCREAMING_SNAKE_CASE :bool = field(default=__a ,metadata={"""help""": """Trace memory line by line"""} )
__SCREAMING_SNAKE_CASE :bool = field(default=__a ,metadata={"""help""": """Save result to a CSV file"""} )
__SCREAMING_SNAKE_CASE :bool = field(default=__a ,metadata={"""help""": """Save all print statements in a log file"""} )
__SCREAMING_SNAKE_CASE :bool = field(default=__a ,metadata={"""help""": """Whether to print environment information"""} )
__SCREAMING_SNAKE_CASE :bool = field(
default=__a ,metadata={
"""help""": (
"""Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
""" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
""" for debugging / testing and on TPU."""
)
} ,)
__SCREAMING_SNAKE_CASE :str = field(
default=f'''inference_time_{round(time() )}.csv''' ,metadata={"""help""": """CSV filename used if saving time results to csv."""} ,)
__SCREAMING_SNAKE_CASE :str = field(
default=f'''inference_memory_{round(time() )}.csv''' ,metadata={"""help""": """CSV filename used if saving memory results to csv."""} ,)
__SCREAMING_SNAKE_CASE :str = field(
default=f'''train_time_{round(time() )}.csv''' ,metadata={"""help""": """CSV filename used if saving time results to csv for training."""} ,)
__SCREAMING_SNAKE_CASE :str = field(
default=f'''train_memory_{round(time() )}.csv''' ,metadata={"""help""": """CSV filename used if saving memory results to csv for training."""} ,)
__SCREAMING_SNAKE_CASE :str = field(
default=f'''env_info_{round(time() )}.csv''' ,metadata={"""help""": """CSV filename used if saving environment information."""} ,)
__SCREAMING_SNAKE_CASE :str = field(
default=f'''log_{round(time() )}.csv''' ,metadata={"""help""": """Log filename used if print statements are saved in log."""} ,)
__SCREAMING_SNAKE_CASE :int = field(default=3 ,metadata={"""help""": """Times an experiment will be run."""} )
__SCREAMING_SNAKE_CASE :bool = field(
default=__a ,metadata={
"""help""": (
"""Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
""" model weights."""
)
} ,)
def snake_case__ ( self : Union[str, Any] ):
warnings.warn(
F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , a__ , )
def snake_case__ ( self : Dict ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def snake_case__ ( self : Dict ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def snake_case__ ( self : Dict ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 245 | 1 |
from math import factorial
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers" )
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("the function is defined for non-negative integers" )
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0" )
snake_case__ = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
snake_case__ = float(factorial(__lowerCAmelCase ) )
coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('''Probability of 2 successes out of 4 trails''')
print('''with probability of 0.75 is:''', end=''' ''')
print(binomial_distribution(2, 4, 0.7_5))
| 276 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
}
__magic_name__ = {
'''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
'''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}
__magic_name__ = {
'''ctrl''': 256,
}
__magic_name__ = {
'''Pregnancy''': 168_629,
'''Christianity''': 7_675,
'''Explain''': 106_423,
'''Fitness''': 63_440,
'''Saving''': 63_163,
'''Ask''': 27_171,
'''Ass''': 95_985,
'''Joke''': 163_509,
'''Questions''': 45_622,
'''Thoughts''': 49_605,
'''Retail''': 52_342,
'''Feminism''': 164_338,
'''Writing''': 11_992,
'''Atheism''': 192_263,
'''Netflix''': 48_616,
'''Computing''': 39_639,
'''Opinion''': 43_213,
'''Alone''': 44_967,
'''Funny''': 58_917,
'''Gaming''': 40_358,
'''Human''': 4_088,
'''India''': 1_331,
'''Joker''': 77_138,
'''Diet''': 36_206,
'''Legal''': 11_859,
'''Norman''': 4_939,
'''Tip''': 72_689,
'''Weight''': 52_343,
'''Movies''': 46_273,
'''Running''': 23_425,
'''Science''': 2_090,
'''Horror''': 37_793,
'''Confession''': 60_572,
'''Finance''': 12_250,
'''Politics''': 16_360,
'''Scary''': 191_985,
'''Support''': 12_654,
'''Technologies''': 32_516,
'''Teenage''': 66_160,
'''Event''': 32_769,
'''Learned''': 67_460,
'''Notion''': 182_770,
'''Wikipedia''': 37_583,
'''Books''': 6_665,
'''Extract''': 76_050,
'''Confessions''': 102_701,
'''Conspiracy''': 75_932,
'''Links''': 63_674,
'''Narcissus''': 150_425,
'''Relationship''': 54_766,
'''Relationships''': 134_796,
'''Reviews''': 41_671,
'''News''': 4_256,
'''Translation''': 26_820,
'''multilingual''': 128_406,
}
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
snake_case__ = set()
snake_case__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case__ = char
snake_case__ = set(__lowerCAmelCase )
return pairs
class _SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
_A : Tuple = VOCAB_FILES_NAMES
_A : str = PRETRAINED_VOCAB_FILES_MAP
_A : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : List[Any] = CONTROL_CODES
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase="<unk>" , **lowerCamelCase ):
super().__init__(unk_token=lowerCamelCase , **lowerCamelCase )
with open(lowerCamelCase , encoding="utf-8" ) as vocab_handle:
snake_case__ = json.load(lowerCamelCase )
snake_case__ = {v: k for k, v in self.encoder.items()}
with open(lowerCamelCase , encoding="utf-8" ) as merges_handle:
snake_case__ = merges_handle.read().split("\n" )[1:-1]
snake_case__ = [tuple(merge.split() ) for merge in merges]
snake_case__ = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
snake_case__ = {}
@property
def A_ ( self ):
return len(self.encoder )
def A_ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def A_ ( self , lowerCamelCase ):
if token in self.cache:
return self.cache[token]
snake_case__ = tuple(lowerCamelCase )
snake_case__ = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
snake_case__ = get_pairs(lowerCamelCase )
if not pairs:
return token
while True:
snake_case__ = min(lowerCamelCase , key=lambda lowerCamelCase : self.bpe_ranks.get(lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case__ , snake_case__ = bigram
snake_case__ = []
snake_case__ = 0
while i < len(lowerCamelCase ):
try:
snake_case__ = word.index(lowerCamelCase , lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case__ = j
if word[i] == first and i < len(lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case__ = tuple(lowerCamelCase )
snake_case__ = new_word
if len(lowerCamelCase ) == 1:
break
else:
snake_case__ = get_pairs(lowerCamelCase )
snake_case__ = "@@ ".join(lowerCamelCase )
snake_case__ = word[:-4]
snake_case__ = word
return word
def A_ ( self , lowerCamelCase ):
snake_case__ = []
snake_case__ = re.findall(r"\S+\n?" , lowerCamelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCamelCase ).split(" " ) ) )
return split_tokens
def A_ ( self , lowerCamelCase ):
return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token ) )
def A_ ( self , lowerCamelCase ):
return self.decoder.get(lowerCamelCase , self.unk_token )
def A_ ( self , lowerCamelCase ):
snake_case__ = " ".join(lowerCamelCase ).replace("@@ " , "" ).strip()
return out_string
def A_ ( self , lowerCamelCase , lowerCamelCase = None ):
if not os.path.isdir(lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case__ = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
snake_case__ = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + "\n" )
snake_case__ = 0
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
snake_case__ = token_index
writer.write(" ".join(lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 276 | 1 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
a = '''examples/'''
a = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
'''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
a = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
a = '''README.md'''
def _snake_case ( _snake_case : Optional[int] , _snake_case : str , _snake_case : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
with open(_snake_case , 'r' , encoding='utf-8' , newline='\n' ) as f:
_A = f.read()
_A , _A = REPLACE_PATTERNS[pattern]
_A = replace.replace('VERSION' , _snake_case )
_A = re_pattern.sub(_snake_case , _snake_case )
with open(_snake_case , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(_snake_case )
def _snake_case ( _snake_case : Optional[int] ) -> Dict:
'''simple docstring'''
for folder, directories, fnames in os.walk(_snake_case ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(_snake_case , _snake_case ) , _snake_case , pattern='examples' )
def _snake_case ( _snake_case : Any , _snake_case : str=False ) -> List[str]:
'''simple docstring'''
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_snake_case , _snake_case , _snake_case )
if not patch:
update_version_in_examples(_snake_case )
def _snake_case ( ) -> int:
'''simple docstring'''
_A = '🤗 Transformers currently provides the following architectures'
_A = '1. Want to contribute a new model?'
with open(_snake_case , 'r' , encoding='utf-8' , newline='\n' ) as f:
_A = f.readlines()
# Find the start of the list.
_A = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
_A = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
_A = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(_snake_case , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(_snake_case )
def _snake_case ( ) -> Dict:
'''simple docstring'''
with open(REPLACE_FILES['init'] , 'r' ) as f:
_A = f.read()
_A = REPLACE_PATTERNS['init'][0].search(_snake_case ).groups()[0]
return packaging.version.parse(_snake_case )
def _snake_case ( _snake_case : Any=False ) -> Any:
'''simple docstring'''
_A = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
_A = default_version.base_version
elif patch:
_A = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
_A = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
_A = input(F'''Which version are you releasing? [{default_version}]''' )
if len(_snake_case ) == 0:
_A = default_version
print(F'''Updating version to {version}.''' )
global_version_update(_snake_case , patch=_snake_case )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def _snake_case ( ) -> Optional[int]:
'''simple docstring'''
_A = get_version()
_A = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
_A = current_version.base_version
# Check with the user we got that right.
_A = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(_snake_case ) == 0:
_A = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(_snake_case )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
a = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 505 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] ) -> list[int]:
'''simple docstring'''
if len(_snake_case ) == 0:
return array
_A , _A = min(_snake_case ), max(_snake_case )
# Compute the variables
_A = _max - _min + 1
_A , _A = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
_A = i - _min
_A = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
_A = 0
for i in range(_snake_case ):
while holes_repeat[i] > 0:
_A = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
a = input('''Enter numbers separated by comma:\n''')
a = [int(x) for x in user_input.split(''',''')]
print(pigeon_sort(unsorted))
| 505 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __A:
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = data
UpperCamelCase__ = [0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]
@staticmethod
def UpperCAmelCase_ (SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f
def UpperCAmelCase_ (self ):
UpperCamelCase__ = b"""\x80""" + b"""\x00""" * (63 - (len(self.data ) + 8) % 64)
UpperCamelCase__ = self.data + padding + struct.pack(""">Q""" , 8 * len(self.data ) )
return padded_data
def UpperCAmelCase_ (self ):
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = list(struct.unpack(""">16L""" , __a ) ) + [0] * 64
for i in range(16 , 80 ):
UpperCamelCase__ = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.padding()
UpperCamelCase__ = self.split_blocks()
for block in self.blocks:
UpperCamelCase__ = self.expand_block(__a )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
UpperCamelCase__ = (b & c) | ((~b) & d)
UpperCamelCase__ = 0X5_a_8_2_7_9_9_9
elif 20 <= i < 40:
UpperCamelCase__ = b ^ c ^ d
UpperCamelCase__ = 0X6_e_d_9_e_b_a_1
elif 40 <= i < 60:
UpperCamelCase__ = (b & c) | (b & d) | (c & d)
UpperCamelCase__ = 0X8_f_1_b_b_c_d_c
elif 60 <= i < 80:
UpperCamelCase__ = b ^ c ^ d
UpperCamelCase__ = 0Xc_a_6_2_c_1_d_6
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = (
self.rotate(__a , 5 ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
a,
self.rotate(__a , 30 ),
c,
d,
)
UpperCamelCase__ = (
self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
)
return ("{:08x}" * 5).format(*self.h )
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ = b"""Test String"""
assert SHAaHash(snake_case__ ).final_hash() == hashlib.shaa(snake_case__ ).hexdigest() # noqa: S324
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ = argparse.ArgumentParser(description="""Process some strings or files""" )
parser.add_argument(
"""--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument("""--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase__ = parser.parse_args()
UpperCamelCase__ = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase__ = f.read()
else:
UpperCamelCase__ = bytes(snake_case__ , """utf-8""" )
print(SHAaHash(snake_case__ ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 513 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( snake_case__: np.ndarray ) -> tuple[np.ndarray, np.ndarray]:
UpperCAmelCase__ , UpperCAmelCase__ = np.shape(snake_case__ )
if rows != columns:
UpperCAmelCase__ = (
'\'table\' has to be of square shaped array but got a '
f"{rows}x{columns} array:\n{table}"
)
raise ValueError(snake_case__ )
UpperCAmelCase__ = np.zeros((rows, columns) )
UpperCAmelCase__ = np.zeros((rows, columns) )
for i in range(snake_case__ ):
for j in range(snake_case__ ):
UpperCAmelCase__ = sum(lower[i][k] * upper[k][j] for k in range(snake_case__ ) )
if upper[j][j] == 0:
raise ArithmeticError('No LU decomposition exists' )
UpperCAmelCase__ = (table[i][j] - total) / upper[j][j]
UpperCAmelCase__ = 1
for j in range(snake_case__ , snake_case__ ):
UpperCAmelCase__ = sum(lower[i][k] * upper[k][j] for k in range(snake_case__ ) )
UpperCAmelCase__ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 146 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class snake_case_ ( TensorFormatter[Mapping, """torch.Tensor""", Mapping] ):
"""simple docstring"""
def __init__( self , _A=None , **_A ):
super().__init__(features=_A )
__lowerCAmelCase = torch_tensor_kwargs
import torch # noqa import torch at initialization
def A__ ( self , _A ):
import torch
if isinstance(_A , _A ) and column:
if all(
isinstance(_A , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(_A )
return column
def A__ ( self , _A ):
import torch
if isinstance(_A , (str, bytes, type(_A )) ):
return value
elif isinstance(_A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
__lowerCAmelCase = {}
if isinstance(_A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
__lowerCAmelCase = {'dtype': torch.intaa}
elif isinstance(_A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
__lowerCAmelCase = {'dtype': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_A , PIL.Image.Image ):
__lowerCAmelCase = np.asarray(_A )
return torch.tensor(_A , **{**default_dtype, **self.torch_tensor_kwargs} )
def A__ ( self , _A ):
import torch
# support for torch, tf, jax etc.
if hasattr(_A , '__array__' ) and not isinstance(_A , torch.Tensor ):
__lowerCAmelCase = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_A , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_A ) for substruct in data_struct] )
elif isinstance(_A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_A ) for substruct in data_struct] )
return self._tensorize(_A )
def A__ ( self , _A ):
return map_nested(self._recursive_tensorize , _A , map_list=_A )
def A__ ( self , _A ):
__lowerCAmelCase = self.numpy_arrow_extractor().extract_row(_A )
__lowerCAmelCase = self.python_features_decoder.decode_row(_A )
return self.recursive_tensorize(_A )
def A__ ( self , _A ):
__lowerCAmelCase = self.numpy_arrow_extractor().extract_column(_A )
__lowerCAmelCase = self.python_features_decoder.decode_column(_A , pa_table.column_names[0] )
__lowerCAmelCase = self.recursive_tensorize(_A )
__lowerCAmelCase = self._consolidate(_A )
return column
def A__ ( self , _A ):
__lowerCAmelCase = self.numpy_arrow_extractor().extract_batch(_A )
__lowerCAmelCase = self.python_features_decoder.decode_batch(_A )
__lowerCAmelCase = self.recursive_tensorize(_A )
for column_name in batch:
__lowerCAmelCase = self._consolidate(batch[column_name] )
return batch
| 712 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
__lowerCAmelCase = args.log_outputs
__lowerCAmelCase = '_'.join(args.dataset.split('/' ) + [args.config, args.split] )
# load metric
__lowerCAmelCase = load_metric('wer' )
__lowerCAmelCase = load_metric('cer' )
# compute metrics
__lowerCAmelCase = wer.compute(references=result['target'] , predictions=result['prediction'] )
__lowerCAmelCase = cer.compute(references=result['target'] , predictions=result['prediction'] )
# print & log results
__lowerCAmelCase = F"""WER: {wer_result}\nCER: {cer_result}"""
print(UpperCAmelCase__ )
with open(F"""{dataset_id}_eval_results.txt""" , 'w' ) as f:
f.write(UpperCAmelCase__ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
__lowerCAmelCase = F"""log_{dataset_id}_predictions.txt"""
__lowerCAmelCase = F"""log_{dataset_id}_targets.txt"""
with open(UpperCAmelCase__ , 'w' ) as p, open(UpperCAmelCase__ , 'w' ) as t:
# mapping function to write output
def write_to_file(UpperCAmelCase__ , UpperCAmelCase__ ):
p.write(F"""{i}""" + '\n' )
p.write(batch['prediction'] + '\n' )
t.write(F"""{i}""" + '\n' )
t.write(batch['target'] + '\n' )
result.map(UpperCAmelCase__ , with_indices=UpperCAmelCase__ )
def __lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
__lowerCAmelCase = '[,?.!\-\;\:"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
__lowerCAmelCase = re.sub(UpperCAmelCase__ , '' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
__lowerCAmelCase = ['\n\n', '\n', ' ', ' ']
for t in token_sequences_to_ignore:
__lowerCAmelCase = ' '.join(text.split(UpperCAmelCase__ ) )
return text
def __lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
__lowerCAmelCase = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=UpperCAmelCase__ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
__lowerCAmelCase = AutoFeatureExtractor.from_pretrained(args.model_id )
__lowerCAmelCase = feature_extractor.sampling_rate
# resample audio
__lowerCAmelCase = dataset.cast_column('audio' , Audio(sampling_rate=UpperCAmelCase__ ) )
# load eval pipeline
if args.device is None:
__lowerCAmelCase = 0 if torch.cuda.is_available() else -1
__lowerCAmelCase = pipeline('automatic-speech-recognition' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(UpperCAmelCase__ ):
__lowerCAmelCase = asr(
batch['audio']['array'] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
__lowerCAmelCase = prediction['text']
__lowerCAmelCase = normalize_text(batch['sentence'] )
return batch
# run inference on all examples
__lowerCAmelCase = dataset.map(UpperCAmelCase__ , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
lowerCamelCase = parser.parse_args()
main(args)
| 102 | 0 |
from maths.prime_factors import prime_factors
def UpperCAmelCase__( __UpperCAmelCase : int ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__snake_case : Tuple = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__UpperCAmelCase )
if number < 1:
raise ValueError('Input must be a positive integer' )
return -1 if len(prime_factors(__UpperCAmelCase ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 576 | import numpy as np
def UpperCAmelCase__( __UpperCAmelCase : np.ndarray , __UpperCAmelCase : float ):
return np.where(vector > 0 , __UpperCAmelCase , (alpha * (np.exp(__UpperCAmelCase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 576 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase :Optional[int] = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileNetV2Config',
'MobileNetV2OnnxConfig',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase :str = ['MobileNetV2FeatureExtractor']
__lowerCAmelCase :Optional[int] = ['MobileNetV2ImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase :Tuple = [
'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileNetV2ForImageClassification',
'MobileNetV2ForSemanticSegmentation',
'MobileNetV2Model',
'MobileNetV2PreTrainedModel',
'load_tf_weights_in_mobilenet_v2',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
__lowerCAmelCase :int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 278 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file # noqa: F401
from .default import default_command_parser
from .update import update_command_parser
def A ( UpperCAmelCase=None ):
_snake_case : Union[str, Any] = argparse.ArgumentParser(add_help=UpperCAmelCase , allow_abbrev=UpperCAmelCase )
# The main config parser
_snake_case : Tuple = config_command_parser(UpperCAmelCase )
# The subparser to add commands to
_snake_case : Any = config_parser.add_subparsers(title="subcommands" , dest="subcommand" )
# Then add other parsers with the parent parser
default_command_parser(UpperCAmelCase , parents=[parent_parser] )
update_command_parser(UpperCAmelCase , parents=[parent_parser] )
return config_parser
def A ( ):
_snake_case : str = get_config_parser()
_snake_case : Union[str, Any] = config_parser.parse_args()
if not hasattr(UpperCAmelCase , "func" ):
config_parser.print_help()
exit(1 )
# Run
args.func(UpperCAmelCase )
if __name__ == "__main__":
main() | 278 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowerCAmelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase =RoCBertTokenizer
_lowerCamelCase =None
_lowerCamelCase =False
_lowerCamelCase =True
_lowerCamelCase =filter_non_english
def __snake_case ( self : Any ):
super().setUp()
UpperCAmelCase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''你''', '''好''', '''是''', '''谁''', '''a''', '''b''', '''c''', '''d''']
UpperCAmelCase = {}
UpperCAmelCase = {}
for i, value in enumerate(a__ ):
UpperCAmelCase = i
UpperCAmelCase = i
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''word_shape_file'''] )
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''word_pronunciation_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
with open(self.word_shape_file , '''w''' , encoding='''utf-8''' ) as word_shape_writer:
json.dump(a__ , a__ , ensure_ascii=a__ )
with open(self.word_pronunciation_file , '''w''' , encoding='''utf-8''' ) as word_pronunciation_writer:
json.dump(a__ , a__ , ensure_ascii=a__ )
def __snake_case ( self : List[Any] ):
UpperCAmelCase = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
UpperCAmelCase = tokenizer.tokenize('''你好[SEP]你是谁''' )
self.assertListEqual(a__ , ['''你''', '''好''', '''[SEP]''', '''你''', '''是''', '''谁'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(a__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(a__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(a__ ) , [5, 6, 2, 5, 7, 8] )
def __snake_case ( self : Optional[int] ):
UpperCAmelCase = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ) , ['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )
def __snake_case ( self : Optional[int] ):
UpperCAmelCase = RoCBertBasicTokenizer(do_lower_case=a__ )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __snake_case ( self : Tuple ):
UpperCAmelCase = RoCBertBasicTokenizer(do_lower_case=a__ , strip_accents=a__ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''h\u00E9llo'''] )
def __snake_case ( self : Optional[int] ):
UpperCAmelCase = RoCBertBasicTokenizer(do_lower_case=a__ , strip_accents=a__ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __snake_case ( self : Tuple ):
UpperCAmelCase = RoCBertBasicTokenizer(do_lower_case=a__ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __snake_case ( self : int ):
UpperCAmelCase = RoCBertBasicTokenizer(do_lower_case=a__ )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __snake_case ( self : Optional[Any] ):
UpperCAmelCase = RoCBertBasicTokenizer(do_lower_case=a__ , strip_accents=a__ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __snake_case ( self : Optional[Any] ):
UpperCAmelCase = RoCBertBasicTokenizer(do_lower_case=a__ , strip_accents=a__ )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __snake_case ( self : Optional[int] ):
UpperCAmelCase = RoCBertBasicTokenizer(do_lower_case=a__ , never_split=['''[UNK]'''] )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? [UNK]''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )
def __snake_case ( self : Optional[int] ):
UpperCAmelCase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']
UpperCAmelCase = {}
for i, token in enumerate(a__ ):
UpperCAmelCase = i
UpperCAmelCase = RoCBertWordpieceTokenizer(vocab=a__ , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''unwanted running''' ) , ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ) , ['''[UNK]''', '''runn''', '''##ing'''] )
def __snake_case ( self : int ):
self.assertTrue(_is_whitespace(''' ''' ) )
self.assertTrue(_is_whitespace('''\t''' ) )
self.assertTrue(_is_whitespace('''\r''' ) )
self.assertTrue(_is_whitespace('''\n''' ) )
self.assertTrue(_is_whitespace('''\u00A0''' ) )
self.assertFalse(_is_whitespace('''A''' ) )
self.assertFalse(_is_whitespace('''-''' ) )
def __snake_case ( self : Union[str, Any] ):
self.assertTrue(_is_control('''\u0005''' ) )
self.assertFalse(_is_control('''A''' ) )
self.assertFalse(_is_control(''' ''' ) )
self.assertFalse(_is_control('''\t''' ) )
self.assertFalse(_is_control('''\r''' ) )
def __snake_case ( self : Any ):
self.assertTrue(_is_punctuation('''-''' ) )
self.assertTrue(_is_punctuation('''$''' ) )
self.assertTrue(_is_punctuation('''`''' ) )
self.assertTrue(_is_punctuation('''.''' ) )
self.assertFalse(_is_punctuation('''A''' ) )
self.assertFalse(_is_punctuation(''' ''' ) )
def __snake_case ( self : Optional[int] ):
UpperCAmelCase = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(a__ ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] )
if self.test_rust_tokenizer:
UpperCAmelCase = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(a__ ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] )
def __snake_case ( self : int ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(a__ , **a__ )
UpperCAmelCase = f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
UpperCAmelCase = tokenizer_r.encode_plus(
a__ , return_attention_mask=a__ , return_token_type_ids=a__ , return_offsets_mapping=a__ , add_special_tokens=a__ , )
UpperCAmelCase = tokenizer_r.do_lower_case if hasattr(a__ , '''do_lower_case''' ) else False
UpperCAmelCase = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), '''A'''),
((1, 2), ''','''),
((3, 5), '''na'''),
((5, 6), '''##ï'''),
((6, 8), '''##ve'''),
((9, 15), tokenizer_r.mask_token),
((16, 21), '''Allen'''),
((21, 23), '''##NL'''),
((23, 24), '''##P'''),
((25, 33), '''sentence'''),
((33, 34), '''.'''),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), '''a'''),
((1, 2), ''','''),
((3, 8), '''naive'''),
((9, 15), tokenizer_r.mask_token),
((16, 21), '''allen'''),
((21, 23), '''##nl'''),
((23, 24), '''##p'''),
((25, 33), '''sentence'''),
((33, 34), '''.'''),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['''offset_mapping'''] )
def __snake_case ( self : List[str] ):
UpperCAmelCase = ['''的''', '''人''', '''有''']
UpperCAmelCase = ''''''.join(a__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCAmelCase = True
UpperCAmelCase = self.tokenizer_class.from_pretrained(a__ , **a__ )
UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(a__ , **a__ )
UpperCAmelCase = tokenizer_p.encode(a__ , add_special_tokens=a__ )
UpperCAmelCase = tokenizer_r.encode(a__ , add_special_tokens=a__ )
UpperCAmelCase = tokenizer_r.convert_ids_to_tokens(a__ )
UpperCAmelCase = tokenizer_p.convert_ids_to_tokens(a__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(a__ , a__ )
self.assertListEqual(a__ , a__ )
UpperCAmelCase = False
UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(a__ , **a__ )
UpperCAmelCase = self.tokenizer_class.from_pretrained(a__ , **a__ )
UpperCAmelCase = tokenizer_r.encode(a__ , add_special_tokens=a__ )
UpperCAmelCase = tokenizer_p.encode(a__ , add_special_tokens=a__ )
UpperCAmelCase = tokenizer_r.convert_ids_to_tokens(a__ )
UpperCAmelCase = tokenizer_p.convert_ids_to_tokens(a__ )
# it is expected that only the first Chinese character is not preceded by "##".
UpperCAmelCase = [
f"##{token}" if idx != 0 else token for idx, token in enumerate(a__ )
]
self.assertListEqual(a__ , a__ )
self.assertListEqual(a__ , a__ )
@slow
def __snake_case ( self : str ):
UpperCAmelCase = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
UpperCAmelCase = tokenizer.encode('''你好''' , add_special_tokens=a__ )
UpperCAmelCase = tokenizer.encode('''你是谁''' , add_special_tokens=a__ )
UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(a__ )
UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(a__ , a__ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def __snake_case ( self : str ):
UpperCAmelCase = self.get_tokenizers(do_lower_case=a__ )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
UpperCAmelCase = '''你好，你是谁'''
UpperCAmelCase = tokenizer.tokenize(a__ )
UpperCAmelCase = tokenizer.convert_tokens_to_ids(a__ )
UpperCAmelCase = tokenizer.convert_tokens_to_shape_ids(a__ )
UpperCAmelCase = tokenizer.convert_tokens_to_pronunciation_ids(a__ )
UpperCAmelCase = tokenizer.prepare_for_model(
a__ , a__ , a__ , add_special_tokens=a__ )
UpperCAmelCase = tokenizer.encode_plus(a__ , add_special_tokens=a__ )
self.assertEqual(a__ , a__ )
| 51 |
def SCREAMING_SNAKE_CASE__ ( _lowercase : str ) -> 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' )
lowercase__ : Tuple = ''
while len(_lowercase ) % 3 != 0:
lowercase__ : Optional[int] = '0' + bin_string
lowercase__ : List[Any] = [
bin_string[index : index + 3]
for index in range(len(_lowercase ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
lowercase__ : int = 0
for index, val in enumerate(_lowercase ):
oct_val += int(2 ** (2 - index) * int(_lowercase ) )
oct_string += str(_lowercase )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 266 | 0 |
from datetime import datetime
import requests
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> bytes:
"""simple docstring"""
A__ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
A__ = requests.get(base_url + url ).json()[0]['''urls'''][0]['''src''']
return requests.get(lowercase_ ).content
if __name__ == "__main__":
_lowerCamelCase : Optional[int] = input("""Enter Video/IGTV url: """).strip()
_lowerCamelCase : Tuple = F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
with open(file_name, """wb""") as fp:
fp.write(download_video(url))
print(F'''Done. Video saved to disk as {file_name}.''')
| 706 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCamelCase : Optional[int] = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE ( lowercase_ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
"""simple docstring"""
A__ = '''//div[@class = "maincounter-number"]/span/text()'''
return covid_data(*html.fromstring(requests.get(lowercase_ ).content ).xpath(lowercase_ ) )
_lowerCamelCase : Optional[int] = """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))
| 177 | 0 |
class __magic_name__ :
'''simple docstring'''
def __init__( self: Optional[int] ):
SCREAMING_SNAKE_CASE_ = {}
def _A ( self: Optional[Any] ):
print(self.vertex )
for i in self.vertex:
print(_lowerCamelCase , ''' -> ''' , ''' -> '''.join([str(_lowerCamelCase ) for j in self.vertex[i]] ) )
def _A ( self: Optional[int] , _lowerCamelCase: int , _lowerCamelCase: int ):
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(_lowerCamelCase )
else:
# else make a new vertex
SCREAMING_SNAKE_CASE_ = [to_vertex]
def _A ( self: int ):
# visited array for storing already visited nodes
SCREAMING_SNAKE_CASE_ = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(_lowerCamelCase , _lowerCamelCase )
def _A ( self: Dict , _lowerCamelCase: int , _lowerCamelCase: list ):
# mark start vertex as visited
SCREAMING_SNAKE_CASE_ = True
print(_lowerCamelCase , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(_lowerCamelCase , _lowerCamelCase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE =Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("""DFS:""")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 234 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
def a (_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = DPTConfig()
if "large" in checkpoint_url:
SCREAMING_SNAKE_CASE_ = 1_0_2_4
SCREAMING_SNAKE_CASE_ = 4_0_9_6
SCREAMING_SNAKE_CASE_ = 2_4
SCREAMING_SNAKE_CASE_ = 1_6
SCREAMING_SNAKE_CASE_ = [5, 1_1, 1_7, 2_3]
SCREAMING_SNAKE_CASE_ = [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
SCREAMING_SNAKE_CASE_ = (1, 3_8_4, 3_8_4)
if "ade" in checkpoint_url:
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = 1_5_0
SCREAMING_SNAKE_CASE_ = '''huggingface/label-files'''
SCREAMING_SNAKE_CASE_ = '''ade20k-id2label.json'''
SCREAMING_SNAKE_CASE_ = json.load(open(cached_download(hf_hub_url(_lowerCAmelCase , _lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) )
SCREAMING_SNAKE_CASE_ = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = idalabel
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = [1, 1_5_0, 4_8_0, 4_8_0]
return config, expected_shape
def a (_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def a (_lowerCAmelCase ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.model''' , '''dpt.encoder''' )
if "pretrained.model" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.model''' , '''dpt.embeddings''' )
if "patch_embed" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''patch_embed''' , '''patch_embeddings''' )
if "pos_embed" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pos_embed''' , '''position_embeddings''' )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "proj" in name and "project" not in name:
SCREAMING_SNAKE_CASE_ = name.replace('''proj''' , '''projection''' )
if "blocks" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''blocks''' , '''layer''' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''mlp.fc2''' , '''output.dense''' )
if "norm1" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''norm2''' , '''layernorm_after''' )
if "scratch.output_conv" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''scratch.output_conv''' , '''head''' )
if "scratch" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''scratch''' , '''neck''' )
if "layer1_rn" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''layer1_rn''' , '''convs.0''' )
if "layer2_rn" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''layer2_rn''' , '''convs.1''' )
if "layer3_rn" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''layer3_rn''' , '''convs.2''' )
if "layer4_rn" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''layer4_rn''' , '''convs.3''' )
if "refinenet" in name:
SCREAMING_SNAKE_CASE_ = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
SCREAMING_SNAKE_CASE_ = name.replace(F"refinenet{layer_idx}" , F"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''out_conv''' , '''projection''' )
if "resConfUnit1" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''resConfUnit1''' , '''residual_layer1''' )
if "resConfUnit2" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''resConfUnit2''' , '''residual_layer2''' )
if "conv1" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''conv1''' , '''convolution1''' )
if "conv2" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''conv2''' , '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''pretrained''' , '''dpt''' )
if "bn" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''bn''' , '''batch_norm''' )
if "head" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''head''' , '''head.head''' )
if "encoder.norm" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''encoder.norm''' , '''layernorm''' )
if "auxlayer" in name:
SCREAMING_SNAKE_CASE_ = name.replace('''auxlayer''' , '''auxiliary_head.head''' )
return name
def a (_lowerCAmelCase , _lowerCAmelCase ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE_ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
SCREAMING_SNAKE_CASE_ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE_ = in_proj_weight[: config.hidden_size, :]
SCREAMING_SNAKE_CASE_ = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE_ = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE_ = in_proj_bias[-config.hidden_size :]
def a ():
SCREAMING_SNAKE_CASE_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE_ = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def a (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = get_dpt_config(_lowerCAmelCase )
# load original state_dict from URL
SCREAMING_SNAKE_CASE_ = torch.hub.load_state_dict_from_url(_lowerCAmelCase , map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(_lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
SCREAMING_SNAKE_CASE_ = state_dict.pop(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = val
# read in qkv matrices
read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase )
# load HuggingFace model
SCREAMING_SNAKE_CASE_ = DPTForSemanticSegmentation(_lowerCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
model.eval()
# Check outputs on an image
SCREAMING_SNAKE_CASE_ = 4_8_0 if '''ade''' in checkpoint_url else 3_8_4
SCREAMING_SNAKE_CASE_ = DPTImageProcessor(size=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(_lowerCAmelCase , return_tensors='''pt''' )
# forward pass
SCREAMING_SNAKE_CASE_ = model(**_lowerCAmelCase ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase ).predicted_depth
# Assert logits
SCREAMING_SNAKE_CASE_ = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
if "ade" in checkpoint_url:
SCREAMING_SNAKE_CASE_ = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
assert outputs.shape == torch.Size(_lowerCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3] , _lowerCAmelCase , atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , _lowerCAmelCase )
)
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCAmelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
print('''Pushing model to hub...''' )
model.push_to_hub(
repo_path_or_name=Path(_lowerCAmelCase , _lowerCAmelCase ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=_lowerCAmelCase , )
image_processor.push_to_hub(
repo_path_or_name=Path(_lowerCAmelCase , _lowerCAmelCase ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=_lowerCAmelCase , )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
__SCREAMING_SNAKE_CASE =parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 234 | 1 |
"""simple docstring"""
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:
lowercase__ :Optional[Any] = None
lowercase__ :List[str] = logging.get_logger(__name__)
lowercase__ :Any = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
lowercase__ :List[str] = {
'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'
),
},
}
lowercase__ :Dict = {
'facebook/nllb-large-en-ro': 1_0_2_4,
'facebook/nllb-200-distilled-600M': 1_0_2_4,
}
# fmt: off
lowercase__ :Dict = ['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'''
_A : Tuple = VOCAB_FILES_NAMES
_A : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : List[str] = PRETRAINED_VOCAB_FILES_MAP
_A : Optional[int] = ['input_ids', 'attention_mask']
_A : int = NllbTokenizer
_A : List[int] = []
_A : List[int] = []
def __init__( self : Union[str, Any] , __lowercase : Tuple=None , __lowercase : List[str]=None , __lowercase : Union[str, Any]="<s>" , __lowercase : Union[str, Any]="</s>" , __lowercase : Dict="</s>" , __lowercase : Optional[Any]="<s>" , __lowercase : Union[str, Any]="<unk>" , __lowercase : Optional[Any]="<pad>" , __lowercase : int="<mask>" , __lowercase : Any=None , __lowercase : int=None , __lowercase : Dict=None , __lowercase : Tuple=False , **__lowercase : int , ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
__UpperCAmelCase : 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 , )
__UpperCAmelCase : Union[str, Any] = vocab_file
__UpperCAmelCase : List[str] = False if not self.vocab_file else True
__UpperCAmelCase : List[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} )
__UpperCAmelCase : Tuple = {
lang_code: self.convert_tokens_to_ids(__lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
__UpperCAmelCase : str = src_lang if src_lang is not None else '''eng_Latn'''
__UpperCAmelCase : Union[str, Any] = self.convert_tokens_to_ids(self._src_lang )
__UpperCAmelCase : Optional[Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def A_ ( self : Optional[Any] ):
'''simple docstring'''
return self._src_lang
@src_lang.setter
def A_ ( self : str , __lowercase : str ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def A_ ( self : Any , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def A_ ( self : str , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ):
'''simple docstring'''
__UpperCAmelCase : Dict = [self.sep_token_id]
__UpperCAmelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def A_ ( self : Optional[Any] , __lowercase : Union[str, Any] , __lowercase : str , __lowercase : Optional[str] , __lowercase : Optional[str] , **__lowercase : int ):
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
__UpperCAmelCase : str = src_lang
__UpperCAmelCase : Tuple = self(__lowercase , add_special_tokens=__lowercase , return_tensors=__lowercase , **__lowercase )
__UpperCAmelCase : Optional[int] = self.convert_tokens_to_ids(__lowercase )
__UpperCAmelCase : Any = tgt_lang_id
return inputs
def A_ ( self : int , __lowercase : List[str] , __lowercase : str = "eng_Latn" , __lowercase : Optional[List[str]] = None , __lowercase : str = "fra_Latn" , **__lowercase : Dict , ):
'''simple docstring'''
__UpperCAmelCase : str = src_lang
__UpperCAmelCase : str = tgt_lang
return super().prepare_seqaseq_batch(__lowercase , __lowercase , **__lowercase )
def A_ ( self : int ):
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def A_ ( self : List[Any] ):
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def A_ ( self : List[Any] , __lowercase : Dict ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
__UpperCAmelCase : int = []
__UpperCAmelCase : Dict = [self.eos_token_id, self.cur_lang_code]
else:
__UpperCAmelCase : Any = [self.cur_lang_code]
__UpperCAmelCase : Union[str, Any] = [self.eos_token_id]
__UpperCAmelCase : List[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
__UpperCAmelCase : List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
__UpperCAmelCase : Union[str, Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def A_ ( self : List[str] , __lowercase : str ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
__UpperCAmelCase : int = []
__UpperCAmelCase : Dict = [self.eos_token_id, self.cur_lang_code]
else:
__UpperCAmelCase : str = [self.cur_lang_code]
__UpperCAmelCase : int = [self.eos_token_id]
__UpperCAmelCase : Any = self.convert_ids_to_tokens(self.prefix_tokens )
__UpperCAmelCase : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
__UpperCAmelCase : Optional[int] = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$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 A_ ( self : Union[str, Any] , __lowercase : str , __lowercase : Optional[str] = None ):
'''simple docstring'''
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
__UpperCAmelCase : Tuple = 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,) | 716 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCamelCase_ ( UpperCAmelCase_ ) ->Optional[Any]:
"""simple docstring"""
__UpperCAmelCase : Tuple = SwinvaConfig()
__UpperCAmelCase : Any = swinva_name.split('''_''' )
__UpperCAmelCase : Optional[Any] = name_split[1]
if "to" in name_split[3]:
__UpperCAmelCase : List[str] = int(name_split[3][-3:] )
else:
__UpperCAmelCase : Union[str, Any] = int(name_split[3] )
if "to" in name_split[2]:
__UpperCAmelCase : List[str] = int(name_split[2][-2:] )
else:
__UpperCAmelCase : str = int(name_split[2][6:] )
if model_size == "tiny":
__UpperCAmelCase : Union[str, Any] = 96
__UpperCAmelCase : str = (2, 2, 6, 2)
__UpperCAmelCase : Union[str, Any] = (3, 6, 12, 24)
elif model_size == "small":
__UpperCAmelCase : List[Any] = 96
__UpperCAmelCase : Union[str, Any] = (2, 2, 18, 2)
__UpperCAmelCase : str = (3, 6, 12, 24)
elif model_size == "base":
__UpperCAmelCase : Any = 1_28
__UpperCAmelCase : int = (2, 2, 18, 2)
__UpperCAmelCase : Dict = (4, 8, 16, 32)
else:
__UpperCAmelCase : Union[str, Any] = 1_92
__UpperCAmelCase : Union[str, Any] = (2, 2, 18, 2)
__UpperCAmelCase : str = (6, 12, 24, 48)
if "to" in swinva_name:
__UpperCAmelCase : Optional[int] = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__UpperCAmelCase : int = 2_18_41
__UpperCAmelCase : Optional[Any] = '''huggingface/label-files'''
__UpperCAmelCase : Optional[Any] = '''imagenet-22k-id2label.json'''
__UpperCAmelCase : Any = json.load(open(hf_hub_download(UpperCAmelCase_ , UpperCAmelCase_ , repo_type='''dataset''' ) , '''r''' ) )
__UpperCAmelCase : List[Any] = {int(UpperCAmelCase_ ): v for k, v in idalabel.items()}
__UpperCAmelCase : Optional[Any] = idalabel
__UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
else:
__UpperCAmelCase : List[Any] = 10_00
__UpperCAmelCase : str = '''huggingface/label-files'''
__UpperCAmelCase : Tuple = '''imagenet-1k-id2label.json'''
__UpperCAmelCase : Optional[int] = json.load(open(hf_hub_download(UpperCAmelCase_ , UpperCAmelCase_ , repo_type='''dataset''' ) , '''r''' ) )
__UpperCAmelCase : str = {int(UpperCAmelCase_ ): v for k, v in idalabel.items()}
__UpperCAmelCase : Any = idalabel
__UpperCAmelCase : List[Any] = {v: k for k, v in idalabel.items()}
__UpperCAmelCase : Optional[int] = img_size
__UpperCAmelCase : int = num_classes
__UpperCAmelCase : Tuple = embed_dim
__UpperCAmelCase : str = depths
__UpperCAmelCase : Optional[Any] = num_heads
__UpperCAmelCase : Optional[int] = window_size
return config
def lowerCamelCase_ ( UpperCAmelCase_ ) ->str:
"""simple docstring"""
if "patch_embed.proj" in name:
__UpperCAmelCase : Tuple = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
__UpperCAmelCase : List[str] = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
__UpperCAmelCase : Optional[int] = '''encoder.''' + name
if "attn.proj" in name:
__UpperCAmelCase : Optional[Any] = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
__UpperCAmelCase : List[Any] = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
__UpperCAmelCase : List[str] = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
__UpperCAmelCase : Dict = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
__UpperCAmelCase : str = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
__UpperCAmelCase : int = name.replace('''mlp.fc2''' , '''output.dense''' )
if "q_bias" in name:
__UpperCAmelCase : Tuple = name.replace('''q_bias''' , '''query.bias''' )
if "k_bias" in name:
__UpperCAmelCase : List[str] = name.replace('''k_bias''' , '''key.bias''' )
if "v_bias" in name:
__UpperCAmelCase : str = name.replace('''v_bias''' , '''value.bias''' )
if "cpb_mlp" in name:
__UpperCAmelCase : Tuple = name.replace('''cpb_mlp''' , '''continuous_position_bias_mlp''' )
if name == "norm.weight":
__UpperCAmelCase : int = '''layernorm.weight'''
if name == "norm.bias":
__UpperCAmelCase : Optional[Any] = '''layernorm.bias'''
if "head" in name:
__UpperCAmelCase : List[Any] = name.replace('''head''' , '''classifier''' )
else:
__UpperCAmelCase : List[Any] = '''swinv2.''' + name
return name
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ) ->int:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__UpperCAmelCase : Any = orig_state_dict.pop(UpperCAmelCase_ )
if "mask" in key:
continue
elif "qkv" in key:
__UpperCAmelCase : int = key.split('''.''' )
__UpperCAmelCase : int = int(key_split[1] )
__UpperCAmelCase : str = int(key_split[3] )
__UpperCAmelCase : List[str] = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__UpperCAmelCase : Optional[Any] = val[:dim, :]
__UpperCAmelCase : Union[str, Any] = val[dim : dim * 2, :]
__UpperCAmelCase : List[Any] = val[-dim:, :]
else:
__UpperCAmelCase : Optional[int] = val[:dim]
__UpperCAmelCase : Any = val[
dim : dim * 2
]
__UpperCAmelCase : List[str] = val[-dim:]
else:
__UpperCAmelCase : Tuple = val
return orig_state_dict
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ) ->List[str]:
"""simple docstring"""
__UpperCAmelCase : str = timm.create_model(UpperCAmelCase_ , pretrained=UpperCAmelCase_ )
timm_model.eval()
__UpperCAmelCase : Any = get_swinva_config(UpperCAmelCase_ )
__UpperCAmelCase : List[str] = SwinvaForImageClassification(UpperCAmelCase_ )
model.eval()
__UpperCAmelCase : int = convert_state_dict(timm_model.state_dict() , UpperCAmelCase_ )
model.load_state_dict(UpperCAmelCase_ )
__UpperCAmelCase : Optional[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__UpperCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''' , '''-''' ) ) )
__UpperCAmelCase : List[Any] = Image.open(requests.get(UpperCAmelCase_ , stream=UpperCAmelCase_ ).raw )
__UpperCAmelCase : Union[str, Any] = image_processor(images=UpperCAmelCase_ , return_tensors='''pt''' )
__UpperCAmelCase : List[Any] = timm_model(inputs['''pixel_values'''] )
__UpperCAmelCase : Tuple = model(**UpperCAmelCase_ ).logits
assert torch.allclose(UpperCAmelCase_ , UpperCAmelCase_ , atol=1e-3 )
print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase_ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase_ )
model.push_to_hub(
repo_path_or_name=Path(UpperCAmelCase_ , UpperCAmelCase_ ) , organization='''nandwalritik''' , commit_message='''Add model''' , )
if __name__ == "__main__":
lowercase__ :List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swinv2_name',
default='swinv2_tiny_patch4_window8_256',
type=str,
help='Name of the Swinv2 timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
lowercase__ :List[Any] = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path) | 374 | 0 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def UpperCAmelCase_ ( A = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def UpperCAmelCase_ ( A = "" ):
'''simple docstring'''
if len(A ) == 0:
return True
_a : Any = input_str.replace(' ' , '' ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
_a : dict[str, int] = {}
for character in lower_case_input_str:
_a : Union[str, Any] = character_freq_dict.get(A , 0 ) + 1
_a : Tuple = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def UpperCAmelCase_ ( A = "" ):
'''simple docstring'''
print('\nFor string = ' , A , ':' )
print(
'> can_string_be_rearranged_as_palindrome_counter()' , '\tans =' , can_string_be_rearranged_as_palindrome_counter(A ) , '\ttime =' , timeit(
'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)' , setup='import __main__ as z' , ) , 'seconds' , )
print(
'> can_string_be_rearranged_as_palindrome()' , '\tans =' , can_string_be_rearranged_as_palindrome(A ) , '\ttime =' , timeit(
'z.can_string_be_rearranged_as_palindrome(z.check_str)' , setup='import __main__ as z' , ) , 'seconds' , )
if __name__ == "__main__":
UpperCAmelCase_ : int = input(
"Enter string to determine if it can be rearranged as a palindrome or not: "
).strip()
benchmark(check_str)
UpperCAmelCase_ : Tuple = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f'''{check_str} can {"" if status else "not "}be rearranged as a palindrome''')
| 120 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( A = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_a : Union[str, Any] = BeautifulSoup(requests.get(A ).text , 'html.parser' )
_a : int = soup.findAll('h1' )
_a : Union[str, Any] = soup.findAll('div' , {'class': 'maincounter-number'} )
keys += soup.findAll('span' , {'class': 'panel-title'} )
values += soup.findAll('div' , {'class': 'number-table-main'} )
return {key.text.strip(): value.text.strip() for key, value in zip(A , A )}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covidaa_stats().items():
print(f'''{key}\n{value}\n''')
| 120 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
UpperCamelCase = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if isinstance(SCREAMING_SNAKE_CASE , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(SCREAMING_SNAKE_CASE , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(SCREAMING_SNAKE_CASE ):
return [[videos]]
raise ValueError(f'''Could not make batched video from {videos}''' )
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = ["pixel_values"]
def __init__( self , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = 1 / 255 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , )->None:
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
A_ : List[str] = size if size is not None else {'''shortest_edge''': 256}
A_ : Any = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
A_ : Any = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
A_ : int = get_size_dict(_SCREAMING_SNAKE_CASE , param_name='''crop_size''' )
A_ : Any = do_resize
A_ : List[str] = size
A_ : int = do_center_crop
A_ : Tuple = crop_size
A_ : Any = resample
A_ : Dict = do_rescale
A_ : Optional[Any] = rescale_factor
A_ : Optional[Any] = offset
A_ : Optional[int] = do_normalize
A_ : List[str] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A_ : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , )->np.ndarray:
'''simple docstring'''
A_ : Union[str, Any] = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
if "shortest_edge" in size:
A_ : Any = get_resize_output_image_size(_SCREAMING_SNAKE_CASE , size['''shortest_edge'''] , default_to_square=_SCREAMING_SNAKE_CASE )
elif "height" in size and "width" in size:
A_ : List[str] = (size['''height'''], size['''width'''])
else:
raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , )->np.ndarray:
'''simple docstring'''
A_ : int = get_size_dict(_SCREAMING_SNAKE_CASE )
if "height" not in size or "width" not in size:
raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(_SCREAMING_SNAKE_CASE , size=(size['''height'''], size['''width''']) , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , )->int:
'''simple docstring'''
A_ : Optional[Any] = image.astype(np.floataa )
if offset:
A_ : Dict = image - (scale / 2)
return rescale(_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , )->np.ndarray:
'''simple docstring'''
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST , )->np.ndarray:
'''simple docstring'''
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
if offset and not do_rescale:
raise ValueError('''For offset, do_rescale must also be set to True.''' )
# All transformations expect numpy arrays.
A_ : Any = to_numpy_array(_SCREAMING_SNAKE_CASE )
if do_resize:
A_ : int = self.resize(image=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE )
if do_center_crop:
A_ : Union[str, Any] = self.center_crop(_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE )
if do_rescale:
A_ : str = self.rescale(image=_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE , offset=_SCREAMING_SNAKE_CASE )
if do_normalize:
A_ : Any = self.normalize(image=_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE )
A_ : List[Any] = to_channel_dimension_format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return image
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE , )->PIL.Image.Image:
'''simple docstring'''
A_ : Optional[int] = do_resize if do_resize is not None else self.do_resize
A_ : List[Any] = resample if resample is not None else self.resample
A_ : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
A_ : List[str] = do_rescale if do_rescale is not None else self.do_rescale
A_ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : List[str] = offset if offset is not None else self.offset
A_ : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
A_ : List[str] = image_mean if image_mean is not None else self.image_mean
A_ : Optional[int] = image_std if image_std is not None else self.image_std
A_ : int = size if size is not None else self.size
A_ : List[Any] = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
A_ : str = crop_size if crop_size is not None else self.crop_size
A_ : Any = get_size_dict(_SCREAMING_SNAKE_CASE , param_name='''crop_size''' )
if not valid_images(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
A_ : Dict = make_batched(_SCREAMING_SNAKE_CASE )
A_ : Tuple = [
[
self._preprocess_image(
image=_SCREAMING_SNAKE_CASE , do_resize=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE , do_center_crop=_SCREAMING_SNAKE_CASE , crop_size=_SCREAMING_SNAKE_CASE , do_rescale=_SCREAMING_SNAKE_CASE , rescale_factor=_SCREAMING_SNAKE_CASE , offset=_SCREAMING_SNAKE_CASE , do_normalize=_SCREAMING_SNAKE_CASE , image_mean=_SCREAMING_SNAKE_CASE , image_std=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , )
for img in video
]
for video in videos
]
A_ : Optional[int] = {'''pixel_values''': videos}
return BatchFeature(data=_SCREAMING_SNAKE_CASE , tensor_type=_SCREAMING_SNAKE_CASE )
| 152 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
snake_case = field(default="summarization" , metadata={"include_in_asdict_even_if_is_default": True} )
snake_case = Features({"text": Value("string" )} )
snake_case = Features({"summary": Value("string" )} )
snake_case = "text"
snake_case = "summary"
@property
def _snake_case ( self )->Dict[str, str]:
'''simple docstring'''
return {self.text_column: "text", self.summary_column: "summary"}
| 152 | 1 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a : int = logging.getLogger(__name__)
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : Tuple ) -> Dict:
__snake_case = np.argmax(_UpperCAmelCase , axis=1 )
return np.sum(outputs == labels )
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Dict:
with open(_UpperCAmelCase , encoding="utf_8" ) as f:
__snake_case = csv.reader(_UpperCAmelCase )
__snake_case = []
next(_UpperCAmelCase ) # skip the first line
for line in tqdm(_UpperCAmelCase ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def __UpperCAmelCase ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] ) -> List[Any]:
__snake_case = []
for dataset in encoded_datasets:
__snake_case = len(_UpperCAmelCase )
__snake_case = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
__snake_case = np.zeros((n_batch, 2) , dtype=np.intaa )
__snake_case = np.full((n_batch, 2, input_len) , fill_value=-1_00 , dtype=np.intaa )
__snake_case = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_UpperCAmelCase ):
__snake_case = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__snake_case = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__snake_case = with_conta
__snake_case = with_conta
__snake_case = len(_UpperCAmelCase ) - 1
__snake_case = len(_UpperCAmelCase ) - 1
__snake_case = with_conta
__snake_case = with_conta
__snake_case = mc_label
__snake_case = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_UpperCAmelCase ) for t in all_inputs ) )
return tensor_datasets
def __UpperCAmelCase ( ) -> Optional[int]:
__snake_case = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=_UpperCAmelCase , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=_UpperCAmelCase , type=_UpperCAmelCase , required=_UpperCAmelCase , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=_UpperCAmelCase , default="" )
parser.add_argument("--eval_dataset" , type=_UpperCAmelCase , default="" )
parser.add_argument("--seed" , type=_UpperCAmelCase , default=42 )
parser.add_argument("--num_train_epochs" , type=_UpperCAmelCase , default=3 )
parser.add_argument("--train_batch_size" , type=_UpperCAmelCase , default=8 )
parser.add_argument("--eval_batch_size" , type=_UpperCAmelCase , default=16 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=_UpperCAmelCase , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=_UpperCAmelCase , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=_UpperCAmelCase , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=_UpperCAmelCase , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=_UpperCAmelCase , default=6.2_5E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=_UpperCAmelCase , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=_UpperCAmelCase , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=_UpperCAmelCase , default=0.01 )
parser.add_argument("--lm_coef" , type=_UpperCAmelCase , default=0.9 )
parser.add_argument("--n_valid" , type=_UpperCAmelCase , default=3_74 )
parser.add_argument("--server_ip" , type=_UpperCAmelCase , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=_UpperCAmelCase , default="" , help="Can be used for distant debugging." )
__snake_case = parser.parse_args()
print(_UpperCAmelCase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=_UpperCAmelCase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__snake_case = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
__snake_case = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(_UpperCAmelCase , _UpperCAmelCase ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__snake_case = ["_start_", "_delimiter_", "_classify_"]
__snake_case = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_UpperCAmelCase )
__snake_case = tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
__snake_case = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_UpperCAmelCase ) )
model.to(_UpperCAmelCase )
# Load and encode the datasets
def tokenize_and_encode(_UpperCAmelCase : Optional[Any] ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_UpperCAmelCase ) )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return obj
return [tokenize_and_encode(_UpperCAmelCase ) for o in obj]
logger.info("Encoding dataset..." )
__snake_case = load_rocstories_dataset(args.train_dataset )
__snake_case = load_rocstories_dataset(args.eval_dataset )
__snake_case = (train_dataset, eval_dataset)
__snake_case = tokenize_and_encode(_UpperCAmelCase )
# Compute the max input length for the Transformer
__snake_case = model.config.n_positions // 2 - 2
__snake_case = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__snake_case = min(_UpperCAmelCase , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__snake_case = pre_process_datasets(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase )
__snake_case , __snake_case = tensor_datasets[0], tensor_datasets[1]
__snake_case = TensorDataset(*_UpperCAmelCase )
__snake_case = RandomSampler(_UpperCAmelCase )
__snake_case = DataLoader(_UpperCAmelCase , sampler=_UpperCAmelCase , batch_size=args.train_batch_size )
__snake_case = TensorDataset(*_UpperCAmelCase )
__snake_case = SequentialSampler(_UpperCAmelCase )
__snake_case = DataLoader(_UpperCAmelCase , sampler=_UpperCAmelCase , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__snake_case = args.max_steps
__snake_case = args.max_steps // (len(_UpperCAmelCase ) // args.gradient_accumulation_steps) + 1
else:
__snake_case = len(_UpperCAmelCase ) // args.gradient_accumulation_steps * args.num_train_epochs
__snake_case = list(model.named_parameters() )
__snake_case = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
__snake_case = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], "weight_decay": 0.0},
]
__snake_case = AdamW(_UpperCAmelCase , lr=args.learning_rate , eps=args.adam_epsilon )
__snake_case = get_linear_schedule_with_warmup(
_UpperCAmelCase , num_warmup_steps=args.warmup_steps , num_training_steps=_UpperCAmelCase )
if args.do_train:
__snake_case , __snake_case , __snake_case = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
__snake_case = 0
__snake_case = 0
__snake_case = tqdm(_UpperCAmelCase , desc="Training" )
for step, batch in enumerate(_UpperCAmelCase ):
__snake_case = tuple(t.to(_UpperCAmelCase ) for t in batch )
__snake_case , __snake_case , __snake_case , __snake_case = batch
__snake_case = model(_UpperCAmelCase , mc_token_ids=_UpperCAmelCase , lm_labels=_UpperCAmelCase , mc_labels=_UpperCAmelCase )
__snake_case = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__snake_case = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__snake_case = "Training loss: {:.2e} lr: {:.2e}".format(_UpperCAmelCase , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__snake_case = model.module if hasattr(_UpperCAmelCase , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__snake_case = os.path.join(args.output_dir , _UpperCAmelCase )
__snake_case = os.path.join(args.output_dir , _UpperCAmelCase )
torch.save(model_to_save.state_dict() , _UpperCAmelCase )
model_to_save.config.to_json_file(_UpperCAmelCase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__snake_case = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__snake_case = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_UpperCAmelCase )
if args.do_eval:
model.eval()
__snake_case , __snake_case = 0, 0
__snake_case , __snake_case = 0, 0
for batch in tqdm(_UpperCAmelCase , desc="Evaluating" ):
__snake_case = tuple(t.to(_UpperCAmelCase ) for t in batch )
__snake_case , __snake_case , __snake_case , __snake_case = batch
with torch.no_grad():
__snake_case , __snake_case , __snake_case , __snake_case = model(
_UpperCAmelCase , mc_token_ids=_UpperCAmelCase , lm_labels=_UpperCAmelCase , mc_labels=_UpperCAmelCase )
__snake_case = mc_logits.detach().cpu().numpy()
__snake_case = mc_labels.to("cpu" ).numpy()
__snake_case = accuracy(_UpperCAmelCase , _UpperCAmelCase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__snake_case = eval_loss / nb_eval_steps
__snake_case = eval_accuracy / nb_eval_examples
__snake_case = tr_loss / nb_tr_steps if args.do_train else None
__snake_case = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
__snake_case = os.path.join(args.output_dir , "eval_results.txt" )
with open(_UpperCAmelCase , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , _UpperCAmelCase , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 69 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( lowercase__ , unittest.TestCase ):
"""simple docstring"""
a : Optional[int] = GPTSanJapaneseTokenizer
a : Optional[Any] = False
a : List[str] = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCAmelCase ( self : Tuple ) -> Any:
super().setUp()
# fmt: off
__UpperCAmelCase : Tuple = ["""こん""", """こんに""", """にちは""", """ばんは""", """世界,㔺界""", """、""", """。""", """<BR>""", """<SP>""", """<TAB>""", """<URL>""", """<EMAIL>""", """<TEL>""", """<DATE>""", """<PRICE>""", """<BLOCK>""", """<KIGOU>""", """<U2000U2BFF>""", """<|emoji1|>""", """<unk>""", """<|bagoftoken|>""", """<|endoftext|>"""]
# fmt: on
__UpperCAmelCase : Dict = {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}} # 😀
__UpperCAmelCase : Dict = {"""unk_token""": """<unk>"""}
__UpperCAmelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
__UpperCAmelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""emoji_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
with open(self.emoji_file , """w""" ) as emoji_writer:
emoji_writer.write(json.dumps(__lowercase ) )
def UpperCAmelCase ( self : Tuple , **__lowercase : int ) -> Any:
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def UpperCAmelCase ( self : str , __lowercase : Union[str, Any] ) -> Any:
__UpperCAmelCase : Any = """こんにちは、世界。 \nこんばんは、㔺界。😀"""
__UpperCAmelCase : int = """こんにちは、世界。 \nこんばんは、世界。😀"""
return input_text, output_text
def UpperCAmelCase ( self : List[Any] , __lowercase : Optional[int] ) -> List[Any]:
__UpperCAmelCase , __UpperCAmelCase : int = self.get_input_output_texts(__lowercase )
__UpperCAmelCase : Tuple = tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
__UpperCAmelCase : Dict = tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
return text, ids
def UpperCAmelCase ( self : int ) -> Optional[Any]:
pass # TODO add if relevant
def UpperCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
pass # TODO add if relevant
def UpperCAmelCase ( self : Dict ) -> Tuple:
pass # TODO add if relevant
def UpperCAmelCase ( self : str ) -> Tuple:
__UpperCAmelCase : List[str] = self.get_tokenizer()
# Testing tokenization
__UpperCAmelCase : int = """こんにちは、世界。 こんばんは、㔺界。"""
__UpperCAmelCase : Dict = ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
__UpperCAmelCase : Optional[Any] = tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids without special tokens
__UpperCAmelCase : List[str] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
__UpperCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids with special tokens
__UpperCAmelCase : List[Any] = tokens + [tokenizer.unk_token]
__UpperCAmelCase : str = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
__UpperCAmelCase : Any = tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
def UpperCAmelCase ( self : Tuple ) -> Dict:
__UpperCAmelCase : int = self.get_tokenizer()
# Testing tokenization
__UpperCAmelCase : Tuple = """こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
__UpperCAmelCase : int = """こんにちは、、、、世界。こんばんは、、、、世界。"""
__UpperCAmelCase : Tuple = tokenizer.encode(__lowercase )
__UpperCAmelCase : int = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , __lowercase )
@slow
def UpperCAmelCase ( self : int ) -> Optional[int]:
__UpperCAmelCase : Tuple = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
# Testing tokenization
__UpperCAmelCase : List[Any] = """こんにちは、世界。"""
__UpperCAmelCase : Optional[int] = """こんばんは、㔺界。😀"""
__UpperCAmelCase : List[Any] = """こんにちは、世界。こんばんは、世界。😀"""
__UpperCAmelCase : List[str] = tokenizer.encode(prefix_text + input_text )
__UpperCAmelCase : List[Any] = tokenizer.encode("""""" , prefix_text=prefix_text + input_text )
__UpperCAmelCase : Any = tokenizer.encode(__lowercase , prefix_text=__lowercase )
__UpperCAmelCase : Optional[int] = tokenizer.decode(__lowercase )
__UpperCAmelCase : Any = tokenizer.decode(__lowercase )
__UpperCAmelCase : Optional[Any] = tokenizer.decode(__lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , __lowercase )
@slow
def UpperCAmelCase ( self : Any ) -> str:
__UpperCAmelCase : int = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
# Testing tokenization
__UpperCAmelCase : int = """こんにちは、世界。"""
__UpperCAmelCase : List[Any] = """こんばんは、㔺界。😀"""
__UpperCAmelCase : Union[str, Any] = len(tokenizer.encode(__lowercase ) ) - 2
__UpperCAmelCase : int = len(tokenizer.encode(__lowercase ) ) - 2
__UpperCAmelCase : List[Any] = [1] + [0] * (len_prefix + len_text + 1)
__UpperCAmelCase : Union[str, Any] = [1] * (len_prefix + len_text + 1) + [0]
__UpperCAmelCase : List[Any] = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
__UpperCAmelCase : Union[str, Any] = tokenizer(prefix_text + input_text ).token_type_ids
__UpperCAmelCase : Optional[Any] = tokenizer("""""" , prefix_text=prefix_text + input_text ).token_type_ids
__UpperCAmelCase : Tuple = tokenizer(__lowercase , prefix_text=__lowercase ).token_type_ids
self.assertListEqual(__lowercase , __lowercase )
self.assertListEqual(__lowercase , __lowercase )
self.assertListEqual(__lowercase , __lowercase )
@slow
def UpperCAmelCase ( self : List[str] ) -> int:
__UpperCAmelCase : Tuple = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
__UpperCAmelCase : Optional[int] = tokenizer.encode("""あンいワ""" )
__UpperCAmelCase : Tuple = tokenizer.encode("""""" , prefix_text="""あンいワ""" )
__UpperCAmelCase : Optional[int] = tokenizer.encode("""いワ""" , prefix_text="""あン""" )
self.assertEqual(tokenizer.decode(__lowercase ) , tokenizer.decode(__lowercase ) )
self.assertEqual(tokenizer.decode(__lowercase ) , tokenizer.decode(__lowercase ) )
self.assertNotEqual(__lowercase , __lowercase )
self.assertNotEqual(__lowercase , __lowercase )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCAmelCase ( self : List[Any] ) -> List[str]:
__UpperCAmelCase : Any = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
__UpperCAmelCase : List[Any] = [["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
__UpperCAmelCase : int = tokenizer(__lowercase , padding=__lowercase )
__UpperCAmelCase : Optional[Any] = tokenizer.batch_encode_plus(__lowercase , padding=__lowercase )
# fmt: off
__UpperCAmelCase : Optional[int] = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
__UpperCAmelCase : Tuple = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
__UpperCAmelCase : Union[str, Any] = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , __lowercase )
self.assertListEqual(x_token.token_type_ids , __lowercase )
self.assertListEqual(x_token.attention_mask , __lowercase )
self.assertListEqual(x_token_a.input_ids , __lowercase )
self.assertListEqual(x_token_a.token_type_ids , __lowercase )
self.assertListEqual(x_token_a.attention_mask , __lowercase )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def UpperCAmelCase ( self : Any ) -> int:
# tokenizer has no padding token
pass
| 63 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = field(default='automatic-speech-recognition' ,metadata={'include_in_asdict_even_if_is_default': True} )
_UpperCamelCase = Features({'audio': Audio()} )
_UpperCamelCase = Features({'transcription': Value('string' )} )
_UpperCamelCase = "audio"
_UpperCamelCase = "transcription"
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if self.audio_column not in features:
raise ValueError(F'''Column {self.audio_column} is not present in features.''' )
if not isinstance(features[self.audio_column] ,_lowerCAmelCase ):
raise ValueError(F'''Column {self.audio_column} is not an Audio type.''' )
lowerCamelCase__ = copy.deepcopy(self )
lowerCamelCase__ = self.input_schema.copy()
lowerCamelCase__ = features[self.audio_column]
lowerCamelCase__ = input_schema
return task_template
@property
def UpperCamelCase_ ( self ):
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 9 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase : List[Any] = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = GPTSwaTokenizer
_UpperCamelCase = False
_UpperCamelCase = True
_UpperCamelCase = False
def UpperCamelCase_ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase__ = GPTSwaTokenizer(_lowerCAmelCase ,eos_token="""<unk>""" ,bos_token="""<unk>""" ,pad_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = """This is a test"""
lowerCamelCase__ = """This is a test"""
return input_text, output_text
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """<s>"""
lowerCamelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase ) ,_lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase ) ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<unk>""" )
self.assertEqual(vocab_keys[1] ,"""<s>""" )
self.assertEqual(vocab_keys[-1] ,"""j""" )
self.assertEqual(len(_lowerCAmelCase ) ,20_00 )
def UpperCamelCase_ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size ,20_00 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = GPTSwaTokenizer(_lowerCAmelCase )
lowerCamelCase__ = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_lowerCAmelCase ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) ,[4_65, 2_87, 2_65, 6_31, 8_42] )
lowerCamelCase__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
# fmt: off
self.assertListEqual(
_lowerCAmelCase ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] ,)
# fmt: on
lowerCamelCase__ = tokenizer.convert_tokens_to_ids(_lowerCAmelCase )
self.assertListEqual(
_lowerCAmelCase ,[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60] ,)
lowerCamelCase__ = tokenizer.convert_ids_to_tokens(_lowerCAmelCase )
# fmt: off
self.assertListEqual(
_lowerCAmelCase ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] )
# fmt: on
def UpperCamelCase_ ( self ):
lowerCamelCase__ = GPTSwaTokenizer(_lowerCAmelCase )
lowerCamelCase__ = ["""This is a test""", """I was born in 92000, and this is falsé."""]
lowerCamelCase__ = [
[4_65, 2_87, 2_65, 6_31, 8_42],
[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(_lowerCAmelCase ,_lowerCAmelCase ):
self.assertListEqual(tokenizer.encode_fast(_lowerCAmelCase ) ,_lowerCAmelCase )
# Test that decode_fast returns the input text
for text, token_ids in zip(_lowerCAmelCase ,_lowerCAmelCase ):
self.assertEqual(tokenizer.decode_fast(_lowerCAmelCase ) ,_lowerCAmelCase )
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')""",
"""Hey there, how are you doing this fine day?""",
"""This is a text with a trailing spaces followed by a dot .""",
"""Häj sväjs lillebrör! =)""",
"""Det är inget fel på Mr. Cool""",
]
# fmt: off
lowerCamelCase__ = {"""input_ids""": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCAmelCase ,model_name="""AI-Sweden/gpt-sw3-126m""" ,sequences=_lowerCAmelCase ,)
| 9 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__lowercase : str = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
''' Distillation'''
)
)
parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
parser.add_argument('''--vocab_transform''', action='''store_true''')
__lowercase : List[str] = parser.parse_args()
if args.model_type == "bert":
__lowercase : int = BertForMaskedLM.from_pretrained(args.model_name)
__lowercase : List[Any] = '''bert'''
else:
raise ValueError('''args.model_type should be "bert".''')
__lowercase : Dict = model.state_dict()
__lowercase : Optional[Any] = {}
for w in ["word_embeddings", "position_embeddings"]:
__lowercase : Union[str, Any] = state_dict[f'''{prefix}.embeddings.{w}.weight''']
for w in ["weight", "bias"]:
__lowercase : List[str] = state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']
__lowercase : Tuple = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
__lowercase : List[str] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
]
__lowercase : Union[str, Any] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
]
__lowercase : int = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
]
__lowercase : Any = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
]
__lowercase : Dict = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
]
__lowercase : Tuple = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
]
__lowercase : Dict = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
]
__lowercase : Optional[Any] = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
]
std_idx += 1
__lowercase : Any = state_dict['''cls.predictions.decoder.weight''']
__lowercase : List[str] = state_dict['''cls.predictions.bias''']
if args.vocab_transform:
for w in ["weight", "bias"]:
__lowercase : Any = state_dict[f'''cls.predictions.transform.dense.{w}''']
__lowercase : str = state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']
print(f'''N layers selected for distillation: {std_idx}''')
print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint)
| 36 | '''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCAmelCase ( a__ ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=True , __lowerCAmelCase=None , **__lowerCAmelCase ) -> Dict:
lowercase__ : Any = parent
lowercase__ : List[Any] = config_class
lowercase__ : Dict = has_text_modality
lowercase__ : List[str] = kwargs
lowercase__ : List[Any] = common_properties
def _lowerCAmelCase( self ) -> Any:
lowercase__ : int = self.config_class(**self.inputs_dict )
lowercase__ : Any = (
['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(['''vocab_size'''] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(__lowerCAmelCase , __lowerCAmelCase ) , msg=F"""`{prop}` does not exist""" )
# Test that config has the common properties as setter
for idx, name in enumerate(__lowerCAmelCase ):
try:
setattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
self.parent.assertEqual(
getattr(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , msg=F"""`{name} value {idx} expected, but was {getattr(__lowerCAmelCase , __lowerCAmelCase )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(__lowerCAmelCase ):
try:
lowercase__ : Tuple = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , msg=F"""`{name} value {idx} expected, but was {getattr(__lowerCAmelCase , __lowerCAmelCase )}""" )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : Union[str, Any] = self.config_class(**self.inputs_dict )
lowercase__ : int = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> int:
lowercase__ : List[str] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : Tuple = os.path.join(__lowerCAmelCase , '''config.json''' )
config_first.to_json_file(__lowerCAmelCase )
lowercase__ : Tuple = self.config_class.from_json_file(__lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ : Optional[int] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(__lowerCAmelCase )
lowercase__ : Dict = self.config_class.from_pretrained(__lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowerCAmelCase( self ) -> Optional[int]:
lowercase__ : Union[str, Any] = self.config_class(**self.inputs_dict )
lowercase__ : str = '''test'''
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : Any = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
config_first.save_pretrained(__lowerCAmelCase )
lowercase__ : Tuple = self.config_class.from_pretrained(__lowerCAmelCase , subfolder=__lowerCAmelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : List[Any] = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
lowercase__ : List[Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def _lowerCAmelCase( self ) -> Any:
if self.config_class.is_composition:
return
lowercase__ : Tuple = self.config_class()
self.parent.assertIsNotNone(__lowerCAmelCase )
def _lowerCAmelCase( self ) -> Any:
lowercase__ : str = copy.deepcopy(__lowerCAmelCase )
lowercase__ : Dict = self.config_class(**__lowerCAmelCase )
lowercase__ : Dict = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa) )
elif getattr(__lowerCAmelCase , __lowerCAmelCase ) != value:
wrong_values.append((key, getattr(__lowerCAmelCase , __lowerCAmelCase ), value) )
if len(__lowerCAmelCase ) > 0:
lowercase__ : Any = '''\n'''.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""" )
def _lowerCAmelCase( self ) -> Any:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 152 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase : int = {
'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 : Dict = [
'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 : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 432 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = ["""pixel_values"""]
def __init__( self , A_ = True , A_ = None , A_ = PILImageResampling.BILINEAR , A_ = True , A_ = 1 / 255 , A_ = True , A_ = None , A_ = True , **A_ , )-> None:
'''simple docstring'''
super().__init__(**A_ )
UpperCamelCase = size if size is not None else {'shortest_edge': 224}
UpperCamelCase = get_size_dict(A_ , default_to_square=A_ )
UpperCamelCase = crop_size if crop_size is not None else {'height': 256, 'width': 256}
UpperCamelCase = get_size_dict(A_ , param_name='crop_size' )
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = resample
UpperCamelCase = do_rescale
UpperCamelCase = rescale_factor
UpperCamelCase = do_center_crop
UpperCamelCase = crop_size
UpperCamelCase = do_flip_channel_order
def UpperCAmelCase_ ( self , A_ , A_ , A_ = PIL.Image.BILINEAR , A_ = None , **A_ , )-> np.ndarray:
'''simple docstring'''
UpperCamelCase = get_size_dict(A_ , default_to_square=A_ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''' )
UpperCamelCase = get_resize_output_image_size(A_ , size=size['shortest_edge'] , default_to_square=A_ )
return resize(A_ , size=A_ , resample=A_ , data_format=A_ , **A_ )
def UpperCAmelCase_ ( self , A_ , A_ , A_ = None , **A_ , )-> np.ndarray:
'''simple docstring'''
UpperCamelCase = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
return center_crop(A_ , size=(size['height'], size['width']) , data_format=A_ , **A_ )
def UpperCAmelCase_ ( self , A_ , A_ , A_ = None , **A_ , )-> List[str]:
'''simple docstring'''
return rescale(A_ , scale=A_ , data_format=A_ , **A_ )
def UpperCAmelCase_ ( self , A_ , A_ = None )-> np.ndarray:
'''simple docstring'''
return flip_channel_order(A_ , data_format=A_ )
def UpperCAmelCase_ ( self , A_ , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = ChannelDimension.FIRST , **A_ , )-> PIL.Image.Image:
'''simple docstring'''
UpperCamelCase = do_resize if do_resize is not None else self.do_resize
UpperCamelCase = resample if resample is not None else self.resample
UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCamelCase = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
UpperCamelCase = size if size is not None else self.size
UpperCamelCase = get_size_dict(A_ , default_to_square=A_ )
UpperCamelCase = crop_size if crop_size is not None else self.crop_size
UpperCamelCase = get_size_dict(A_ , param_name='crop_size' )
UpperCamelCase = make_list_of_images(A_ )
if not valid_images(A_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
# All transformations expect numpy arrays.
UpperCamelCase = [to_numpy_array(A_ ) for image in images]
if do_resize:
UpperCamelCase = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images]
if do_center_crop:
UpperCamelCase = [self.center_crop(image=A_ , size=A_ ) for image in images]
if do_rescale:
UpperCamelCase = [self.rescale(image=A_ , scale=A_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
UpperCamelCase = [self.flip_channel_order(image=A_ ) for image in images]
UpperCamelCase = [to_channel_dimension_format(A_ , A_ ) for image in images]
UpperCamelCase = {'pixel_values': images}
return BatchFeature(data=A_ , tensor_type=A_ )
def UpperCAmelCase_ ( self , A_ , A_ = None )-> Dict:
'''simple docstring'''
UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(A_ ) != len(A_ ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(A_ ):
UpperCamelCase = target_sizes.numpy()
UpperCamelCase = []
for idx in range(len(A_ ) ):
UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=A_ )
UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(A_ )
else:
UpperCamelCase = logits.argmax(dim=1 )
UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 432 | 1 |
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