code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
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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
lowerCAmelCase_ = datasets.utils.logging.get_logger(__name__)
lowerCAmelCase_ = ['''names''', '''prefix''']
lowerCAmelCase_ = ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
lowerCAmelCase_ = ['''encoding_errors''', '''on_bad_lines''']
lowerCAmelCase_ = ['''date_format''']
@dataclass
class __lowerCAmelCase ( datasets.BuilderConfig ):
lowerCamelCase_ : str = ","
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : Optional[Union[int, List[int], str]] = "infer"
lowerCamelCase_ : Optional[List[str]] = None
lowerCamelCase_ : Optional[List[str]] = None
lowerCamelCase_ : Optional[Union[int, str, List[int], List[str]]] = None
lowerCamelCase_ : Optional[Union[List[int], List[str]]] = None
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : bool = True
lowerCamelCase_ : Optional[Literal["c", "python", "pyarrow"]] = None
lowerCamelCase_ : Dict[Union[int, str], Callable[[Any], Any]] = None
lowerCamelCase_ : Optional[list] = None
lowerCamelCase_ : Optional[list] = None
lowerCamelCase_ : bool = False
lowerCamelCase_ : Optional[Union[int, List[int]]] = None
lowerCamelCase_ : Optional[int] = None
lowerCamelCase_ : Optional[Union[str, List[str]]] = None
lowerCamelCase_ : bool = True
lowerCamelCase_ : bool = True
lowerCamelCase_ : bool = False
lowerCamelCase_ : bool = True
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : str = "."
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : str = '"'
lowerCamelCase_ : int = 0
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : bool = True
lowerCamelCase_ : bool = True
lowerCamelCase_ : int = 0
lowerCamelCase_ : bool = True
lowerCamelCase_ : bool = False
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : int = 10_000
lowerCamelCase_ : Optional[datasets.Features] = None
lowerCamelCase_ : Optional[str] = "strict"
lowerCamelCase_ : Literal["error", "warn", "skip"] = "error"
lowerCamelCase_ : Optional[str] = None
def lowerCamelCase (self ) -> str:
'''simple docstring'''
if self.delimiter is not None:
snake_case_ : Any = self.delimiter
if self.column_names is not None:
snake_case_ : Any = self.column_names
@property
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[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() , __magic_name__ ):
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 ):
lowerCamelCase_ : int = CsvConfig
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase (self , __magic_name__ ) -> Optional[int]:
'''simple docstring'''
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}''' )
snake_case_ : Tuple = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__magic_name__ , (str, list, tuple) ):
snake_case_ : Union[str, Any] = data_files
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : str = [files]
snake_case_ : Dict = [dl_manager.iter_files(__magic_name__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
snake_case_ : str = []
for split_name, files in data_files.items():
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : int = [files]
snake_case_ : Optional[Any] = [dl_manager.iter_files(__magic_name__ ) for file in files]
splits.append(datasets.SplitGenerator(name=__magic_name__ , gen_kwargs={'''files''': files} ) )
return splits
def lowerCamelCase (self , __magic_name__ ) -> pa.Table:
'''simple docstring'''
if self.config.features is not None:
snake_case_ : Union[str, Any] = self.config.features.arrow_schema
if all(not require_storage_cast(__magic_name__ ) for feature in self.config.features.values() ):
# cheaper cast
snake_case_ : List[Any] = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=__magic_name__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
snake_case_ : List[str] = table_cast(__magic_name__ , __magic_name__ )
return pa_table
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
snake_case_ : int = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
snake_case_ : Optional[Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(__magic_name__ ) 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(__magic_name__ ) ):
snake_case_ : Any = pd.read_csv(__magic_name__ , iterator=__magic_name__ , dtype=__magic_name__ , **self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(__magic_name__ ):
snake_case_ : Dict = pa.Table.from_pandas(__magic_name__ )
# 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(__magic_name__ )
except ValueError as e:
logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__ )}: {e}''' )
raise
| 60 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Dict = '''layoutlmv3'''
def __init__(self , __magic_name__=5_0265 , __magic_name__=768 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3072 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=512 , __magic_name__=2 , __magic_name__=0.02 , __magic_name__=1e-5 , __magic_name__=1 , __magic_name__=0 , __magic_name__=2 , __magic_name__=1024 , __magic_name__=128 , __magic_name__=128 , __magic_name__=True , __magic_name__=32 , __magic_name__=128 , __magic_name__=64 , __magic_name__=256 , __magic_name__=True , __magic_name__=True , __magic_name__=True , __magic_name__=224 , __magic_name__=3 , __magic_name__=16 , __magic_name__=None , **__magic_name__ , ) -> List[Any]:
'''simple docstring'''
super().__init__(
vocab_size=__magic_name__ , hidden_size=__magic_name__ , num_hidden_layers=__magic_name__ , num_attention_heads=__magic_name__ , intermediate_size=__magic_name__ , hidden_act=__magic_name__ , hidden_dropout_prob=__magic_name__ , attention_probs_dropout_prob=__magic_name__ , max_position_embeddings=__magic_name__ , type_vocab_size=__magic_name__ , initializer_range=__magic_name__ , layer_norm_eps=__magic_name__ , pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ , )
snake_case_ : List[Any] = max_ad_position_embeddings
snake_case_ : Optional[int] = coordinate_size
snake_case_ : Tuple = shape_size
snake_case_ : str = has_relative_attention_bias
snake_case_ : int = rel_pos_bins
snake_case_ : Optional[Any] = max_rel_pos
snake_case_ : List[str] = has_spatial_attention_bias
snake_case_ : Tuple = rel_ad_pos_bins
snake_case_ : Optional[int] = max_rel_ad_pos
snake_case_ : Union[str, Any] = text_embed
snake_case_ : str = visual_embed
snake_case_ : Dict = input_size
snake_case_ : Any = num_channels
snake_case_ : str = patch_size
snake_case_ : List[Any] = classifier_dropout
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = version.parse('''1.12''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
else:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 12
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = False , __magic_name__ = None , __magic_name__ = 3 , __magic_name__ = 40 , __magic_name__ = 40 , ) -> Mapping[str, Any]:
'''simple docstring'''
setattr(processor.image_processor , '''apply_ocr''' , __magic_name__ )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case_ : Dict = compute_effective_axis_dimension(
__magic_name__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case_ : Optional[int] = processor.tokenizer.num_special_tokens_to_add(__magic_name__ )
snake_case_ : Any = compute_effective_axis_dimension(
__magic_name__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__magic_name__ )
# Generate dummy inputs according to compute batch and sequence
snake_case_ : str = [[''' '''.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
snake_case_ : Any = [[[48, 84, 73, 128]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
snake_case_ : Optional[int] = self._generate_dummy_images(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
snake_case_ : List[Any] = dict(
processor(
__magic_name__ , text=__magic_name__ , boxes=__magic_name__ , return_tensors=__magic_name__ , ) )
return inputs
| 60 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''deta'''
lowerCamelCase_ : Optional[Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__(self , __magic_name__=None , __magic_name__=900 , __magic_name__=2048 , __magic_name__=6 , __magic_name__=2048 , __magic_name__=8 , __magic_name__=6 , __magic_name__=1024 , __magic_name__=8 , __magic_name__=0.0 , __magic_name__=True , __magic_name__="relu" , __magic_name__=256 , __magic_name__=0.1 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=True , __magic_name__=False , __magic_name__="sine" , __magic_name__=5 , __magic_name__=4 , __magic_name__=4 , __magic_name__=True , __magic_name__=300 , __magic_name__=True , __magic_name__=True , __magic_name__=1 , __magic_name__=5 , __magic_name__=2 , __magic_name__=1 , __magic_name__=1 , __magic_name__=5 , __magic_name__=2 , __magic_name__=0.1 , __magic_name__=0.25 , **__magic_name__ , ) -> Optional[Any]:
'''simple docstring'''
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
snake_case_ : Tuple = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : int = backbone_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = CONFIG_MAPPING[backbone_model_type]
snake_case_ : Tuple = config_class.from_dict(__magic_name__ )
snake_case_ : Dict = backbone_config
snake_case_ : str = num_queries
snake_case_ : int = max_position_embeddings
snake_case_ : List[Any] = d_model
snake_case_ : Optional[int] = encoder_ffn_dim
snake_case_ : List[str] = encoder_layers
snake_case_ : Optional[Any] = encoder_attention_heads
snake_case_ : Union[str, Any] = decoder_ffn_dim
snake_case_ : int = decoder_layers
snake_case_ : Optional[int] = decoder_attention_heads
snake_case_ : int = dropout
snake_case_ : Tuple = attention_dropout
snake_case_ : Tuple = activation_dropout
snake_case_ : Union[str, Any] = activation_function
snake_case_ : Dict = init_std
snake_case_ : List[str] = init_xavier_std
snake_case_ : int = encoder_layerdrop
snake_case_ : Tuple = auxiliary_loss
snake_case_ : Union[str, Any] = position_embedding_type
# deformable attributes
snake_case_ : Any = num_feature_levels
snake_case_ : List[Any] = encoder_n_points
snake_case_ : Dict = decoder_n_points
snake_case_ : List[str] = two_stage
snake_case_ : Any = two_stage_num_proposals
snake_case_ : List[Any] = with_box_refine
snake_case_ : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
snake_case_ : int = class_cost
snake_case_ : str = bbox_cost
snake_case_ : Optional[Any] = giou_cost
# Loss coefficients
snake_case_ : Dict = mask_loss_coefficient
snake_case_ : int = dice_loss_coefficient
snake_case_ : str = bbox_loss_coefficient
snake_case_ : Union[str, Any] = giou_loss_coefficient
snake_case_ : Tuple = eos_coefficient
snake_case_ : Optional[int] = focal_alpha
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return self.d_model
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[str] = self.backbone_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
| 60 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 1 |
from __future__ import annotations
lowerCAmelCase_ = []
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
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 , _UpperCamelCase ) -> bool:
"""simple docstring"""
if row >= len(_UpperCamelCase ):
solution.append(_UpperCamelCase )
printboard(_UpperCamelCase )
print()
return True
for i in range(len(_UpperCamelCase ) ):
if is_safe(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = 1
solve(_UpperCamelCase , row + 1 )
snake_case_ : Dict = 0
return False
def lowerCamelCase_ ( _UpperCamelCase ) -> None:
"""simple docstring"""
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"))
lowerCAmelCase_ = 8
lowerCAmelCase_ = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 60 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 1 |
from torch import nn
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported activation function: {act_fn}''' )
| 60 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''VisionEncoderDecoderModel''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''TFVisionEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''FlaxVisionEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 1 |
import os
import sys
import unittest
lowerCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCAmelCase_ = os.path.join(git_repo_path, '''src''', '''transformers''')
lowerCAmelCase_ = '''
{0} = None
'''
lowerCAmelCase_ = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
'''
lowerCAmelCase_ = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : List[str] = find_backend(''' _import_structure["models.albert"].append("AlbertTokenizerFast")''' )
self.assertIsNone(__magic_name__ )
snake_case_ : Optional[Any] = find_backend(''' if not is_tokenizers_available():''' )
self.assertEqual(__magic_name__ , '''tokenizers''' )
snake_case_ : Dict = find_backend(''' if not is_tensorflow_text_available():''' )
self.assertEqual(__magic_name__ , '''tensorflow_text''' )
snake_case_ : str = find_backend(''' if not (is_sentencepiece_available() and is_tokenizers_available()):''' )
self.assertEqual(__magic_name__ , '''sentencepiece_and_tokenizers''' )
snake_case_ : Tuple = find_backend(
''' if not (is_sentencepiece_available() and is_tensorflow_text_available()):''' )
self.assertEqual(__magic_name__ , '''sentencepiece_and_tensorflow_text''' )
snake_case_ : Tuple = find_backend(
''' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''' )
self.assertEqual(__magic_name__ , '''sentencepiece_and_tokenizers_and_vision''' )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , __magic_name__ )
self.assertIn('''tensorflow_text''' , __magic_name__ )
self.assertIn('''sentencepiece_and_tokenizers''' , __magic_name__ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertModel''' , objects['''tf'''] )
self.assertIn('''FlaxBertModel''' , objects['''flax'''] )
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertTokenizer''' , objects['''tensorflow_text'''] )
self.assertIn('''convert_slow_tokenizer''' , objects['''sentencepiece_and_tokenizers'''] )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Union[str, Any] = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(__magic_name__ , '''\nCONSTANT = None\n''' )
snake_case_ : Tuple = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
__magic_name__ , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
snake_case_ : Union[str, Any] = '''
class FakeClass(metaclass=DummyObject):
_backends = \'torch\'
def __init__(self, *args, **kwargs):
requires_backends(self, \'torch\')
'''
snake_case_ : Tuple = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(__magic_name__ , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
'''
snake_case_ : List[str] = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , __magic_name__ )
| 60 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 1 |
lowerCAmelCase_ = 8.314_462 # Unit - J mol-1 K-1
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 60 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 60 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_ = Lock()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(_UpperCamelCase )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
snake_case_ : Optional[Any] = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
snake_case_ : Union[str, Any] = min(_UpperCamelCase , _UpperCamelCase )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(_UpperCamelCase )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
snake_case_ : List[Any] = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
snake_case_ : Optional[int] = max(_UpperCamelCase , _UpperCamelCase )
# after all swaps are performed, send the values back to main
result_pipe[1].send(_UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : str = []
snake_case_ : Any = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
snake_case_ : List[str] = Pipe()
snake_case_ : Optional[int] = Pipe()
process_array_.append(
Process(
target=_UpperCamelCase , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
snake_case_ : Union[str, Any] = temp_rs
snake_case_ : Tuple = temp_rr
for i in range(1 , len(_UpperCamelCase ) - 1 ):
snake_case_ : Optional[Any] = Pipe()
snake_case_ : Optional[int] = Pipe()
process_array_.append(
Process(
target=_UpperCamelCase , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
snake_case_ : Any = temp_rs
snake_case_ : Optional[Any] = temp_rr
process_array_.append(
Process(
target=_UpperCamelCase , args=(
len(_UpperCamelCase ) - 1,
arr[len(_UpperCamelCase ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(_UpperCamelCase ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(_UpperCamelCase ) ):
snake_case_ : Optional[int] = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def lowerCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Optional[Any] = list(range(10 , 0 , -1 ) )
print('''Initial List''' )
print(*_UpperCamelCase )
snake_case_ : Dict = odd_even_transposition(_UpperCamelCase )
print('''Sorted List\n''' )
print(*_UpperCamelCase )
if __name__ == "__main__":
main()
| 60 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
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_mvp import MvpTokenizer
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase_ = {
'''vocab_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
},
'''added_tokens.json''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
},
'''merges_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
},
}
lowerCAmelCase_ = {
'''RUCAIBox/mvp''': 1_0_2_4,
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = VOCAB_FILES_NAMES
lowerCamelCase_ : int = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ : Tuple = ['''input_ids''', '''attention_mask''']
lowerCamelCase_ : str = MvpTokenizer
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__="replace" , __magic_name__="<s>" , __magic_name__="</s>" , __magic_name__="</s>" , __magic_name__="<s>" , __magic_name__="<unk>" , __magic_name__="<pad>" , __magic_name__="<mask>" , __magic_name__=False , __magic_name__=True , **__magic_name__ , ) -> Tuple:
'''simple docstring'''
super().__init__(
__magic_name__ , __magic_name__ , tokenizer_file=__magic_name__ , errors=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , add_prefix_space=__magic_name__ , trim_offsets=__magic_name__ , **__magic_name__ , )
snake_case_ : List[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __magic_name__ ) != add_prefix_space:
snake_case_ : Optional[int] = getattr(__magic_name__ , pre_tok_state.pop('''type''' ) )
snake_case_ : Any = add_prefix_space
snake_case_ : List[Any] = pre_tok_class(**__magic_name__ )
snake_case_ : int = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
snake_case_ : Tuple = '''post_processor'''
snake_case_ : Optional[Any] = getattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
if tokenizer_component_instance:
snake_case_ : List[Any] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
snake_case_ : List[str] = tuple(state['''sep'''] )
if "cls" in state:
snake_case_ : str = tuple(state['''cls'''] )
snake_case_ : Tuple = False
if state.get('''add_prefix_space''' , __magic_name__ ) != add_prefix_space:
snake_case_ : int = add_prefix_space
snake_case_ : Dict = True
if state.get('''trim_offsets''' , __magic_name__ ) != trim_offsets:
snake_case_ : Optional[Any] = trim_offsets
snake_case_ : str = True
if changes_to_apply:
snake_case_ : Optional[Any] = getattr(__magic_name__ , state.pop('''type''' ) )
snake_case_ : Optional[int] = component_class(**__magic_name__ )
setattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
snake_case_ : Tuple = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else value
snake_case_ : Union[str, Any] = value
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Any = kwargs.get('''is_split_into_words''' , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> BatchEncoding:
'''simple docstring'''
snake_case_ : Tuple = kwargs.get('''is_split_into_words''' , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> Tuple[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> List[int]:
'''simple docstring'''
snake_case_ : int = [self.sep_token_id]
snake_case_ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 60 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
from __future__ import annotations
lowerCAmelCase_ = 1_0
def lowerCamelCase_ ( _UpperCamelCase ) -> list[int]:
"""simple docstring"""
snake_case_ : Optional[int] = 1
snake_case_ : int = max(_UpperCamelCase )
while placement <= max_digit:
# declare and initialize empty buckets
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
# split list_of_ints between the buckets
for i in list_of_ints:
snake_case_ : Tuple = int((i / placement) % RADIX )
buckets[tmp].append(_UpperCamelCase )
# put each buckets' contents into list_of_ints
snake_case_ : Optional[Any] = 0
for b in range(_UpperCamelCase ):
for i in buckets[b]:
snake_case_ : Optional[int] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def lowerCamelCase_ ( _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ , snake_case_ : Optional[Any] = image.size
snake_case_ , snake_case_ : int = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
snake_case_ : Union[str, Any] = image.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos'''] )
snake_case_ : int = np.array(_UpperCamelCase ).astype(np.floataa ) / 255.0
snake_case_ : Optional[int] = image[None].transpose(0 , 3 , 1 , 2 )
snake_case_ : Tuple = torch.from_numpy(_UpperCamelCase )
return 2.0 * image - 1.0
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , ) -> Any:
'''simple docstring'''
super().__init__()
self.register_modules(vqvae=__magic_name__ , unet=__magic_name__ , scheduler=__magic_name__ )
@torch.no_grad()
def __call__(self , __magic_name__ = None , __magic_name__ = 1 , __magic_name__ = 100 , __magic_name__ = 0.0 , __magic_name__ = None , __magic_name__ = "pil" , __magic_name__ = True , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
if isinstance(__magic_name__ , PIL.Image.Image ):
snake_case_ : Tuple = 1
elif isinstance(__magic_name__ , torch.Tensor ):
snake_case_ : List[str] = image.shape[0]
else:
raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__magic_name__ )}''' )
if isinstance(__magic_name__ , PIL.Image.Image ):
snake_case_ : Optional[int] = preprocess(__magic_name__ )
snake_case_ , snake_case_ : Optional[int] = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
snake_case_ : int = (batch_size, self.unet.config.in_channels // 2, height, width)
snake_case_ : Optional[Any] = next(self.unet.parameters() ).dtype
snake_case_ : Tuple = randn_tensor(__magic_name__ , generator=__magic_name__ , device=self.device , dtype=__magic_name__ )
snake_case_ : str = image.to(device=self.device , dtype=__magic_name__ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(__magic_name__ , device=self.device )
snake_case_ : int = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
snake_case_ : Tuple = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
snake_case_ : List[str] = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
snake_case_ : Optional[Any] = {}
if accepts_eta:
snake_case_ : int = eta
for t in self.progress_bar(__magic_name__ ):
# concat latents and low resolution image in the channel dimension.
snake_case_ : str = torch.cat([latents, image] , dim=1 )
snake_case_ : Tuple = self.scheduler.scale_model_input(__magic_name__ , __magic_name__ )
# predict the noise residual
snake_case_ : Optional[int] = self.unet(__magic_name__ , __magic_name__ ).sample
# compute the previous noisy sample x_t -> x_t-1
snake_case_ : Dict = self.scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ).prev_sample
# decode the image latents with the VQVAE
snake_case_ : Union[str, Any] = self.vqvae.decode(__magic_name__ ).sample
snake_case_ : str = torch.clamp(__magic_name__ , -1.0 , 1.0 )
snake_case_ : Union[str, Any] = image / 2 + 0.5
snake_case_ : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ : Optional[Any] = self.numpy_to_pil(__magic_name__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__magic_name__ )
| 60 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 1 |
# 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
lowerCAmelCase_ = get_logger()
lowerCAmelCase_ = None
class __lowerCAmelCase ( TensorFormatter[Mapping, '''jax.Array''', Mapping] ):
def __init__(self , __magic_name__=None , __magic_name__=None , **__magic_name__ ) -> Tuple:
'''simple docstring'''
super().__init__(features=__magic_name__ )
import jax
from jaxlib.xla_client import Device
if isinstance(__magic_name__ , __magic_name__ ):
raise ValueError(
F'''Expected {device} to be a `str` not {type(__magic_name__ )}, 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`.''' )
snake_case_ : Any = device if isinstance(__magic_name__ , __magic_name__ ) 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:
snake_case_ : Dict = 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] )}.''' )
snake_case_ : Any = str(jax.devices()[0] )
snake_case_ : int = jnp_array_kwargs
@staticmethod
def lowerCamelCase () -> Dict[str, "jaxlib.xla_extension.Device"]:
'''simple docstring'''
import jax
return {str(__magic_name__ ): device for device in jax.devices()}
def lowerCamelCase (self , __magic_name__ ) -> List[Any]:
'''simple docstring'''
import jax
import jax.numpy as jnp
if isinstance(__magic_name__ , __magic_name__ ) and column:
if all(
isinstance(__magic_name__ , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__magic_name__ , axis=0 )
return column
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
import jax
import jax.numpy as jnp
if isinstance(__magic_name__ , (str, bytes, type(__magic_name__ )) ):
return value
elif isinstance(__magic_name__ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
snake_case_ : Any = {}
if isinstance(__magic_name__ , (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:
snake_case_ : List[Any] = {'''dtype''': jnp.intaa}
else:
snake_case_ : Dict = {'''dtype''': jnp.intaa}
elif isinstance(__magic_name__ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
snake_case_ : Any = {'''dtype''': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__magic_name__ , PIL.Image.Image ):
snake_case_ : Dict = np.asarray(__magic_name__ )
# 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:
snake_case_ : str = 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(__magic_name__ , **{**default_dtype, **self.jnp_array_kwargs} )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__magic_name__ , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__magic_name__ , '''__array__''' ) and not isinstance(__magic_name__ , jax.Array ):
snake_case_ : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__magic_name__ , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__magic_name__ ) for substruct in data_struct] )
elif isinstance(__magic_name__ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__magic_name__ ) for substruct in data_struct] )
return self._tensorize(__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return map_nested(self._recursive_tensorize , __magic_name__ , map_list=__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Mapping:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.numpy_arrow_extractor().extract_row(__magic_name__ )
snake_case_ : str = self.python_features_decoder.decode_row(__magic_name__ )
return self.recursive_tensorize(__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> "jax.Array":
'''simple docstring'''
snake_case_ : Dict = self.numpy_arrow_extractor().extract_column(__magic_name__ )
snake_case_ : str = self.python_features_decoder.decode_column(__magic_name__ , pa_table.column_names[0] )
snake_case_ : List[Any] = self.recursive_tensorize(__magic_name__ )
snake_case_ : Dict = self._consolidate(__magic_name__ )
return column
def lowerCamelCase (self , __magic_name__ ) -> Mapping:
'''simple docstring'''
snake_case_ : List[Any] = self.numpy_arrow_extractor().extract_batch(__magic_name__ )
snake_case_ : Any = self.python_features_decoder.decode_batch(__magic_name__ )
snake_case_ : Any = self.recursive_tensorize(__magic_name__ )
for column_name in batch:
snake_case_ : str = self._consolidate(batch[column_name] )
return batch
| 60 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 1 |
from collections.abc import Generator
from math import sin
def lowerCamelCase_ ( _UpperCamelCase ) -> bytes:
"""simple docstring"""
if len(_UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
snake_case_ : str = b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def lowerCamelCase_ ( _UpperCamelCase ) -> bytes:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
snake_case_ : Any = format(_UpperCamelCase , '''08x''' )[-8:]
snake_case_ : Optional[int] = b''''''
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8''' )
return little_endian_hex
def lowerCamelCase_ ( _UpperCamelCase ) -> bytes:
"""simple docstring"""
snake_case_ : Union[str, Any] = b''''''
for char in message:
bit_string += format(_UpperCamelCase , '''08b''' ).encode('''utf-8''' )
snake_case_ : str = format(len(_UpperCamelCase ) , '''064b''' ).encode('''utf-8''' )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(_UpperCamelCase ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def lowerCamelCase_ ( _UpperCamelCase ) -> Generator[list[int], None, None]:
"""simple docstring"""
if len(_UpperCamelCase ) % 512 != 0:
raise ValueError('''Input must have length that\'s a multiple of 512''' )
for pos in range(0 , len(_UpperCamelCase ) , 512 ):
snake_case_ : List[Any] = bit_string[pos : pos + 512]
snake_case_ : Tuple = []
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
snake_case_ : List[Any] = format(_UpperCamelCase , '''032b''' )
snake_case_ : Union[str, Any] = ''''''
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(_UpperCamelCase , 2 )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
return (a + b) % 2**32
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
if shift < 0:
raise ValueError('''Shift must be non-negative''' )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def lowerCamelCase_ ( _UpperCamelCase ) -> bytes:
"""simple docstring"""
snake_case_ : Dict = preprocess(_UpperCamelCase )
snake_case_ : List[str] = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
snake_case_ : Optional[int] = 0x67452301
snake_case_ : Union[str, Any] = 0xefcdab89
snake_case_ : Dict = 0x98badcfe
snake_case_ : Tuple = 0x10325476
snake_case_ : List[str] = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(_UpperCamelCase ):
snake_case_ : Optional[Any] = aa
snake_case_ : Dict = ba
snake_case_ : Union[str, Any] = ca
snake_case_ : Optional[Any] = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
snake_case_ : int = d ^ (b & (c ^ d))
snake_case_ : Union[str, Any] = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
snake_case_ : Tuple = c ^ (d & (b ^ c))
snake_case_ : List[str] = (5 * i + 1) % 16
elif i <= 47:
snake_case_ : Optional[Any] = b ^ c ^ d
snake_case_ : List[Any] = (3 * i + 5) % 16
else:
snake_case_ : Tuple = c ^ (b | not_aa(_UpperCamelCase ))
snake_case_ : Optional[int] = (7 * i) % 16
snake_case_ : str = (f + a + added_consts[i] + block_words[g]) % 2**32
snake_case_ : int = d
snake_case_ : Any = c
snake_case_ : Optional[int] = b
snake_case_ : Tuple = sum_aa(_UpperCamelCase , left_rotate_aa(_UpperCamelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
snake_case_ : List[Any] = sum_aa(_UpperCamelCase , _UpperCamelCase )
snake_case_ : Optional[int] = sum_aa(_UpperCamelCase , _UpperCamelCase )
snake_case_ : List[str] = sum_aa(_UpperCamelCase , _UpperCamelCase )
snake_case_ : List[str] = sum_aa(_UpperCamelCase , _UpperCamelCase )
snake_case_ : Optional[Any] = reformat_hex(_UpperCamelCase ) + reformat_hex(_UpperCamelCase ) + reformat_hex(_UpperCamelCase ) + reformat_hex(_UpperCamelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : Dict = WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase , config=_UpperCamelCase )
snake_case_ : Optional[int] = downstream_dict['''projector.weight''']
snake_case_ : Dict = downstream_dict['''projector.bias''']
snake_case_ : Union[str, Any] = downstream_dict['''model.post_net.linear.weight''']
snake_case_ : Union[str, Any] = downstream_dict['''model.post_net.linear.bias''']
return model
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : Union[str, Any] = WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase , config=_UpperCamelCase )
snake_case_ : Optional[Any] = downstream_dict['''model.linear.weight''']
snake_case_ : int = downstream_dict['''model.linear.bias''']
return model
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = WavaVecaForXVector.from_pretrained(_UpperCamelCase , config=_UpperCamelCase )
snake_case_ : List[Any] = downstream_dict['''connector.weight''']
snake_case_ : Any = downstream_dict['''connector.bias''']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
snake_case_ : Any = downstream_dict[
f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
snake_case_ : List[Any] = downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
snake_case_ : Any = downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
snake_case_ : Union[str, Any] = downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
snake_case_ : Optional[int] = downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
snake_case_ : Optional[int] = downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
snake_case_ : Optional[Any] = downstream_dict['''objective.W''']
return model
@torch.no_grad()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Tuple = torch.load(_UpperCamelCase , map_location='''cpu''' )
snake_case_ : Union[str, Any] = checkpoint['''Downstream''']
snake_case_ : Dict = WavaVecaConfig.from_pretrained(_UpperCamelCase )
snake_case_ : List[Any] = WavaVecaFeatureExtractor.from_pretrained(
_UpperCamelCase , return_attention_mask=_UpperCamelCase , do_normalize=_UpperCamelCase )
snake_case_ : List[Any] = hf_config.architectures[0]
if arch.endswith('''ForSequenceClassification''' ):
snake_case_ : Tuple = convert_classification(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
elif arch.endswith('''ForAudioFrameClassification''' ):
snake_case_ : Any = convert_diarization(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
elif arch.endswith('''ForXVector''' ):
snake_case_ : Optional[Any] = convert_xvector(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
else:
raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
snake_case_ : Dict = checkpoint['''Featurizer''']['''weights''']
hf_feature_extractor.save_pretrained(_UpperCamelCase )
hf_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
lowerCAmelCase_ = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 60 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''Speech2TextFeatureExtractor'''
lowerCamelCase_ : Optional[Any] = '''Speech2TextTokenizer'''
def __init__(self , __magic_name__ , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self.feature_extractor
snake_case_ : List[str] = False
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*__magic_name__ , **__magic_name__ )
if "raw_speech" in kwargs:
warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
snake_case_ : Tuple = kwargs.pop('''raw_speech''' )
else:
snake_case_ : List[str] = kwargs.pop('''audio''' , __magic_name__ )
snake_case_ : Optional[Any] = kwargs.pop('''sampling_rate''' , __magic_name__ )
snake_case_ : Tuple = kwargs.pop('''text''' , __magic_name__ )
if len(__magic_name__ ) > 0:
snake_case_ : Tuple = args[0]
snake_case_ : List[str] = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
snake_case_ : Optional[Any] = self.feature_extractor(__magic_name__ , *__magic_name__ , sampling_rate=__magic_name__ , **__magic_name__ )
if text is not None:
snake_case_ : Tuple = self.tokenizer(__magic_name__ , **__magic_name__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case_ : Union[str, Any] = encodings['''input_ids''']
return inputs
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.tokenizer.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.tokenizer.decode(*__magic_name__ , **__magic_name__ )
@contextmanager
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your audio inputs, or in a separate call.''' )
snake_case_ : int = True
snake_case_ : int = self.tokenizer
yield
snake_case_ : List[Any] = self.feature_extractor
snake_case_ : str = False
| 60 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
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(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=1 ) -> List[Any]:
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
else:
return ".".join(path.split('''.''' )[:n_shave_prefix_segments] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=0 ) -> Tuple:
"""simple docstring"""
snake_case_ : Tuple = []
for old_item in old_list:
snake_case_ : str = old_item.replace('''in_layers.0''' , '''norm1''' )
snake_case_ : Dict = new_item.replace('''in_layers.2''' , '''conv1''' )
snake_case_ : Union[str, Any] = new_item.replace('''out_layers.0''' , '''norm2''' )
snake_case_ : str = new_item.replace('''out_layers.3''' , '''conv2''' )
snake_case_ : List[str] = new_item.replace('''emb_layers.1''' , '''time_emb_proj''' )
snake_case_ : List[Any] = new_item.replace('''skip_connection''' , '''conv_shortcut''' )
snake_case_ : Optional[Any] = shave_segments(_UpperCamelCase , n_shave_prefix_segments=_UpperCamelCase )
mapping.append({'''old''': old_item, '''new''': new_item} )
return mapping
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=0 ) -> Dict:
"""simple docstring"""
snake_case_ : List[Any] = []
for old_item in old_list:
snake_case_ : Tuple = old_item
snake_case_ : List[Any] = new_item.replace('''norm.weight''' , '''group_norm.weight''' )
snake_case_ : List[Any] = new_item.replace('''norm.bias''' , '''group_norm.bias''' )
snake_case_ : Dict = new_item.replace('''proj_out.weight''' , '''proj_attn.weight''' )
snake_case_ : Union[str, Any] = new_item.replace('''proj_out.bias''' , '''proj_attn.bias''' )
snake_case_ : List[Any] = shave_segments(_UpperCamelCase , n_shave_prefix_segments=_UpperCamelCase )
mapping.append({'''old''': old_item, '''new''': new_item} )
return mapping
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=None ) -> Any:
"""simple docstring"""
assert isinstance(_UpperCamelCase , _UpperCamelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
snake_case_ : List[Any] = old_checkpoint[path]
snake_case_ : Tuple = old_tensor.shape[0] // 3
snake_case_ : int = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
snake_case_ : Dict = old_tensor.shape[0] // config['''num_head_channels'''] // 3
snake_case_ : Optional[Any] = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
snake_case_ , snake_case_ , snake_case_ : Any = old_tensor.split(channels // num_heads , dim=1 )
snake_case_ : List[str] = query.reshape(_UpperCamelCase )
snake_case_ : Dict = key.reshape(_UpperCamelCase )
snake_case_ : List[str] = value.reshape(_UpperCamelCase )
for path in paths:
snake_case_ : Any = path['''new''']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
snake_case_ : List[Any] = new_path.replace('''middle_block.0''' , '''mid_block.resnets.0''' )
snake_case_ : Dict = new_path.replace('''middle_block.1''' , '''mid_block.attentions.0''' )
snake_case_ : str = new_path.replace('''middle_block.2''' , '''mid_block.resnets.1''' )
if additional_replacements is not None:
for replacement in additional_replacements:
snake_case_ : str = new_path.replace(replacement['''old'''] , replacement['''new'''] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
snake_case_ : Dict = old_checkpoint[path['''old''']][:, :, 0]
else:
snake_case_ : int = old_checkpoint[path['''old''']]
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : Optional[int] = {}
snake_case_ : Tuple = checkpoint['''time_embed.0.weight''']
snake_case_ : Any = checkpoint['''time_embed.0.bias''']
snake_case_ : Optional[Any] = checkpoint['''time_embed.2.weight''']
snake_case_ : List[Any] = checkpoint['''time_embed.2.bias''']
snake_case_ : List[str] = checkpoint['''input_blocks.0.0.weight''']
snake_case_ : Optional[int] = checkpoint['''input_blocks.0.0.bias''']
snake_case_ : Any = checkpoint['''out.0.weight''']
snake_case_ : Union[str, Any] = checkpoint['''out.0.bias''']
snake_case_ : Optional[Any] = checkpoint['''out.2.weight''']
snake_case_ : Union[str, Any] = checkpoint['''out.2.bias''']
# Retrieves the keys for the input blocks only
snake_case_ : Union[str, Any] = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''input_blocks''' in layer} )
snake_case_ : Optional[Any] = {
layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
for layer_id in range(_UpperCamelCase )
}
# Retrieves the keys for the middle blocks only
snake_case_ : List[Any] = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''middle_block''' in layer} )
snake_case_ : List[Any] = {
layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
for layer_id in range(_UpperCamelCase )
}
# Retrieves the keys for the output blocks only
snake_case_ : Any = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''output_blocks''' in layer} )
snake_case_ : int = {
layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
for layer_id in range(_UpperCamelCase )
}
for i in range(1 , _UpperCamelCase ):
snake_case_ : Optional[Any] = (i - 1) // (config['''num_res_blocks'''] + 1)
snake_case_ : List[Any] = (i - 1) % (config['''num_res_blocks'''] + 1)
snake_case_ : Dict = [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
snake_case_ : int = [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
snake_case_ : Optional[int] = checkpoint[
f'''input_blocks.{i}.0.op.weight'''
]
snake_case_ : Union[str, Any] = checkpoint[
f'''input_blocks.{i}.0.op.bias'''
]
continue
snake_case_ : Optional[Any] = renew_resnet_paths(_UpperCamelCase )
snake_case_ : Union[str, Any] = {'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
snake_case_ : Dict = {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
assign_to_checkpoint(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , additional_replacements=[meta_path, resnet_op] , config=_UpperCamelCase )
if len(_UpperCamelCase ):
snake_case_ : Union[str, Any] = renew_attention_paths(_UpperCamelCase )
snake_case_ : int = {
'''old''': f'''input_blocks.{i}.1''',
'''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
snake_case_ : Optional[Any] = {
f'''input_blocks.{i}.1.qkv.bias''': {
'''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
'''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
'''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
f'''input_blocks.{i}.1.qkv.weight''': {
'''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
'''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
'''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=_UpperCamelCase , config=_UpperCamelCase , )
snake_case_ : int = middle_blocks[0]
snake_case_ : List[str] = middle_blocks[1]
snake_case_ : Optional[int] = middle_blocks[2]
snake_case_ : List[str] = renew_resnet_paths(_UpperCamelCase )
assign_to_checkpoint(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , config=_UpperCamelCase )
snake_case_ : Tuple = renew_resnet_paths(_UpperCamelCase )
assign_to_checkpoint(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , config=_UpperCamelCase )
snake_case_ : Optional[Any] = renew_attention_paths(_UpperCamelCase )
snake_case_ : Optional[Any] = {
'''middle_block.1.qkv.bias''': {
'''key''': '''mid_block.attentions.0.key.bias''',
'''query''': '''mid_block.attentions.0.query.bias''',
'''value''': '''mid_block.attentions.0.value.bias''',
},
'''middle_block.1.qkv.weight''': {
'''key''': '''mid_block.attentions.0.key.weight''',
'''query''': '''mid_block.attentions.0.query.weight''',
'''value''': '''mid_block.attentions.0.value.weight''',
},
}
assign_to_checkpoint(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , attention_paths_to_split=_UpperCamelCase , config=_UpperCamelCase )
for i in range(_UpperCamelCase ):
snake_case_ : Tuple = i // (config['''num_res_blocks'''] + 1)
snake_case_ : List[str] = i % (config['''num_res_blocks'''] + 1)
snake_case_ : str = [shave_segments(_UpperCamelCase , 2 ) for name in output_blocks[i]]
snake_case_ : Optional[Any] = {}
for layer in output_block_layers:
snake_case_ , snake_case_ : Any = layer.split('''.''' )[0], shave_segments(_UpperCamelCase , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(_UpperCamelCase )
else:
snake_case_ : int = [layer_name]
if len(_UpperCamelCase ) > 1:
snake_case_ : Tuple = [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
snake_case_ : Any = [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
snake_case_ : List[Any] = renew_resnet_paths(_UpperCamelCase )
snake_case_ : Dict = renew_resnet_paths(_UpperCamelCase )
snake_case_ : Tuple = {'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
assign_to_checkpoint(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , additional_replacements=[meta_path] , config=_UpperCamelCase )
if ["conv.weight", "conv.bias"] in output_block_list.values():
snake_case_ : Optional[Any] = list(output_block_list.values() ).index(['''conv.weight''', '''conv.bias'''] )
snake_case_ : Optional[Any] = checkpoint[
f'''output_blocks.{i}.{index}.conv.weight'''
]
snake_case_ : Any = checkpoint[
f'''output_blocks.{i}.{index}.conv.bias'''
]
# Clear attentions as they have been attributed above.
if len(_UpperCamelCase ) == 2:
snake_case_ : str = []
if len(_UpperCamelCase ):
snake_case_ : Tuple = renew_attention_paths(_UpperCamelCase )
snake_case_ : str = {
'''old''': f'''output_blocks.{i}.1''',
'''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
snake_case_ : List[str] = {
f'''output_blocks.{i}.1.qkv.bias''': {
'''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
'''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
'''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
f'''output_blocks.{i}.1.qkv.weight''': {
'''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
'''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
'''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions ) else None , config=_UpperCamelCase , )
else:
snake_case_ : Union[str, Any] = renew_resnet_paths(_UpperCamelCase , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
snake_case_ : Dict = '''.'''.join(['''output_blocks''', str(_UpperCamelCase ), path['''old''']] )
snake_case_ : Optional[Any] = '''.'''.join(['''up_blocks''', str(_UpperCamelCase ), '''resnets''', str(_UpperCamelCase ), path['''new''']] )
snake_case_ : List[Any] = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the architecture.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
lowerCAmelCase_ = parser.parse_args()
lowerCAmelCase_ = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
lowerCAmelCase_ = json.loads(f.read())
lowerCAmelCase_ = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
lowerCAmelCase_ = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
lowerCAmelCase_ = DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
lowerCAmelCase_ = VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
lowerCAmelCase_ = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 60 |
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 ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = 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 lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : 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=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = 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=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
| 60 | 1 |
# Copyright 2022 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
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def lowerCamelCase_ ( _UpperCamelCase=None ) -> Optional[int]:
"""simple docstring"""
if subparsers is not None:
snake_case_ : Union[str, Any] = subparsers.add_parser('''env''' )
else:
snake_case_ : Optional[int] = argparse.ArgumentParser('''Accelerate env command''' )
parser.add_argument(
'''--config_file''' , default=_UpperCamelCase , help='''The config file to use for the default values in the launching script.''' )
if subparsers is not None:
parser.set_defaults(func=_UpperCamelCase )
return parser
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = torch.__version__
snake_case_ : List[Any] = torch.cuda.is_available()
snake_case_ : str = is_xpu_available()
snake_case_ : List[Any] = is_npu_available()
snake_case_ : List[Any] = '''Not found'''
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_UpperCamelCase ):
snake_case_ : Any = load_config_from_file(args.config_file ).to_dict()
snake_case_ : Dict = {
'''`Accelerate` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Numpy version''': np.__version__,
'''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
'''PyTorch XPU available''': str(_UpperCamelCase ),
'''PyTorch NPU available''': str(_UpperCamelCase ),
'''System RAM''': f'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
}
if pt_cuda_available:
snake_case_ : List[str] = torch.cuda.get_device_name()
print('''\nCopy-and-paste the text below in your GitHub issue\n''' )
print('''\n'''.join([f'''- {prop}: {val}''' for prop, val in info.items()] ) )
print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' )
snake_case_ : Union[str, Any] = (
'''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(_UpperCamelCase , _UpperCamelCase )
else f'''\t{accelerate_config}'''
)
print(_UpperCamelCase )
snake_case_ : Dict = accelerate_config
return info
def lowerCamelCase_ ( ) -> int:
"""simple docstring"""
snake_case_ : List[Any] = env_command_parser()
snake_case_ : int = parser.parse_args()
env_command(_UpperCamelCase )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 60 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''AlbertTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''AlbertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AlbertForMaskedLM''',
'''AlbertForMultipleChoice''',
'''AlbertForPreTraining''',
'''AlbertForQuestionAnswering''',
'''AlbertForSequenceClassification''',
'''AlbertForTokenClassification''',
'''AlbertModel''',
'''AlbertPreTrainedModel''',
'''load_tf_weights_in_albert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFAlbertForMaskedLM''',
'''TFAlbertForMultipleChoice''',
'''TFAlbertForPreTraining''',
'''TFAlbertForQuestionAnswering''',
'''TFAlbertForSequenceClassification''',
'''TFAlbertForTokenClassification''',
'''TFAlbertMainLayer''',
'''TFAlbertModel''',
'''TFAlbertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''FlaxAlbertForMaskedLM''',
'''FlaxAlbertForMultipleChoice''',
'''FlaxAlbertForPreTraining''',
'''FlaxAlbertForQuestionAnswering''',
'''FlaxAlbertForSequenceClassification''',
'''FlaxAlbertForTokenClassification''',
'''FlaxAlbertModel''',
'''FlaxAlbertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 1 |
from scipy.stats import pearsonr
import datasets
lowerCAmelCase_ = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''
lowerCAmelCase_ = '''
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results[\'pearsonr\'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
[\'p-value\', \'pearsonr\']
>>> print(round(results[\'pearsonr\'], 2))
-0.74
>>> print(round(results[\'p-value\'], 2))
0.15
'''
lowerCAmelCase_ = '''
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'''] , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=False ) -> Union[str, Any]:
'''simple docstring'''
if return_pvalue:
snake_case_ : Dict = pearsonr(__magic_name__ , __magic_name__ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(__magic_name__ , __magic_name__ )[0] )}
| 60 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 1 |
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_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = ['''pixel_values''']
def __init__(self , __magic_name__ = True , __magic_name__ = None , __magic_name__ = PILImageResampling.BILINEAR , __magic_name__ = True , __magic_name__ = 1 / 255 , __magic_name__ = True , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> None:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Dict = size if size is not None else {'''shortest_edge''': 224}
snake_case_ : List[Any] = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
snake_case_ : Optional[Any] = crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
snake_case_ : Union[str, Any] = get_size_dict(__magic_name__ , param_name='''crop_size''' )
snake_case_ : List[str] = do_resize
snake_case_ : List[Any] = size
snake_case_ : List[str] = resample
snake_case_ : int = do_rescale
snake_case_ : Tuple = rescale_factor
snake_case_ : Any = do_center_crop
snake_case_ : Union[str, Any] = crop_size
snake_case_ : int = do_flip_channel_order
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ = PIL.Image.BILINEAR , __magic_name__ = None , **__magic_name__ , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Optional[Any] = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''' )
snake_case_ : Dict = get_resize_output_image_size(__magic_name__ , size=size['''shortest_edge'''] , default_to_square=__magic_name__ )
return resize(__magic_name__ , size=__magic_name__ , resample=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ = None , **__magic_name__ , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Optional[Any] = get_size_dict(__magic_name__ )
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(__magic_name__ , size=(size['''height'''], size['''width''']) , data_format=__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ = None , **__magic_name__ , ) -> Optional[int]:
'''simple docstring'''
return rescale(__magic_name__ , scale=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> np.ndarray:
'''simple docstring'''
return flip_channel_order(__magic_name__ , data_format=__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = ChannelDimension.FIRST , **__magic_name__ , ) -> PIL.Image.Image:
'''simple docstring'''
snake_case_ : Dict = do_resize if do_resize is not None else self.do_resize
snake_case_ : Any = resample if resample is not None else self.resample
snake_case_ : Any = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ : str = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ : List[str] = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
snake_case_ : Any = size if size is not None else self.size
snake_case_ : Optional[int] = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
snake_case_ : Tuple = crop_size if crop_size is not None else self.crop_size
snake_case_ : Tuple = get_size_dict(__magic_name__ , param_name='''crop_size''' )
snake_case_ : Tuple = make_list_of_images(__magic_name__ )
if not valid_images(__magic_name__ ):
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.
snake_case_ : Tuple = [to_numpy_array(__magic_name__ ) for image in images]
if do_resize:
snake_case_ : List[str] = [self.resize(image=__magic_name__ , size=__magic_name__ , resample=__magic_name__ ) for image in images]
if do_center_crop:
snake_case_ : Dict = [self.center_crop(image=__magic_name__ , size=__magic_name__ ) for image in images]
if do_rescale:
snake_case_ : str = [self.rescale(image=__magic_name__ , scale=__magic_name__ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
snake_case_ : str = [self.flip_channel_order(image=__magic_name__ ) for image in images]
snake_case_ : Tuple = [to_channel_dimension_format(__magic_name__ , __magic_name__ ) for image in images]
snake_case_ : Optional[int] = {'''pixel_values''': images}
return BatchFeature(data=__magic_name__ , tensor_type=__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(__magic_name__ ):
snake_case_ : List[str] = target_sizes.numpy()
snake_case_ : Any = []
for idx in range(len(__magic_name__ ) ):
snake_case_ : Any = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=__magic_name__ )
snake_case_ : Union[str, Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__magic_name__ )
else:
snake_case_ : str = logits.argmax(dim=1 )
snake_case_ : int = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 60 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 1 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = '''conditional_detr'''
lowerCamelCase_ : List[Any] = ['''past_key_values''']
lowerCamelCase_ : Union[str, Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__(self , __magic_name__=True , __magic_name__=None , __magic_name__=3 , __magic_name__=300 , __magic_name__=6 , __magic_name__=2048 , __magic_name__=8 , __magic_name__=6 , __magic_name__=2048 , __magic_name__=8 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=True , __magic_name__="relu" , __magic_name__=256 , __magic_name__=0.1 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=False , __magic_name__="sine" , __magic_name__="resnet50" , __magic_name__=True , __magic_name__=False , __magic_name__=2 , __magic_name__=5 , __magic_name__=2 , __magic_name__=1 , __magic_name__=1 , __magic_name__=2 , __magic_name__=5 , __magic_name__=2 , __magic_name__=0.25 , **__magic_name__ , ) -> List[Any]:
'''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.''' )
snake_case_ : Union[str, Any] = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Union[str, Any] = backbone_config.get('''model_type''' )
snake_case_ : str = CONFIG_MAPPING[backbone_model_type]
snake_case_ : List[Any] = config_class.from_dict(__magic_name__ )
snake_case_ : Tuple = use_timm_backbone
snake_case_ : Tuple = backbone_config
snake_case_ : Optional[int] = num_channels
snake_case_ : Dict = num_queries
snake_case_ : List[str] = d_model
snake_case_ : List[Any] = encoder_ffn_dim
snake_case_ : List[str] = encoder_layers
snake_case_ : Dict = encoder_attention_heads
snake_case_ : str = decoder_ffn_dim
snake_case_ : Tuple = decoder_layers
snake_case_ : str = decoder_attention_heads
snake_case_ : Dict = dropout
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = activation_dropout
snake_case_ : Union[str, Any] = activation_function
snake_case_ : Any = init_std
snake_case_ : Any = init_xavier_std
snake_case_ : List[str] = encoder_layerdrop
snake_case_ : Tuple = decoder_layerdrop
snake_case_ : Optional[Any] = encoder_layers
snake_case_ : List[str] = auxiliary_loss
snake_case_ : Any = position_embedding_type
snake_case_ : str = backbone
snake_case_ : List[Any] = use_pretrained_backbone
snake_case_ : List[str] = dilation
# Hungarian matcher
snake_case_ : Any = class_cost
snake_case_ : Optional[int] = bbox_cost
snake_case_ : List[Any] = giou_cost
# Loss coefficients
snake_case_ : Optional[Any] = mask_loss_coefficient
snake_case_ : Optional[Any] = dice_loss_coefficient
snake_case_ : Any = cls_loss_coefficient
snake_case_ : str = bbox_loss_coefficient
snake_case_ : Optional[int] = giou_loss_coefficient
snake_case_ : Tuple = focal_alpha
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return self.d_model
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : int = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
snake_case_ : str = self.backbone_config.to_dict()
snake_case_ : Optional[int] = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[str] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 12
| 60 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''' )
print('''Labels: {labels}''' )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 1 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 1 |
import argparse
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_dummies.py
lowerCAmelCase_ = '''src/diffusers'''
# Matches is_xxx_available()
lowerCAmelCase_ = re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
lowerCAmelCase_ = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
lowerCAmelCase_ = '''
{0} = None
'''
lowerCAmelCase_ = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
'''
lowerCAmelCase_ = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
def lowerCamelCase_ ( _UpperCamelCase ) -> List[str]:
"""simple docstring"""
snake_case_ : Optional[Any] = _re_backend.findall(_UpperCamelCase )
if len(_UpperCamelCase ) == 0:
return None
return "_and_".join(_UpperCamelCase )
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
with open(os.path.join(_UpperCamelCase , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case_ : Any = f.readlines()
# Get to the point we do the actual imports for type checking
snake_case_ : List[Any] = 0
snake_case_ : Tuple = {}
# Go through the end of the file
while line_index < len(_UpperCamelCase ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
snake_case_ : int = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
snake_case_ : List[str] = []
# Until we unindent, add backend objects to the list
while line_index < len(_UpperCamelCase ) and len(lines[line_index] ) > 1:
snake_case_ : Optional[Any] = lines[line_index]
snake_case_ : List[str] = _re_single_line_import.search(_UpperCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(_UpperCamelCase ) > 0:
snake_case_ : Union[str, Any] = objects
else:
line_index += 1
return backend_specific_objects
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
if name.isupper():
return DUMMY_CONSTANT.format(_UpperCamelCase )
elif name.islower():
return DUMMY_FUNCTION.format(_UpperCamelCase , _UpperCamelCase )
else:
return DUMMY_CLASS.format(_UpperCamelCase , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase=None ) -> List[str]:
"""simple docstring"""
if backend_specific_objects is None:
snake_case_ : Optional[int] = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
snake_case_ : List[str] = {}
for backend, objects in backend_specific_objects.items():
snake_case_ : List[str] = '''[''' + ''', '''.join(f'''"{b}"''' for b in backend.split('''_and_''' ) ) + ''']'''
snake_case_ : str = '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(_UpperCamelCase , _UpperCamelCase ) for o in objects] )
snake_case_ : Any = dummy_file
return dummy_files
def lowerCamelCase_ ( _UpperCamelCase=False ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Any = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
snake_case_ : Optional[Any] = {'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
snake_case_ : Any = os.path.join(_UpperCamelCase , '''utils''' )
snake_case_ : List[str] = {
backend: os.path.join(_UpperCamelCase , f'''dummy_{short_names.get(_UpperCamelCase , _UpperCamelCase )}_objects.py''' )
for backend in dummy_files.keys()
}
snake_case_ : Union[str, Any] = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(_UpperCamelCase ):
with open(_UpperCamelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case_ : Union[str, Any] = f.read()
else:
snake_case_ : Optional[Any] = ''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f'''Updating diffusers.utils.dummy_{short_names.get(_UpperCamelCase , _UpperCamelCase )}_objects.py as the main '''
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
f'''diffusers.utils.dummy_{short_names.get(_UpperCamelCase , _UpperCamelCase )}_objects.py. Run `make fix-copies` '''
'''to fix this.''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCAmelCase_ = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 60 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
lowerCAmelCase_ = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
lowerCAmelCase_ = 1_2_8_0_2_2
lowerCAmelCase_ = 1_2_8_0_2_8
@require_sentencepiece
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : Any = MaMaaaTokenizer
lowerCamelCase_ : Dict = False
lowerCamelCase_ : Dict = False
lowerCamelCase_ : Optional[Any] = True
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
super().setUp()
snake_case_ : str = ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
snake_case_ : Dict = dict(zip(__magic_name__ , range(len(__magic_name__ ) ) ) )
snake_case_ : List[str] = Path(self.tmpdirname )
save_json(__magic_name__ , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(__magic_name__ , save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
snake_case_ : Dict = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase (self , **__magic_name__ ) -> str:
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Tuple:
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = '''</s>'''
snake_case_ : List[str] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__ ) , __magic_name__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__ ) , __magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Any = self.get_tokenizer()
snake_case_ : List[Any] = list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''</s>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''<s>''' )
self.assertEqual(len(__magic_name__ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('''Skip this test while all models are still to be uploaded.''' )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.get_tokenizer()
snake_case_ : str = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__magic_name__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__magic_name__ ) , [2, 3, 4, 5, 6] , )
snake_case_ : Tuple = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(__magic_name__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
snake_case_ : Optional[int] = tokenizer.convert_tokens_to_string(__magic_name__ )
self.assertEqual(__magic_name__ , '''This is a test''' )
@slow
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = {'''input_ids''': [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 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], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 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, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=__magic_name__ , model_name='''facebook/m2m100_418M''' , revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''facebook/m2m100_418M'''
lowerCamelCase_ : List[Any] = [
'''In my opinion, there are two levels of response from the French government.''',
'''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
]
lowerCamelCase_ : int = [
'''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
'''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
]
# fmt: off
lowerCamelCase_ : Optional[int] = [EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]
@classmethod
def lowerCamelCase (cls ) -> Optional[int]:
'''simple docstring'''
snake_case_ : MaMaaaTokenizer = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en''' , tgt_lang='''fr''' )
snake_case_ : Union[str, Any] = 1
return cls
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id('''ar''' ) , 12_8006 )
self.assertEqual(self.tokenizer.get_lang_id('''en''' ) , 12_8022 )
self.assertEqual(self.tokenizer.get_lang_id('''ro''' ) , 12_8076 )
self.assertEqual(self.tokenizer.get_lang_id('''mr''' ) , 12_8063 )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.tokenizer.get_vocab()
self.assertEqual(len(__magic_name__ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['''<unk>'''] , 3 )
self.assertIn(self.tokenizer.get_lang_token('''en''' ) , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = '''en'''
snake_case_ : Dict = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
self.assertIn(__magic_name__ , self.tokenizer.all_special_ids )
# fmt: off
snake_case_ : Dict = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
# fmt: on
snake_case_ : Union[str, Any] = self.tokenizer.decode(__magic_name__ , skip_special_tokens=__magic_name__ )
snake_case_ : Any = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__magic_name__ )
self.assertEqual(__magic_name__ , __magic_name__ )
self.assertNotIn(self.tokenizer.eos_token , __magic_name__ )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Tuple = tempfile.mkdtemp()
snake_case_ : List[Any] = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(__magic_name__ )
snake_case_ : Union[str, Any] = MaMaaaTokenizer.from_pretrained(__magic_name__ )
self.assertDictEqual(new_tok.lang_token_to_id , __magic_name__ )
@require_torch
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : int = '''en'''
snake_case_ : Union[str, Any] = '''fr'''
snake_case_ : Any = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__magic_name__ , return_tensors='''pt''' )
snake_case_ : Optional[Any] = shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
snake_case_ : Union[str, Any] = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = '''mr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
snake_case_ : Optional[int] = '''zh'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = '''mr'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
snake_case_ : Optional[int] = '''zh'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = self.tokenizer._build_translation_inputs('''A test''' , return_tensors='''pt''' , src_lang='''en''' , tgt_lang='''ar''' )
self.assertEqual(
nested_simplify(__magic_name__ ) , {
# en_XX, A, test, EOS
'''input_ids''': [[12_8022, 58, 4183, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 12_8006,
} , )
| 60 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 1 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=2 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=3 , __magic_name__=None , __magic_name__=2 , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = parent
snake_case_ : List[str] = batch_size
snake_case_ : List[str] = image_size
snake_case_ : Tuple = patch_size
snake_case_ : List[str] = num_channels
snake_case_ : List[Any] = is_training
snake_case_ : Optional[Any] = use_labels
snake_case_ : Union[str, Any] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Tuple = hidden_act
snake_case_ : Tuple = hidden_dropout_prob
snake_case_ : List[str] = attention_probs_dropout_prob
snake_case_ : Tuple = type_sequence_label_size
snake_case_ : Tuple = initializer_range
snake_case_ : Optional[Any] = scope
snake_case_ : List[Any] = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
snake_case_ : Optional[Any] = (image_size // patch_size) ** 2
snake_case_ : List[Any] = num_patches + 2
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = None
if self.use_labels:
snake_case_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__magic_name__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = TFDeiTModel(config=__magic_name__ )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = TFDeiTForMaskedImageModeling(config=__magic_name__ )
snake_case_ : int = model(__magic_name__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case_ : Dict = 1
snake_case_ : int = TFDeiTForMaskedImageModeling(__magic_name__ )
snake_case_ : Any = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Optional[int] = model(__magic_name__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> int:
'''simple docstring'''
snake_case_ : List[str] = self.type_sequence_label_size
snake_case_ : Any = TFDeiTForImageClassification(__magic_name__ )
snake_case_ : Dict = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case_ : Dict = 1
snake_case_ : Tuple = TFDeiTForImageClassification(__magic_name__ )
snake_case_ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Optional[int] = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : str = config_and_inputs
snake_case_ : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : Optional[Any] = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
lowerCamelCase_ : Tuple = (
{
'''feature-extraction''': TFDeiTModel,
'''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Union[str, Any] = False
lowerCamelCase_ : Tuple = False
lowerCamelCase_ : Dict = False
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = TFDeiTModelTester(self )
snake_case_ : Any = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''DeiT does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ , snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Optional[Any] = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case_ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , tf.keras.layers.Dense ) )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ , snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : int = model_class(__magic_name__ )
snake_case_ : int = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Dict = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=False ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = super()._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Tuple = TFDeiTModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
if is_vision_available()
else None
)
@slow
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
snake_case_ : Optional[Any] = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : Optional[Any] = image_processor(images=__magic_name__ , return_tensors='''tf''' )
# forward pass
snake_case_ : Optional[Any] = model(**__magic_name__ )
# verify the logits
snake_case_ : List[str] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : str = tf.constant([-1.0_266, 0.1_912, -1.2_861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 1 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : str = [1]
snake_case_ , snake_case_ , snake_case_ : Tuple = 0, 0, 0
snake_case_ : Optional[int] = ugly_nums[ia] * 2
snake_case_ : List[str] = ugly_nums[ia] * 3
snake_case_ : Union[str, Any] = ugly_nums[ia] * 5
for _ in range(1 , _UpperCamelCase ):
snake_case_ : Dict = min(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
ugly_nums.append(_UpperCamelCase )
if next_num == next_a:
ia += 1
snake_case_ : Optional[Any] = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
snake_case_ : List[str] = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
snake_case_ : Optional[Any] = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(F'''{ugly_numbers(2_0_0) = }''')
| 60 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase = 1_000 ) -> int:
"""simple docstring"""
snake_case_ : Any = -1
snake_case_ : Optional[int] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
snake_case_ : str = (n * n - 2 * a * n) // (2 * n - 2 * a)
snake_case_ : Dict = n - a - b
if c * c == (a * a + b * b):
snake_case_ : Any = a * b * c
if candidate >= product:
snake_case_ : int = candidate
return product
if __name__ == "__main__":
print(F'''{solution() = }''')
| 60 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 60 | 1 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = {}
snake_case_ : Dict = tokenizer(example['''content'''] , truncation=_UpperCamelCase )['''input_ids''']
snake_case_ : str = len(example['''content'''] ) / len(output['''input_ids'''] )
return output
lowerCAmelCase_ = HfArgumentParser(PretokenizationArguments)
lowerCAmelCase_ = parser.parse_args()
if args.num_workers is None:
lowerCAmelCase_ = multiprocessing.cpu_count()
lowerCAmelCase_ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
lowerCAmelCase_ = time.time()
lowerCAmelCase_ = load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')
lowerCAmelCase_ = time.time()
lowerCAmelCase_ = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
'''repo_name''',
'''path''',
'''copies''',
'''size''',
'''content''',
'''license''',
'''hash''',
'''line_mean''',
'''line_max''',
'''alpha_frac''',
'''autogenerated''',
],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')
lowerCAmelCase_ = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')
| 60 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=32 , __magic_name__=2 , __magic_name__=3 , __magic_name__=16 , __magic_name__=[32, 64, 128] , __magic_name__=[1, 2, 1] , __magic_name__=[2, 2, 4] , __magic_name__=2 , __magic_name__=2.0 , __magic_name__=True , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.1 , __magic_name__="gelu" , __magic_name__=False , __magic_name__=True , __magic_name__=0.02 , __magic_name__=1e-5 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=10 , __magic_name__=8 , __magic_name__=["stage1", "stage2"] , __magic_name__=[1, 2] , ) -> str:
'''simple docstring'''
snake_case_ : Dict = parent
snake_case_ : int = batch_size
snake_case_ : Union[str, Any] = image_size
snake_case_ : List[str] = patch_size
snake_case_ : List[Any] = num_channels
snake_case_ : Tuple = embed_dim
snake_case_ : List[str] = hidden_sizes
snake_case_ : Optional[Any] = depths
snake_case_ : Optional[Any] = num_heads
snake_case_ : List[Any] = window_size
snake_case_ : Optional[int] = mlp_ratio
snake_case_ : Dict = qkv_bias
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[str] = attention_probs_dropout_prob
snake_case_ : str = drop_path_rate
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = use_absolute_embeddings
snake_case_ : str = patch_norm
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : Dict = initializer_range
snake_case_ : List[Any] = is_training
snake_case_ : List[Any] = scope
snake_case_ : Any = use_labels
snake_case_ : int = type_sequence_label_size
snake_case_ : List[Any] = encoder_stride
snake_case_ : List[Any] = out_features
snake_case_ : List[str] = out_indices
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : str = FocalNetModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Union[str, Any] = model(__magic_name__ )
snake_case_ : Optional[int] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case_ : Union[str, Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = FocalNetBackbone(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[Any] = model(__magic_name__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
snake_case_ : Tuple = None
snake_case_ : Union[str, Any] = FocalNetBackbone(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Tuple = FocalNetForMaskedImageModeling(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case_ : int = 1
snake_case_ : Tuple = FocalNetForMaskedImageModeling(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.type_sequence_label_size
snake_case_ : int = FocalNetForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Optional[Any] = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case_ : Any = 1
snake_case_ : Optional[int] = FocalNetForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : str = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : Optional[Any] = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase_ : Any = (
{'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : List[Any] = False
lowerCamelCase_ : Any = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Tuple = False
lowerCamelCase_ : Tuple = False
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = FocalNetModelTester(self )
snake_case_ : Any = ConfigTester(self , config_class=__magic_name__ , embed_dim=37 , has_text_modality=__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
return
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__magic_name__ )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@unittest.skip(reason='''FocalNet does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='''FocalNet does not use feedforward chunking''' )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
snake_case_ : Optional[Any] = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ , snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
snake_case_ : Optional[int] = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : List[str] = [*signature.parameters.keys()]
snake_case_ : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> Dict:
'''simple docstring'''
snake_case_ : Tuple = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
snake_case_ : str = outputs.hidden_states
snake_case_ : str = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
# FocalNet has a different seq_length
snake_case_ : List[Any] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ : str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
snake_case_ : Tuple = outputs.reshaped_hidden_states
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[str] = reshaped_hidden_states[0].shape
snake_case_ : Dict = (
reshaped_hidden_states[0].view(__magic_name__ , __magic_name__ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ , snake_case_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : int = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
snake_case_ : int = True
self.check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ : List[str] = True
self.check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Tuple = 3
snake_case_ : Optional[int] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case_ : Union[str, Any] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ : str = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case_ : Union[str, Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
snake_case_ : List[Any] = True
self.check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ : List[Any] = True
self.check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ , (padded_height, padded_width) )
@slow
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = FocalNetModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ , snake_case_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Tuple = _config_zero_init(__magic_name__ )
for model_class in self.all_model_classes:
snake_case_ : List[str] = model_class(config=__magic_name__ )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@require_vision
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Tuple = FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(__magic_name__ )
snake_case_ : Any = self.default_image_processor
snake_case_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
snake_case_ : Any = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : str = model(**__magic_name__ )
# verify the logits
snake_case_ : int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : Any = torch.tensor([0.2_166, -0.4_368, 0.2_191] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 281 )
@require_torch
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : Any = (FocalNetBackbone,) if is_torch_available() else ()
lowerCamelCase_ : Tuple = FocalNetConfig
lowerCamelCase_ : Any = False
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = FocalNetModelTester(self )
| 60 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase_ = '''src/diffusers'''
lowerCAmelCase_ = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
lowerCAmelCase_ = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCAmelCase_ = spec.loader.load_module()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return line.startswith(_UpperCamelCase ) or len(_UpperCamelCase ) <= 1 or re.search(R'''^\s*\)(\s*->.*:|:)\s*$''' , _UpperCamelCase ) is not None
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = object_name.split('''.''' )
snake_case_ : Optional[int] = 0
# First let's find the module where our object lives.
snake_case_ : str = parts[i]
while i < len(_UpperCamelCase ) and not os.path.isfile(os.path.join(_UpperCamelCase , f'''{module}.py''' ) ):
i += 1
if i < len(_UpperCamelCase ):
snake_case_ : Optional[Any] = os.path.join(_UpperCamelCase , parts[i] )
if i >= len(_UpperCamelCase ):
raise ValueError(f'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''' )
with open(os.path.join(_UpperCamelCase , f'''{module}.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case_ : Any = f.readlines()
# Now let's find the class / func in the code!
snake_case_ : Optional[int] = ''''''
snake_case_ : Any = 0
for name in parts[i + 1 :]:
while (
line_index < len(_UpperCamelCase ) and re.search(Rf'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(_UpperCamelCase ):
raise ValueError(f''' {object_name} does not match any function or class in {module}.''' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
snake_case_ : Optional[Any] = line_index
while line_index < len(_UpperCamelCase ) and _should_continue(lines[line_index] , _UpperCamelCase ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
snake_case_ : Any = lines[start_index:line_index]
return "".join(_UpperCamelCase )
lowerCAmelCase_ = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
lowerCAmelCase_ = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
lowerCAmelCase_ = re.compile(r'''<FILL\s+[^>]*>''')
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : Tuple = code.split('''\n''' )
snake_case_ : Optional[int] = 0
while idx < len(_UpperCamelCase ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(_UpperCamelCase ):
return re.search(R'''^(\s*)\S''' , lines[idx] ).groups()[0]
return ""
def lowerCamelCase_ ( _UpperCamelCase ) -> List[str]:
"""simple docstring"""
snake_case_ : List[str] = len(get_indent(_UpperCamelCase ) ) > 0
if has_indent:
snake_case_ : Optional[Any] = f'''class Bla:\n{code}'''
snake_case_ : Union[str, Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=_UpperCamelCase )
snake_case_ : str = black.format_str(_UpperCamelCase , mode=_UpperCamelCase )
snake_case_ , snake_case_ : Optional[int] = style_docstrings_in_code(_UpperCamelCase )
return result[len('''class Bla:\n''' ) :] if has_indent else result
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=False ) -> Tuple:
"""simple docstring"""
with open(_UpperCamelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case_ : int = f.readlines()
snake_case_ : Optional[Any] = []
snake_case_ : Any = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(_UpperCamelCase ):
snake_case_ : List[Any] = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
snake_case_ , snake_case_ , snake_case_ : Dict = search.groups()
snake_case_ : str = find_code_in_diffusers(_UpperCamelCase )
snake_case_ : Tuple = get_indent(_UpperCamelCase )
snake_case_ : List[Any] = line_index + 1 if indent == theoretical_indent else line_index + 2
snake_case_ : Optional[Any] = theoretical_indent
snake_case_ : int = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
snake_case_ : Optional[int] = True
while line_index < len(_UpperCamelCase ) and should_continue:
line_index += 1
if line_index >= len(_UpperCamelCase ):
break
snake_case_ : Union[str, Any] = lines[line_index]
snake_case_ : Union[str, Any] = _should_continue(_UpperCamelCase , _UpperCamelCase ) and re.search(f'''^{indent}# End copy''' , _UpperCamelCase ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
snake_case_ : Union[str, Any] = lines[start_index:line_index]
snake_case_ : List[str] = ''''''.join(_UpperCamelCase )
# Remove any nested `Copied from` comments to avoid circular copies
snake_case_ : List[str] = [line for line in theoretical_code.split('''\n''' ) if _re_copy_warning.search(_UpperCamelCase ) is None]
snake_case_ : str = '''\n'''.join(_UpperCamelCase )
# Before comparing, use the `replace_pattern` on the original code.
if len(_UpperCamelCase ) > 0:
snake_case_ : Union[str, Any] = replace_pattern.replace('''with''' , '''''' ).split(''',''' )
snake_case_ : List[Any] = [_re_replace_pattern.search(_UpperCamelCase ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
snake_case_ , snake_case_ , snake_case_ : Optional[int] = pattern.groups()
snake_case_ : Dict = re.sub(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if option.strip() == "all-casing":
snake_case_ : List[Any] = re.sub(obja.lower() , obja.lower() , _UpperCamelCase )
snake_case_ : Optional[Any] = re.sub(obja.upper() , obja.upper() , _UpperCamelCase )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
snake_case_ : str = blackify(lines[start_index - 1] + theoretical_code )
snake_case_ : Any = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
snake_case_ : int = lines[:start_index] + [theoretical_code] + lines[line_index:]
snake_case_ : Any = start_index + 1
if overwrite and len(_UpperCamelCase ) > 0:
# Warn the user a file has been modified.
print(f'''Detected changes, rewriting {filename}.''' )
with open(_UpperCamelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(_UpperCamelCase )
return diffs
def lowerCamelCase_ ( _UpperCamelCase = False ) -> str:
"""simple docstring"""
snake_case_ : str = glob.glob(os.path.join(_UpperCamelCase , '''**/*.py''' ) , recursive=_UpperCamelCase )
snake_case_ : Optional[Any] = []
for filename in all_files:
snake_case_ : Optional[Any] = is_copy_consistent(_UpperCamelCase , _UpperCamelCase )
diffs += [f'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(_UpperCamelCase ) > 0:
snake_case_ : Optional[Any] = '''\n'''.join(_UpperCamelCase )
raise Exception(
'''Found the following copy inconsistencies:\n'''
+ diff
+ '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCAmelCase_ = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 60 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 1 |
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase_ = list[list[float | int]]
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Matrix:
"""simple docstring"""
snake_case_ : int = len(_UpperCamelCase )
snake_case_ : Matrix = [[0 for _ in range(size + 1 )] for _ in range(_UpperCamelCase )]
snake_case_ : int
snake_case_ : int
snake_case_ : int
snake_case_ : int
snake_case_ : int
snake_case_ : float
for row in range(_UpperCamelCase ):
for col in range(_UpperCamelCase ):
snake_case_ : int = matrix[row][col]
snake_case_ : int = vector[row][0]
snake_case_ : str = 0
snake_case_ : List[str] = 0
while row < size and col < size:
# pivoting
snake_case_ : Any = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCamelCase , _UpperCamelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
snake_case_ , snake_case_ : int = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCamelCase ):
snake_case_ : Tuple = augmented[rowa][col] / augmented[row][col]
snake_case_ : str = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _UpperCamelCase ):
for row in range(_UpperCamelCase ):
snake_case_ : Tuple = augmented[row][col] / augmented[col][col]
for cola in range(_UpperCamelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(_UpperCamelCase )
]
def lowerCamelCase_ ( _UpperCamelCase ) -> Callable[[int], int]:
"""simple docstring"""
snake_case_ : int = len(_UpperCamelCase )
snake_case_ : Matrix = [[0 for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase )]
snake_case_ : Matrix = [[0] for _ in range(_UpperCamelCase )]
snake_case_ : Matrix
snake_case_ : int
snake_case_ : int
snake_case_ : int
for x_val, y_val in enumerate(_UpperCamelCase ):
for col in range(_UpperCamelCase ):
snake_case_ : str = (x_val + 1) ** (size - col - 1)
snake_case_ : Optional[int] = y_val
snake_case_ : Any = solve(_UpperCamelCase , _UpperCamelCase )
def interpolated_func(_UpperCamelCase ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_UpperCamelCase ) )
return interpolated_func
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def lowerCamelCase_ ( _UpperCamelCase = question_function , _UpperCamelCase = 10 ) -> int:
"""simple docstring"""
snake_case_ : list[int] = [func(_UpperCamelCase ) for x_val in range(1 , order + 1 )]
snake_case_ : list[Callable[[int], int]] = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
snake_case_ : int = 0
snake_case_ : Callable[[int], int]
snake_case_ : int
for poly in polynomials:
snake_case_ : List[Any] = 1
while func(_UpperCamelCase ) == poly(_UpperCamelCase ):
x_val += 1
ret += poly(_UpperCamelCase )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 60 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 1 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
import math
def lowerCamelCase_ ( _UpperCamelCase ) -> bool:
"""simple docstring"""
assert isinstance(_UpperCamelCase , _UpperCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
snake_case_ : List[Any] = range(3 , int(math.sqrt(_UpperCamelCase ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=1 , **_UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : Optional[int] = factor * value
snake_case_ : Optional[int] = value
while not is_prime(_UpperCamelCase ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **_UpperCamelCase )
return value
| 60 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
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(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 1 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
re.sub('''<n>''' , '''''' , _UpperCamelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_UpperCamelCase ) )
| 60 |
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 ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = 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 lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : 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=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = 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=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
| 60 | 1 |
lowerCAmelCase_ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowerCAmelCase_ = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase_ = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 60 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 1 |
from ....utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ , __magic_name__=None , __magic_name__=2048 ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = config.__dict__
snake_case_ : Dict = modal_hidden_size
if num_labels:
snake_case_ : Optional[Any] = num_labels
| 60 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : List[Any] = {}
snake_case_ : Optional[int] = job['''started_at''']
snake_case_ : str = job['''completed_at''']
snake_case_ : Dict = date_parser.parse(_UpperCamelCase )
snake_case_ : Tuple = date_parser.parse(_UpperCamelCase )
snake_case_ : str = round((end_datetime - start_datetime).total_seconds() / 60.0 )
snake_case_ : List[str] = start
snake_case_ : Optional[int] = end
snake_case_ : List[str] = duration_in_min
return job_info
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=None ) -> List[Any]:
"""simple docstring"""
snake_case_ : int = None
if token is not None:
snake_case_ : Optional[int] = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}
snake_case_ : Dict = f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
snake_case_ : Optional[Any] = requests.get(_UpperCamelCase , headers=_UpperCamelCase ).json()
snake_case_ : List[str] = {}
try:
job_time.update({job['''name''']: extract_time_from_single_job(_UpperCamelCase ) for job in result['''jobs''']} )
snake_case_ : Optional[int] = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(_UpperCamelCase ):
snake_case_ : Optional[Any] = requests.get(url + f'''&page={i + 2}''' , headers=_UpperCamelCase ).json()
job_time.update({job['''name''']: extract_time_from_single_job(_UpperCamelCase ) for job in result['''jobs''']} )
return job_time
except Exception:
print(f'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
lowerCAmelCase_ = parser.parse_args()
lowerCAmelCase_ = get_job_time(args.workflow_run_id)
lowerCAmelCase_ = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v['duration']}''')
| 60 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''pix2struct_text_model'''
lowerCamelCase_ : str = ['''past_key_values''']
lowerCamelCase_ : Any = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__(self , __magic_name__=5_0244 , __magic_name__=768 , __magic_name__=64 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=12 , __magic_name__=32 , __magic_name__=128 , __magic_name__=0.1 , __magic_name__=1e-6 , __magic_name__=1.0 , __magic_name__="gelu_new" , __magic_name__=0 , __magic_name__=False , __magic_name__=0 , __magic_name__=1 , __magic_name__=False , __magic_name__=True , **__magic_name__ , ) -> Tuple:
'''simple docstring'''
snake_case_ : str = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = d_kv
snake_case_ : Dict = d_ff
snake_case_ : str = num_layers
snake_case_ : Tuple = num_heads
snake_case_ : int = relative_attention_num_buckets
snake_case_ : Optional[int] = relative_attention_max_distance
snake_case_ : int = dropout_rate
snake_case_ : Optional[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_factor
snake_case_ : Union[str, Any] = use_cache
snake_case_ : str = eos_token_id
snake_case_ : Dict = decoder_start_token_id
# for backwards compatibility
snake_case_ : Tuple = dense_act_fn
super().__init__(
pad_token_id=__magic_name__ , eos_token_id=__magic_name__ , decoder_start_token_id=__magic_name__ , tie_word_embeddings=__magic_name__ , is_decoder=__magic_name__ , **__magic_name__ , )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[int] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
snake_case_ : Dict = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''pix2struct_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=768 , __magic_name__=2048 , __magic_name__=64 , __magic_name__=12 , __magic_name__=12 , __magic_name__="gelu_new" , __magic_name__=1e-6 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=1e-10 , __magic_name__=1.0 , __magic_name__=4096 , __magic_name__=32 , __magic_name__=128 , **__magic_name__ , ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Any = hidden_size
snake_case_ : int = patch_embed_hidden_size
snake_case_ : List[Any] = d_ff
snake_case_ : str = dropout_rate
snake_case_ : str = num_hidden_layers
snake_case_ : Optional[int] = num_attention_heads
snake_case_ : Tuple = initializer_range
snake_case_ : Any = initializer_factor
snake_case_ : str = attention_dropout
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : Optional[int] = dense_act_fn
snake_case_ : List[str] = seq_len
snake_case_ : Optional[int] = relative_attention_num_buckets
snake_case_ : List[str] = relative_attention_max_distance
snake_case_ : Tuple = d_kv
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[int] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : str = '''pix2struct'''
lowerCamelCase_ : List[str] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=1.0 , __magic_name__=0.02 , __magic_name__=False , __magic_name__=False , __magic_name__=True , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(tie_word_embeddings=__magic_name__ , is_encoder_decoder=__magic_name__ , **__magic_name__ )
if text_config is None:
snake_case_ : Dict = {}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
snake_case_ : Union[str, Any] = {}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
snake_case_ : Optional[Any] = PixaStructTextConfig(**__magic_name__ )
snake_case_ : Optional[int] = PixaStructVisionConfig(**__magic_name__ )
snake_case_ : Optional[int] = self.text_config.decoder_start_token_id
snake_case_ : Tuple = self.text_config.pad_token_id
snake_case_ : List[str] = self.text_config.eos_token_id
snake_case_ : int = initializer_factor
snake_case_ : List[str] = initializer_range
snake_case_ : Tuple = self.initializer_range
snake_case_ : List[Any] = self.initializer_range
snake_case_ : int = is_vqa
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Dict = self.text_config.to_dict()
snake_case_ : Dict = self.vision_config.to_dict()
snake_case_ : int = self.__class__.model_type
return output
| 60 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 1 |
import math
def lowerCamelCase_ ( _UpperCamelCase = 100 ) -> int:
"""simple docstring"""
snake_case_ : Any = sum(i * i for i in range(1 , n + 1 ) )
snake_case_ : Dict = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
| 60 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''' )
print('''Labels: {labels}''' )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 1 |
from __future__ import annotations
lowerCAmelCase_ = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
lowerCAmelCase_ = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def lowerCamelCase_ ( _UpperCamelCase ) -> list[float]:
"""simple docstring"""
snake_case_ : List[Any] = []
snake_case_ : Any = len(_UpperCamelCase )
for i in range(_UpperCamelCase ):
snake_case_ : float = -1
for j in range(i + 1 , _UpperCamelCase ):
if arr[i] < arr[j]:
snake_case_ : List[Any] = arr[j]
break
result.append(_UpperCamelCase )
return result
def lowerCamelCase_ ( _UpperCamelCase ) -> list[float]:
"""simple docstring"""
snake_case_ : List[str] = []
for i, outer in enumerate(_UpperCamelCase ):
snake_case_ : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
snake_case_ : int = inner
break
result.append(_UpperCamelCase )
return result
def lowerCamelCase_ ( _UpperCamelCase ) -> list[float]:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : list[float] = []
snake_case_ : list[float] = [-1] * arr_size
for index in reversed(range(_UpperCamelCase ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
snake_case_ : Optional[Any] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
lowerCAmelCase_ = (
'''from __main__ import arr, next_greatest_element_slow, '''
'''next_greatest_element_fast, next_greatest_element'''
)
print(
'''next_greatest_element_slow():''',
timeit('''next_greatest_element_slow(arr)''', setup=setup),
)
print(
'''next_greatest_element_fast():''',
timeit('''next_greatest_element_fast(arr)''', setup=setup),
)
print(
''' next_greatest_element():''',
timeit('''next_greatest_element(arr)''', setup=setup),
)
| 60 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCAmelCase_ = pytest.mark.integration
@pytest.mark.parametrize('''path''' , ['''paws''', '''csv'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
inspect_dataset(_UpperCamelCase , _UpperCamelCase )
snake_case_ : Optional[Any] = path + '''.py'''
assert script_name in os.listdir(_UpperCamelCase )
assert "__pycache__" not in os.listdir(_UpperCamelCase )
@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning''' )
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''' )
@pytest.mark.parametrize('''path''' , ['''accuracy'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
inspect_metric(_UpperCamelCase , _UpperCamelCase )
snake_case_ : Any = path + '''.py'''
assert script_name in os.listdir(_UpperCamelCase )
assert "__pycache__" not in os.listdir(_UpperCamelCase )
@pytest.mark.parametrize(
'''path, config_name, expected_splits''' , [
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] , )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Dict = get_dataset_config_info(_UpperCamelCase , config_name=_UpperCamelCase )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' , [
('''paws''', None, ValueError),
] , )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
with pytest.raises(_UpperCamelCase ):
get_dataset_config_info(_UpperCamelCase , config_name=_UpperCamelCase )
@pytest.mark.parametrize(
'''path, expected''' , [
('''squad''', '''plain_text'''),
('''acronym_identification''', '''default'''),
('''lhoestq/squad''', '''plain_text'''),
('''lhoestq/test''', '''default'''),
('''lhoestq/demo1''', '''lhoestq--demo1'''),
('''dalle-mini/wit''', '''dalle-mini--wit'''),
] , )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : List[str] = get_dataset_config_names(_UpperCamelCase )
assert expected in config_names
@pytest.mark.parametrize(
'''path, expected_configs, expected_splits_in_first_config''' , [
('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
] , )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Dict = get_dataset_infos(_UpperCamelCase )
assert list(infos.keys() ) == expected_configs
snake_case_ : List[str] = expected_configs[0]
assert expected_config in infos
snake_case_ : int = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
'''path, expected_config, expected_splits''' , [
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] , )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : List[str] = get_dataset_infos(_UpperCamelCase )
assert expected_config in infos
snake_case_ : List[str] = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' , [
('''paws''', None, ValueError),
] , )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
with pytest.raises(_UpperCamelCase ):
get_dataset_split_names(_UpperCamelCase , config_name=_UpperCamelCase )
| 60 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 1 |
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_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : 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_ ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
snake_case_ : Union[str, 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_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Any = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''') )
return token
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = []
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_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Any = '''imagenet-1k-id2label.json'''
snake_case_ : List[Any] = 1_000
snake_case_ : Dict = '''huggingface/label-files'''
snake_case_ : List[str] = num_labels
snake_case_ : List[str] = json.load(open(cached_download(hf_hub_url(_UpperCamelCase , _UpperCamelCase , repo_type='''dataset''' ) ) , '''r''' ) )
snake_case_ : Dict = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : List[str] = idalabel
snake_case_ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case_ : Tuple = CvtConfig(num_labels=_UpperCamelCase , idalabel=_UpperCamelCase , labelaid=_UpperCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "13":
snake_case_ : Optional[int] = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "21":
snake_case_ : Union[str, Any] = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
snake_case_ : List[Any] = [2, 2, 20]
snake_case_ : str = [3, 12, 16]
snake_case_ : Union[str, Any] = [192, 768, 1_024]
snake_case_ : Optional[int] = CvtForImageClassification(_UpperCamelCase )
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
snake_case_ : List[str] = image_size
snake_case_ : Any = torch.load(_UpperCamelCase , map_location=torch.device('''cpu''' ) )
snake_case_ : Optional[Any] = OrderedDict()
snake_case_ : List[Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
snake_case_ : Dict = list_of_state_dict + cls_token(_UpperCamelCase )
snake_case_ : List[Any] = list_of_state_dict + embeddings(_UpperCamelCase )
for cnt in range(config.depth[idx] ):
snake_case_ : Tuple = list_of_state_dict + attention(_UpperCamelCase , _UpperCamelCase )
snake_case_ : Dict = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCamelCase )
for i in range(len(_UpperCamelCase ) ):
snake_case_ : str = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
image_processor.save_pretrained(_UpperCamelCase )
# 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)
| 60 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> list[int]:
"""simple docstring"""
snake_case_ : Optional[int] = [0 for i in range(len(_UpperCamelCase ) )]
# initialize interval's left pointer and right pointer
snake_case_ , snake_case_ : Tuple = 0, 0
for i in range(1 , len(_UpperCamelCase ) ):
# case when current index is inside the interval
if i <= right_pointer:
snake_case_ : Any = min(right_pointer - i + 1 , z_result[i - left_pointer] )
snake_case_ : Tuple = min_edge
while go_next(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
snake_case_ , snake_case_ : int = i, i + z_result[i] - 1
return z_result
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
return i + z_result[i] < len(_UpperCamelCase ) and s[z_result[i]] == s[i + z_result[i]]
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : Tuple = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
snake_case_ : Union[str, Any] = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(_UpperCamelCase ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 1 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowerCAmelCase_ = re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')
@total_ordering
@dataclass
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : Optional[str] = None
lowerCamelCase_ : Optional[Union[str, int]] = None
lowerCamelCase_ : Optional[Union[str, int]] = None
lowerCamelCase_ : Optional[Union[str, int]] = None
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ , snake_case_ : Dict = _str_to_version_tuple(self.version_str )
def __repr__(self ) -> Optional[Any]:
'''simple docstring'''
return F'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''
@property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return self.major, self.minor, self.patch
def lowerCamelCase (self , __magic_name__ ) -> List[Any]:
'''simple docstring'''
if isinstance(__magic_name__ , __magic_name__ ):
return Version(__magic_name__ )
elif isinstance(__magic_name__ , __magic_name__ ):
return other
raise TypeError(F'''{other} (type {type(__magic_name__ )}) cannot be compared to version.''' )
def __eq__(self , __magic_name__ ) -> Any:
'''simple docstring'''
try:
snake_case_ : Any = self._validate_operand(__magic_name__ )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__(self , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = self._validate_operand(__magic_name__ )
return self.tuple < other.tuple
def __hash__(self ) -> Dict:
'''simple docstring'''
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def lowerCamelCase (cls , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.version_str
def lowerCamelCase_ ( _UpperCamelCase ) -> List[str]:
"""simple docstring"""
snake_case_ : Tuple = _VERSION_REG.match(_UpperCamelCase )
if not res:
raise ValueError(f'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.''' )
return tuple(int(_UpperCamelCase ) for v in [res.group('''major''' ), res.group('''minor''' ), res.group('''patch''' )] )
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
return ".".join(str(_UpperCamelCase ) for v in version_tuple )
| 60 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''NllbTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''NllbTokenizerFast''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase = "cpu" , _UpperCamelCase = None ) -> None:
"""simple docstring"""
snake_case_ : int = torch.load(_UpperCamelCase , map_location=_UpperCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(_UpperCamelCase , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
snake_case_ : Optional[Any] = v.half()
if save_path is None: # overwrite src_path
snake_case_ : Any = src_path
torch.save(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 60 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
return " ".join(
''''''.join(word[::-1] ) if len(_UpperCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 60 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 60 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[str] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Dict = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : int = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Tuple = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Tuple = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Union[str, Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : str = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[int] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Union[str, Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : int = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : int = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
def lowerCamelCase_ ( *_UpperCamelCase , **_UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
requires_backends(_UpperCamelCase , ['''torch'''] )
def lowerCamelCase_ ( *_UpperCamelCase , **_UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(_UpperCamelCase , ['''torch'''] )
def lowerCamelCase_ ( *_UpperCamelCase , **_UpperCamelCase ) -> Dict:
"""simple docstring"""
requires_backends(_UpperCamelCase , ['''torch'''] )
def lowerCamelCase_ ( *_UpperCamelCase , **_UpperCamelCase ) -> int:
"""simple docstring"""
requires_backends(_UpperCamelCase , ['''torch'''] )
def lowerCamelCase_ ( *_UpperCamelCase , **_UpperCamelCase ) -> List[str]:
"""simple docstring"""
requires_backends(_UpperCamelCase , ['''torch'''] )
def lowerCamelCase_ ( *_UpperCamelCase , **_UpperCamelCase ) -> int:
"""simple docstring"""
requires_backends(_UpperCamelCase , ['''torch'''] )
def lowerCamelCase_ ( *_UpperCamelCase , **_UpperCamelCase ) -> Tuple:
"""simple docstring"""
requires_backends(_UpperCamelCase , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Union[str, Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Any = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Union[str, Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Dict = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Any = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : str = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : str = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : int = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[int] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Tuple = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Any = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : int = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[int] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : str = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : str = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[int] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : int = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[str] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : str = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Union[str, Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : str = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : int = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Union[str, Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[Any] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : int = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[str] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Optional[int] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : List[str] = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
class __lowerCAmelCase ( metaclass=_a ):
lowerCamelCase_ : Any = ['''torch''']
def __init__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
@classmethod
def lowerCamelCase (cls , *__magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ['''torch'''] )
| 60 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''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 __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''wavlm'''
def __init__(self , __magic_name__=32 , __magic_name__=768 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3072 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.0 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.02 , __magic_name__=1e-5 , __magic_name__="group" , __magic_name__="gelu" , __magic_name__=(512, 512, 512, 512, 512, 512, 512) , __magic_name__=(5, 2, 2, 2, 2, 2, 2) , __magic_name__=(10, 3, 3, 3, 3, 2, 2) , __magic_name__=False , __magic_name__=128 , __magic_name__=16 , __magic_name__=320 , __magic_name__=800 , __magic_name__=False , __magic_name__=True , __magic_name__=0.05 , __magic_name__=10 , __magic_name__=2 , __magic_name__=0.0 , __magic_name__=10 , __magic_name__=320 , __magic_name__=2 , __magic_name__=0.1 , __magic_name__=100 , __magic_name__=256 , __magic_name__=256 , __magic_name__=0.1 , __magic_name__="mean" , __magic_name__=False , __magic_name__=False , __magic_name__=256 , __magic_name__=(512, 512, 512, 512, 1500) , __magic_name__=(5, 3, 3, 1, 1) , __magic_name__=(1, 2, 3, 1, 1) , __magic_name__=512 , __magic_name__=80 , __magic_name__=0 , __magic_name__=1 , __magic_name__=2 , __magic_name__=False , __magic_name__=3 , __magic_name__=2 , __magic_name__=3 , __magic_name__=None , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ , pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ )
snake_case_ : Tuple = hidden_size
snake_case_ : Optional[Any] = feat_extract_norm
snake_case_ : Any = feat_extract_activation
snake_case_ : Any = list(__magic_name__ )
snake_case_ : Optional[Any] = list(__magic_name__ )
snake_case_ : Tuple = list(__magic_name__ )
snake_case_ : Dict = conv_bias
snake_case_ : Union[str, Any] = num_buckets
snake_case_ : List[Any] = max_bucket_distance
snake_case_ : Union[str, Any] = num_conv_pos_embeddings
snake_case_ : Dict = num_conv_pos_embedding_groups
snake_case_ : Union[str, Any] = len(self.conv_dim )
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : str = num_attention_heads
snake_case_ : int = hidden_dropout
snake_case_ : Optional[int] = attention_dropout
snake_case_ : Tuple = activation_dropout
snake_case_ : str = feat_proj_dropout
snake_case_ : Optional[int] = final_dropout
snake_case_ : Tuple = layerdrop
snake_case_ : Any = layer_norm_eps
snake_case_ : List[str] = initializer_range
snake_case_ : Tuple = num_ctc_classes
snake_case_ : List[Any] = vocab_size
snake_case_ : Tuple = do_stable_layer_norm
snake_case_ : Optional[int] = use_weighted_layer_sum
snake_case_ : Optional[int] = 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
snake_case_ : str = apply_spec_augment
snake_case_ : Optional[int] = mask_time_prob
snake_case_ : List[str] = mask_time_length
snake_case_ : List[str] = mask_time_min_masks
snake_case_ : Optional[int] = mask_feature_prob
snake_case_ : Union[str, Any] = mask_feature_length
# parameters for pretraining with codevector quantized representations
snake_case_ : Union[str, Any] = num_codevectors_per_group
snake_case_ : Union[str, Any] = num_codevector_groups
snake_case_ : str = contrastive_logits_temperature
snake_case_ : Union[str, Any] = num_negatives
snake_case_ : int = codevector_dim
snake_case_ : List[Any] = proj_codevector_dim
snake_case_ : List[str] = diversity_loss_weight
# ctc loss
snake_case_ : Optional[int] = ctc_loss_reduction
snake_case_ : int = ctc_zero_infinity
# adapter
snake_case_ : List[str] = add_adapter
snake_case_ : int = adapter_kernel_size
snake_case_ : List[Any] = adapter_stride
snake_case_ : Dict = num_adapter_layers
snake_case_ : Optional[int] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case_ : Optional[int] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case_ : Optional[Any] = list(__magic_name__ )
snake_case_ : Tuple = list(__magic_name__ )
snake_case_ : Optional[Any] = list(__magic_name__ )
snake_case_ : Tuple = xvector_output_dim
@property
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 60 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __lowerCAmelCase ( _a ):
def __lt__(self , __magic_name__ ) -> str:
'''simple docstring'''
return self[-1] < other[-1]
def __eq__(self , __magic_name__ ) -> Dict:
'''simple docstring'''
return self[-1] == other[-1]
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : list[Stack] = []
# sort into stacks
for element in collection:
snake_case_ : Optional[Any] = Stack([element] )
snake_case_ : Tuple = 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
snake_case_ : Union[str, Any] = merge(*(reversed(_UpperCamelCase ) for stack in stacks) )
return collection
if __name__ == "__main__":
lowerCAmelCase_ = input('''Enter numbers separated by a comma:\n''').strip()
lowerCAmelCase_ = [int(item) for item in user_input.split(''',''')]
print(patience_sort(unsorted))
| 60 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, 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 (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=7 , __magic_name__=True , __magic_name__=True , __magic_name__=True , __magic_name__=True , __magic_name__=True , __magic_name__=False , __magic_name__=False , __magic_name__=False , __magic_name__=2 , __magic_name__=99 , __magic_name__=0 , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=512 , __magic_name__=12 , __magic_name__=2 , __magic_name__=0.02 , __magic_name__=3 , __magic_name__=4 , __magic_name__="last" , __magic_name__=None , __magic_name__=None , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = parent
snake_case_ : Dict = batch_size
snake_case_ : List[str] = seq_length
snake_case_ : Any = is_training
snake_case_ : Tuple = use_input_lengths
snake_case_ : str = use_token_type_ids
snake_case_ : Any = use_labels
snake_case_ : List[str] = gelu_activation
snake_case_ : int = sinusoidal_embeddings
snake_case_ : Optional[Any] = causal
snake_case_ : Any = asm
snake_case_ : Optional[Any] = n_langs
snake_case_ : Any = vocab_size
snake_case_ : int = n_special
snake_case_ : List[Any] = hidden_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Any = num_attention_heads
snake_case_ : List[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : Tuple = type_vocab_size
snake_case_ : Tuple = type_sequence_label_size
snake_case_ : int = initializer_range
snake_case_ : Union[str, Any] = num_labels
snake_case_ : Any = num_choices
snake_case_ : List[str] = summary_type
snake_case_ : int = use_proj
snake_case_ : Tuple = scope
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : str = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Dict = None
if self.use_input_lengths:
snake_case_ : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
snake_case_ : Union[str, Any] = None
if self.use_token_type_ids:
snake_case_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
snake_case_ : Union[str, Any] = None
snake_case_ : int = None
snake_case_ : Optional[Any] = None
if self.use_labels:
snake_case_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , 2 ).float()
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ : Tuple = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return 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 , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = FlaubertModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : str = model(__magic_name__ , lengths=__magic_name__ , langs=__magic_name__ )
snake_case_ : Tuple = model(__magic_name__ , langs=__magic_name__ )
snake_case_ : Dict = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = FlaubertWithLMHeadModel(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Optional[int] = model(__magic_name__ , token_type_ids=__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = FlaubertForQuestionAnsweringSimple(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Optional[int] = model(__magic_name__ )
snake_case_ : Tuple = model(__magic_name__ , start_positions=__magic_name__ , end_positions=__magic_name__ )
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 , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> Dict:
'''simple docstring'''
snake_case_ : Tuple = FlaubertForQuestionAnswering(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : str = model(__magic_name__ )
snake_case_ : Tuple = model(
__magic_name__ , start_positions=__magic_name__ , end_positions=__magic_name__ , cls_index=__magic_name__ , is_impossible=__magic_name__ , p_mask=__magic_name__ , )
snake_case_ : Dict = model(
__magic_name__ , start_positions=__magic_name__ , end_positions=__magic_name__ , cls_index=__magic_name__ , is_impossible=__magic_name__ , )
((snake_case_) , ) : List[Any] = result_with_labels.to_tuple()
snake_case_ : Tuple = model(__magic_name__ , start_positions=__magic_name__ , end_positions=__magic_name__ )
((snake_case_) , ) : Optional[int] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = FlaubertForSequenceClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ )
snake_case_ : List[Any] = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : str = FlaubertForTokenClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = model(__magic_name__ , attention_mask=__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.num_choices
snake_case_ : Optional[Any] = FlaubertForMultipleChoice(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Optional[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : Dict = model(
__magic_name__ , attention_mask=__magic_name__ , token_type_ids=__magic_name__ , labels=__magic_name__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) : Tuple = config_and_inputs
snake_case_ : Optional[Any] = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : str = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
lowerCamelCase_ : List[Any] = (
{
'''feature-extraction''': FlaubertModel,
'''fill-mask''': FlaubertWithLMHeadModel,
'''question-answering''': FlaubertForQuestionAnsweringSimple,
'''text-classification''': FlaubertForSequenceClassification,
'''token-classification''': FlaubertForTokenClassification,
'''zero-shot''': FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
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 lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=False ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = super()._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
snake_case_ : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__magic_name__ )
snake_case_ : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__magic_name__ )
return inputs_dict
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[str] = FlaubertModelTester(self )
snake_case_ : Tuple = ConfigTester(self , config_class=__magic_name__ , emb_dim=37 )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*__magic_name__ )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*__magic_name__ )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*__magic_name__ )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*__magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = FlaubertModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
@slow
@require_torch_gpu
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ , snake_case_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
snake_case_ : Any = True
snake_case_ : str = model_class(config=__magic_name__ )
snake_case_ : str = self._prepare_for_class(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = torch.jit.trace(
__magic_name__ , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(__magic_name__ , os.path.join(__magic_name__ , '''traced_model.pt''' ) )
snake_case_ : Dict = torch.jit.load(os.path.join(__magic_name__ , '''traced_model.pt''' ) , map_location=__magic_name__ )
loaded(inputs_dict['''input_ids'''].to(__magic_name__ ) , inputs_dict['''attention_mask'''].to(__magic_name__ ) )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Union[str, Any] = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
snake_case_ : Dict = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
snake_case_ : List[Any] = model(__magic_name__ )[0]
snake_case_ : Tuple = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , __magic_name__ )
snake_case_ : Tuple = torch.tensor(
[[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __magic_name__ , atol=1e-4 ) )
| 60 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ ) -> int:
'''simple docstring'''
snake_case_ : str = parent
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = '''<HTML>
<HEAD>
<TITLE>sample document</TITLE>
</HEAD>
<BODY BGCOLOR="FFFFFF">
<HR>
<a href="http://google.com">Goog</a>
<H1>This is one header</H1>
<H2>This is a another Header</H2>
<P>Travel from
<P>
<B>SFO to JFK</B>
<BR>
<B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
<HR>
<div style="color:#0000FF">
<h3>Traveler <b> name </b> is
<p> John Doe </p>
</div>'''
snake_case_ : Tuple = '''
<!DOCTYPE html>
<html>
<body>
<h1>My First Heading</h1>
<p>My first paragraph.</p>
</body>
</html>
'''
return [html_string_a, html_string_a]
@require_bsa
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : int = MarkupLMFeatureExtractor if is_bsa_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : str = MarkupLMFeatureExtractionTester(self )
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return self.feature_extract_tester.prepare_feat_extract_dict()
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.feature_extraction_class()
# Test not batched input
snake_case_ : Tuple = get_html_strings()[0]
snake_case_ : Union[str, Any] = feature_extractor(__magic_name__ )
# fmt: off
snake_case_ : int = [['''sample document''', '''Goog''', '''This is one header''', '''This is a another Header''', '''Travel from''', '''SFO to JFK''', '''on May 2, 2015 at 2:00 pm. For details go to confirm.com''', '''Traveler''', '''name''', '''is''', '''John Doe''']]
snake_case_ : Any = [['''/html/head/title''', '''/html/body/a''', '''/html/body/h1''', '''/html/body/h2''', '''/html/body/p''', '''/html/body/p/p/b[1]''', '''/html/body/p/p/b[2]/i''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/b''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/p''']]
# fmt: on
self.assertEqual(encoding.nodes , __magic_name__ )
self.assertEqual(encoding.xpaths , __magic_name__ )
# Test batched
snake_case_ : int = get_html_strings()
snake_case_ : List[str] = feature_extractor(__magic_name__ )
# fmt: off
snake_case_ : Any = expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
snake_case_ : int = expected_xpaths + [['''/html/body/h1''', '''/html/body/p''']]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , __magic_name__ )
self.assertEqual(encoding.xpaths , __magic_name__ )
| 60 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 1 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class __lowerCAmelCase ( _a ):
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__magic_name__ , '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(__magic_name__ , '''neck_hidden_sizes''' ) )
self.parent.assertTrue(hasattr(__magic_name__ , '''num_attention_heads''' ) )
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=32 , __magic_name__=2 , __magic_name__=3 , __magic_name__=640 , __magic_name__=4 , __magic_name__="silu" , __magic_name__=3 , __magic_name__=32 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.02 , __magic_name__=True , __magic_name__=True , __magic_name__=10 , __magic_name__=None , ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = parent
snake_case_ : str = batch_size
snake_case_ : Union[str, Any] = image_size
snake_case_ : Any = patch_size
snake_case_ : Union[str, Any] = num_channels
snake_case_ : Dict = last_hidden_size
snake_case_ : Dict = num_attention_heads
snake_case_ : str = hidden_act
snake_case_ : Optional[int] = conv_kernel_size
snake_case_ : str = output_stride
snake_case_ : int = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = classifier_dropout_prob
snake_case_ : Any = use_labels
snake_case_ : Tuple = is_training
snake_case_ : Dict = num_labels
snake_case_ : Any = initializer_range
snake_case_ : List[Any] = scope
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Tuple = None
snake_case_ : Optional[int] = None
if self.use_labels:
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ : int = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case_ : int = self.get_config()
return config, pixel_values, labels, pixel_labels
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[str] = MobileViTModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Dict = model(__magic_name__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Any = self.num_labels
snake_case_ : Optional[int] = MobileViTForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Dict = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : int = MobileViTForSemanticSegmentation(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Dict = model(__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case_ : List[Any] = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[str] = config_and_inputs
snake_case_ : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : Tuple = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase_ : Tuple = (
{
'''feature-extraction''': MobileViTModel,
'''image-classification''': MobileViTForImageClassification,
'''image-segmentation''': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase_ : Dict = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : int = False
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = MobileViTModelTester(self )
snake_case_ : int = MobileViTConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
pass
@unittest.skip(reason='''MobileViT does not output attentions''' )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ , snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
snake_case_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : int = [*signature.parameters.keys()]
snake_case_ : List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
def check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ ):
snake_case_ : Optional[Any] = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
snake_case_ : Optional[int] = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
snake_case_ : Tuple = outputs.hidden_states
snake_case_ : Tuple = 5
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
snake_case_ : str = 2
for i in range(len(__magic_name__ ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
snake_case_ , snake_case_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : int = True
check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ : Optional[Any] = True
check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = MobileViTModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(__magic_name__ )
snake_case_ : List[Any] = self.default_image_processor
snake_case_ : Dict = prepare_img()
snake_case_ : Optional[int] = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
# verify the logits
snake_case_ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : str = torch.tensor([-1.9_364, -1.2_327, -0.4_653] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
@slow
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
snake_case_ : str = model.to(__magic_name__ )
snake_case_ : Optional[Any] = MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
snake_case_ : Any = prepare_img()
snake_case_ : Optional[Any] = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
snake_case_ : Optional[Any] = outputs.logits
# verify the logits
snake_case_ : List[Any] = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __magic_name__ )
snake_case_ : List[str] = torch.tensor(
[
[[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
[[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
[[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
] , device=__magic_name__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __magic_name__ , atol=1e-4 ) )
@slow
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : int = MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
snake_case_ : Any = model.to(__magic_name__ )
snake_case_ : int = MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
snake_case_ : List[Any] = prepare_img()
snake_case_ : str = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : str = model(**__magic_name__ )
snake_case_ : str = outputs.logits.detach().cpu()
snake_case_ : List[str] = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ , target_sizes=[(50, 60)] )
snake_case_ : List[Any] = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
snake_case_ : List[str] = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ )
snake_case_ : Tuple = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
| 60 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
lowerCAmelCase_ = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__=1 ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Union[str, Any] = tokenizer
snake_case_ : int = dataset
snake_case_ : List[str] = len(__magic_name__ ) if n_tasks is None else n_tasks
snake_case_ : Dict = n_copies
def __iter__(self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip() )
snake_case_ : Optional[Any] = self.tokenizer(__magic_name__ , padding=__magic_name__ , return_tensors='''pt''' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = start_length
snake_case_ : Any = eof_strings
snake_case_ : Tuple = tokenizer
def __call__(self , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
snake_case_ : Union[str, Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(__magic_name__ )
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[int] = re.split('''(%s)''' % '''|'''.join(_UpperCamelCase ) , _UpperCamelCase )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=20 , **_UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Dict = defaultdict(_UpperCamelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_UpperCamelCase ) ):
with torch.no_grad():
snake_case_ : List[Any] = batch['''ids'''].shape[-1]
snake_case_ : List[str] = accelerator.unwrap_model(_UpperCamelCase ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']] , num_return_sequences=_UpperCamelCase , **_UpperCamelCase )
# each task is generated batch_size times
snake_case_ : Tuple = batch['''task_id'''].repeat(_UpperCamelCase )
snake_case_ : Tuple = accelerator.pad_across_processes(
_UpperCamelCase , dim=1 , pad_index=tokenizer.pad_token_id )
snake_case_ , snake_case_ : List[Any] = accelerator.gather((generated_tokens, generated_tasks) )
snake_case_ : Union[str, Any] = generated_tokens.cpu().numpy()
snake_case_ : str = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_UpperCamelCase , _UpperCamelCase ):
gen_token_dict[task].append(_UpperCamelCase )
snake_case_ : Any = [[] for _ in range(_UpperCamelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
snake_case_ : Tuple = tokenizer.decode(_UpperCamelCase , skip_special_tokens=_UpperCamelCase , clean_up_tokenization_spaces=_UpperCamelCase )
code_gens[task].append(remove_last_block(_UpperCamelCase ) )
return code_gens
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Dict = HfArgumentParser(_UpperCamelCase )
snake_case_ : Optional[Any] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
snake_case_ : Any = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
snake_case_ : List[str] = '''false'''
if args.num_workers is None:
snake_case_ : Dict = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
snake_case_ : List[str] = Accelerator()
set_seed(args.seed , device_specific=_UpperCamelCase )
# Load model and tokenizer
snake_case_ : Optional[Any] = AutoTokenizer.from_pretrained(args.model_ckpt )
snake_case_ : List[Any] = tokenizer.eos_token
snake_case_ : Dict = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
snake_case_ : Union[str, Any] = {
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , _UpperCamelCase , _UpperCamelCase )] ),
}
# Load evaluation dataset and metric
snake_case_ : List[Any] = load_dataset('''openai_humaneval''' )
snake_case_ : Dict = load_metric('''code_eval''' )
snake_case_ : Union[str, Any] = args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
snake_case_ : Optional[Any] = args.n_samples // args.batch_size
snake_case_ : List[Any] = TokenizedDataset(_UpperCamelCase , human_eval['''test'''] , n_copies=_UpperCamelCase , n_tasks=_UpperCamelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
snake_case_ : List[Any] = DataLoader(_UpperCamelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
snake_case_ : int = code_eval_metric.compute(references=[''''''] , predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
snake_case_ , snake_case_ : Any = accelerator.prepare(_UpperCamelCase , _UpperCamelCase )
snake_case_ : List[Any] = complete_code(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , n_tasks=_UpperCamelCase , batch_size=args.batch_size , **_UpperCamelCase , )
if accelerator.is_main_process:
snake_case_ : List[Any] = []
for task in tqdm(range(_UpperCamelCase ) ):
snake_case_ : Optional[Any] = human_eval['''test'''][task]['''test''']
snake_case_ : int = f'''check({human_eval["test"][task]["entry_point"]})'''
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
snake_case_ , snake_case_ : Dict = code_eval_metric.compute(
references=_UpperCamelCase , predictions=_UpperCamelCase , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , '''w''' ) as fp:
json.dump(_UpperCamelCase , _UpperCamelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 60 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
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(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'''pipelines_utils''',
'''0.22.0''',
'''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
standard_warn=False,
stacklevel=3,
)
| 60 |
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 ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = 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 lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : 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=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = 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=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
| 60 | 1 |
import os
from pathlib import Path
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
from torch.utils.cpp_extension import load
snake_case_ : List[str] = Path(_UpperCamelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
snake_case_ : Any = [
root / filename
for filename in [
'''vision.cpp''',
os.path.join('''cpu''' , '''ms_deform_attn_cpu.cpp''' ),
os.path.join('''cuda''' , '''ms_deform_attn_cuda.cu''' ),
]
]
load(
'''MultiScaleDeformableAttention''' , _UpperCamelCase , with_cuda=_UpperCamelCase , extra_include_paths=[str(_UpperCamelCase )] , extra_cflags=['''-DWITH_CUDA=1'''] , extra_cuda_cflags=[
'''-DCUDA_HAS_FP16=1''',
'''-D__CUDA_NO_HALF_OPERATORS__''',
'''-D__CUDA_NO_HALF_CONVERSIONS__''',
'''-D__CUDA_NO_HALF2_OPERATORS__''',
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 60 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = '''▁'''
lowerCAmelCase_ = {'''vocab_file''': '''spiece.model'''}
lowerCAmelCase_ = {
'''vocab_file''': {
'''google/reformer-crime-and-punishment''': (
'''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
)
}
}
lowerCAmelCase_ = {
'''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = VOCAB_FILES_NAMES
lowerCamelCase_ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ : List[Any] = ['''input_ids''', '''attention_mask''']
def __init__(self , __magic_name__ , __magic_name__="</s>" , __magic_name__="<unk>" , __magic_name__=[] , __magic_name__ = None , **__magic_name__ , ) -> None:
'''simple docstring'''
snake_case_ : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=__magic_name__ , unk_token=__magic_name__ , additional_special_tokens=__magic_name__ , sp_model_kwargs=self.sp_model_kwargs , **__magic_name__ , )
snake_case_ : int = vocab_file
snake_case_ : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__magic_name__ )
@property
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
return self.sp_model.get_piece_size()
def lowerCamelCase (self ) -> Dict[str, int]:
'''simple docstring'''
snake_case_ : Union[str, Any] = {self.convert_ids_to_tokens(__magic_name__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__(self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.__dict__.copy()
snake_case_ : Any = None
return state
def __setstate__(self , __magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
snake_case_ : Optional[Any] = {}
snake_case_ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCamelCase (self , __magic_name__ ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(__magic_name__ , out_type=__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Tuple:
'''simple docstring'''
return self.sp_model.piece_to_id(__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if index < self.sp_model.get_piece_size():
snake_case_ : List[Any] = self.sp_model.IdToPiece(__magic_name__ )
return token
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = []
snake_case_ : Optional[int] = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__magic_name__ ) + token
snake_case_ : str = []
else:
current_sub_tokens.append(__magic_name__ )
out_string += self.sp_model.decode(__magic_name__ )
return out_string.strip()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(__magic_name__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case_ : str = os.path.join(
__magic_name__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__magic_name__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __magic_name__ )
elif not os.path.isfile(self.vocab_file ):
with open(__magic_name__ , '''wb''' ) as fi:
snake_case_ : Any = self.sp_model.serialized_model_proto()
fi.write(__magic_name__ )
return (out_vocab_file,)
| 60 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 1 |
from math import isqrt
def lowerCamelCase_ ( _UpperCamelCase ) -> list[int]:
"""simple docstring"""
snake_case_ : int = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _UpperCamelCase , _UpperCamelCase ):
snake_case_ : List[Any] = False
return [i for i in range(2 , _UpperCamelCase ) if is_prime[i]]
def lowerCamelCase_ ( _UpperCamelCase = 10**8 ) -> int:
"""simple docstring"""
snake_case_ : Tuple = calculate_prime_numbers(max_number // 2 )
snake_case_ : Optional[Any] = 0
snake_case_ : Tuple = 0
snake_case_ : List[str] = len(_UpperCamelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 60 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
lowerCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCAmelCase_ = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=8 ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : int = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
snake_case_ : int = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , ) -> str:
'''simple docstring'''
super().__init__()
self.register_modules(
unet=__magic_name__ , scheduler=__magic_name__ , movq=__magic_name__ , )
snake_case_ : List[str] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> str:
'''simple docstring'''
if latents is None:
snake_case_ : int = randn_tensor(__magic_name__ , generator=__magic_name__ , device=__magic_name__ , dtype=__magic_name__ )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
snake_case_ : int = latents.to(__magic_name__ )
snake_case_ : Any = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase (self , __magic_name__=0 ) -> Any:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
snake_case_ : Optional[int] = torch.device(F'''cuda:{gpu_id}''' )
snake_case_ : Optional[int] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__=0 ) -> int:
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
snake_case_ : Tuple = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=__magic_name__ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
snake_case_ : Tuple = None
for cpu_offloaded_model in [self.unet, self.movq]:
snake_case_ , snake_case_ : str = cpu_offload_with_hook(__magic_name__ , __magic_name__ , prev_module_hook=__magic_name__ )
# We'll offload the last model manually.
snake_case_ : str = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(__magic_name__ , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__magic_name__ )
def __call__(self , __magic_name__ , __magic_name__ , __magic_name__ = 512 , __magic_name__ = 512 , __magic_name__ = 100 , __magic_name__ = 4.0 , __magic_name__ = 1 , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "pil" , __magic_name__ = True , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = self._execution_device
snake_case_ : Optional[Any] = guidance_scale > 1.0
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Tuple = torch.cat(__magic_name__ , dim=0 )
snake_case_ : Union[str, Any] = image_embeds.shape[0] * num_images_per_prompt
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = torch.cat(__magic_name__ , dim=0 )
if do_classifier_free_guidance:
snake_case_ : List[str] = image_embeds.repeat_interleave(__magic_name__ , dim=0 )
snake_case_ : List[Any] = negative_image_embeds.repeat_interleave(__magic_name__ , dim=0 )
snake_case_ : str = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__magic_name__ )
self.scheduler.set_timesteps(__magic_name__ , device=__magic_name__ )
snake_case_ : Tuple = self.scheduler.timesteps
snake_case_ : Any = self.unet.config.in_channels
snake_case_ , snake_case_ : Union[str, Any] = downscale_height_and_width(__magic_name__ , __magic_name__ , self.movq_scale_factor )
# create initial latent
snake_case_ : List[Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __magic_name__ , __magic_name__ , __magic_name__ , self.scheduler , )
for i, t in enumerate(self.progress_bar(__magic_name__ ) ):
# expand the latents if we are doing classifier free guidance
snake_case_ : Any = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
snake_case_ : List[Any] = {'''image_embeds''': image_embeds}
snake_case_ : Union[str, Any] = self.unet(
sample=__magic_name__ , timestep=__magic_name__ , encoder_hidden_states=__magic_name__ , added_cond_kwargs=__magic_name__ , return_dict=__magic_name__ , )[0]
if do_classifier_free_guidance:
snake_case_ , snake_case_ : List[str] = noise_pred.split(latents.shape[1] , dim=1 )
snake_case_ , snake_case_ : Union[str, Any] = noise_pred.chunk(2 )
snake_case_ , snake_case_ : Dict = variance_pred.chunk(2 )
snake_case_ : Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
snake_case_ : Optional[int] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
snake_case_ , snake_case_ : int = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ : List[Any] = self.scheduler.step(
__magic_name__ , __magic_name__ , __magic_name__ , generator=__magic_name__ , )[0]
# post-processing
snake_case_ : int = self.movq.decode(__magic_name__ , force_not_quantize=__magic_name__ )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
snake_case_ : Tuple = image * 0.5 + 0.5
snake_case_ : List[str] = image.clamp(0 , 1 )
snake_case_ : int = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case_ : str = self.numpy_to_pil(__magic_name__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__magic_name__ )
| 60 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[str] = tempfile.mkdtemp()
snake_case_ : str = BlipImageProcessor()
snake_case_ : Optional[int] = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
snake_case_ : List[Any] = BlipaProcessor(__magic_name__ , __magic_name__ )
processor.save_pretrained(self.tmpdirname )
def lowerCamelCase (self , **__magic_name__ ) -> str:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).tokenizer
def lowerCamelCase (self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).image_processor
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
snake_case_ : List[str] = [Image.fromarray(np.moveaxis(__magic_name__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : int = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case_ : List[Any] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
snake_case_ : List[str] = self.get_image_processor(do_normalize=__magic_name__ , padding_value=1.0 )
snake_case_ : Dict = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__magic_name__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __magic_name__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __magic_name__ )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Dict = self.get_image_processor()
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ : Tuple = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
snake_case_ : str = self.prepare_image_inputs()
snake_case_ : Tuple = image_processor(__magic_name__ , return_tensors='''np''' )
snake_case_ : int = processor(images=__magic_name__ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.get_image_processor()
snake_case_ : Tuple = self.get_tokenizer()
snake_case_ : Optional[int] = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
snake_case_ : Union[str, Any] = '''lower newer'''
snake_case_ : str = processor(text=__magic_name__ )
snake_case_ : Any = tokenizer(__magic_name__ , return_token_type_ids=__magic_name__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = self.get_image_processor()
snake_case_ : str = self.get_tokenizer()
snake_case_ : Tuple = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
snake_case_ : Union[str, Any] = '''lower newer'''
snake_case_ : Tuple = self.prepare_image_inputs()
snake_case_ : Dict = processor(text=__magic_name__ , images=__magic_name__ )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
# test if it raises when no input is passed
with pytest.raises(__magic_name__ ):
processor()
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.get_image_processor()
snake_case_ : Optional[Any] = self.get_tokenizer()
snake_case_ : Union[str, Any] = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
snake_case_ : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case_ : List[Any] = processor.batch_decode(__magic_name__ )
snake_case_ : str = tokenizer.batch_decode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Tuple = self.get_image_processor()
snake_case_ : List[Any] = self.get_tokenizer()
snake_case_ : str = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
snake_case_ : Union[str, Any] = '''lower newer'''
snake_case_ : Tuple = self.prepare_image_inputs()
snake_case_ : Any = processor(text=__magic_name__ , images=__magic_name__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
| 60 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''' )
print('''Labels: {labels}''' )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : Dict = AudioLDMPipeline
lowerCamelCase_ : List[Any] = TEXT_TO_AUDIO_PARAMS
lowerCamelCase_ : Optional[int] = TEXT_TO_AUDIO_BATCH_PARAMS
lowerCamelCase_ : Any = frozenset(
[
'''num_inference_steps''',
'''num_waveforms_per_prompt''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : int = 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, 64) , class_embed_type='''simple_projection''' , projection_class_embeddings_input_dim=32 , class_embeddings_concat=__magic_name__ , )
snake_case_ : Any = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__magic_name__ , set_alpha_to_one=__magic_name__ , )
torch.manual_seed(0 )
snake_case_ : List[Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case_ : Any = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
snake_case_ : str = ClapTextModelWithProjection(__magic_name__ )
snake_case_ : Union[str, Any] = RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta''' , model_max_length=77 )
snake_case_ : Optional[int] = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=__magic_name__ , )
snake_case_ : List[str] = SpeechTaHifiGan(__magic_name__ )
snake_case_ : Dict = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''vocoder''': vocoder,
}
return components
def lowerCamelCase (self , __magic_name__ , __magic_name__=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__magic_name__ ).startswith('''mps''' ):
snake_case_ : Tuple = torch.manual_seed(__magic_name__ )
else:
snake_case_ : Union[str, Any] = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
snake_case_ : Optional[int] = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
}
return inputs
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ : Dict = self.get_dummy_components()
snake_case_ : List[str] = AudioLDMPipeline(**__magic_name__ )
snake_case_ : str = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : int = self.get_dummy_inputs(__magic_name__ )
snake_case_ : Tuple = audioldm_pipe(**__magic_name__ )
snake_case_ : int = output.audios[0]
assert audio.ndim == 1
assert len(__magic_name__ ) == 256
snake_case_ : Optional[Any] = audio[:10]
snake_case_ : Tuple = np.array(
[-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = self.get_dummy_components()
snake_case_ : List[Any] = AudioLDMPipeline(**__magic_name__ )
snake_case_ : int = audioldm_pipe.to(__magic_name__ )
snake_case_ : List[Any] = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : Any = self.get_dummy_inputs(__magic_name__ )
snake_case_ : Optional[Any] = 3 * [inputs['''prompt''']]
# forward
snake_case_ : str = audioldm_pipe(**__magic_name__ )
snake_case_ : Optional[Any] = output.audios[0]
snake_case_ : str = self.get_dummy_inputs(__magic_name__ )
snake_case_ : List[Any] = 3 * [inputs.pop('''prompt''' )]
snake_case_ : int = audioldm_pipe.tokenizer(
__magic_name__ , padding='''max_length''' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=__magic_name__ , return_tensors='''pt''' , )
snake_case_ : Tuple = text_inputs['''input_ids'''].to(__magic_name__ )
snake_case_ : str = audioldm_pipe.text_encoder(
__magic_name__ , )
snake_case_ : List[Any] = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
snake_case_ : Tuple = F.normalize(__magic_name__ , dim=-1 )
snake_case_ : List[Any] = prompt_embeds
# forward
snake_case_ : Optional[Any] = audioldm_pipe(**__magic_name__ )
snake_case_ : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.get_dummy_components()
snake_case_ : List[Any] = AudioLDMPipeline(**__magic_name__ )
snake_case_ : Union[str, Any] = audioldm_pipe.to(__magic_name__ )
snake_case_ : List[Any] = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : Optional[Any] = self.get_dummy_inputs(__magic_name__ )
snake_case_ : Tuple = 3 * ['''this is a negative prompt''']
snake_case_ : Union[str, Any] = negative_prompt
snake_case_ : Optional[int] = 3 * [inputs['''prompt''']]
# forward
snake_case_ : str = audioldm_pipe(**__magic_name__ )
snake_case_ : str = output.audios[0]
snake_case_ : List[Any] = self.get_dummy_inputs(__magic_name__ )
snake_case_ : Dict = 3 * [inputs.pop('''prompt''' )]
snake_case_ : List[str] = []
for p in [prompt, negative_prompt]:
snake_case_ : List[str] = audioldm_pipe.tokenizer(
__magic_name__ , padding='''max_length''' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=__magic_name__ , return_tensors='''pt''' , )
snake_case_ : Optional[Any] = text_inputs['''input_ids'''].to(__magic_name__ )
snake_case_ : int = audioldm_pipe.text_encoder(
__magic_name__ , )
snake_case_ : str = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
snake_case_ : List[Any] = F.normalize(__magic_name__ , dim=-1 )
embeds.append(__magic_name__ )
snake_case_ , snake_case_ : Optional[int] = embeds
# forward
snake_case_ : List[Any] = audioldm_pipe(**__magic_name__ )
snake_case_ : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ : Optional[Any] = self.get_dummy_components()
snake_case_ : int = PNDMScheduler(skip_prk_steps=__magic_name__ )
snake_case_ : Optional[int] = AudioLDMPipeline(**__magic_name__ )
snake_case_ : List[str] = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : int = self.get_dummy_inputs(__magic_name__ )
snake_case_ : Union[str, Any] = '''egg cracking'''
snake_case_ : Union[str, Any] = audioldm_pipe(**__magic_name__ , negative_prompt=__magic_name__ )
snake_case_ : Any = output.audios[0]
assert audio.ndim == 1
assert len(__magic_name__ ) == 256
snake_case_ : int = audio[:10]
snake_case_ : Any = np.array(
[-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ : Dict = self.get_dummy_components()
snake_case_ : int = PNDMScheduler(skip_prk_steps=__magic_name__ )
snake_case_ : Dict = AudioLDMPipeline(**__magic_name__ )
snake_case_ : Tuple = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : str = '''A hammer hitting a wooden surface'''
# test num_waveforms_per_prompt=1 (default)
snake_case_ : int = audioldm_pipe(__magic_name__ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
snake_case_ : Optional[int] = 2
snake_case_ : Dict = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
snake_case_ : int = 2
snake_case_ : Optional[Any] = audioldm_pipe(__magic_name__ , num_inference_steps=2 , num_waveforms_per_prompt=__magic_name__ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
snake_case_ : Any = 2
snake_case_ : Tuple = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=__magic_name__ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ : List[str] = self.get_dummy_components()
snake_case_ : Dict = AudioLDMPipeline(**__magic_name__ )
snake_case_ : Tuple = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : List[Any] = audioldm_pipe.vocoder.config.sampling_rate
snake_case_ : int = self.get_dummy_inputs(__magic_name__ )
snake_case_ : str = audioldm_pipe(audio_length_in_s=0.016 , **__magic_name__ )
snake_case_ : List[str] = output.audios[0]
assert audio.ndim == 1
assert len(__magic_name__ ) / vocoder_sampling_rate == 0.016
snake_case_ : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **__magic_name__ )
snake_case_ : Any = output.audios[0]
assert audio.ndim == 1
assert len(__magic_name__ ) / vocoder_sampling_rate == 0.032
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.get_dummy_components()
snake_case_ : Union[str, Any] = AudioLDMPipeline(**__magic_name__ )
snake_case_ : Optional[Any] = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : int = ['''hey''']
snake_case_ : str = audioldm_pipe(__magic_name__ , num_inference_steps=1 )
snake_case_ : int = output.audios.shape
assert audio_shape == (1, 256)
snake_case_ : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
snake_case_ : str = SpeechTaHifiGan(__magic_name__ ).to(__magic_name__ )
snake_case_ : Union[str, Any] = audioldm_pipe(__magic_name__ , num_inference_steps=1 )
snake_case_ : Any = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
self._test_inference_batch_single_identical(test_mean_pixel_difference=__magic_name__ )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__magic_name__ )
@slow
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase (self , __magic_name__ , __magic_name__="cpu" , __magic_name__=torch.floataa , __magic_name__=0 ) -> Dict:
'''simple docstring'''
snake_case_ : int = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
snake_case_ : Optional[int] = np.random.RandomState(__magic_name__ ).standard_normal((1, 8, 128, 16) )
snake_case_ : Optional[Any] = torch.from_numpy(__magic_name__ ).to(device=__magic_name__ , dtype=__magic_name__ )
snake_case_ : Dict = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 2.5,
}
return inputs
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''' )
snake_case_ : str = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : Tuple = self.get_inputs(__magic_name__ )
snake_case_ : Optional[Any] = 25
snake_case_ : Any = audioldm_pipe(**__magic_name__ ).audios[0]
assert audio.ndim == 1
assert len(__magic_name__ ) == 8_1920
snake_case_ : Union[str, Any] = audio[7_7230:7_7240]
snake_case_ : str = np.array(
[-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315] )
snake_case_ : Union[str, Any] = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''' )
snake_case_ : List[Any] = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
snake_case_ : Dict = audioldm_pipe.to(__magic_name__ )
audioldm_pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : List[Any] = self.get_inputs(__magic_name__ )
snake_case_ : List[Any] = audioldm_pipe(**__magic_name__ ).audios[0]
assert audio.ndim == 1
assert len(__magic_name__ ) == 8_1920
snake_case_ : Any = audio[2_7780:2_7790]
snake_case_ : List[str] = np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212] )
snake_case_ : List[str] = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 60 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 1 |
import numpy as np
def lowerCamelCase_ ( _UpperCamelCase ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def lowerCamelCase_ ( _UpperCamelCase ) -> np.array:
"""simple docstring"""
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 60 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Any = (boundary[1] - boundary[0]) / steps
snake_case_ : List[Any] = boundary[0]
snake_case_ : Tuple = boundary[1]
snake_case_ : Any = make_points(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
snake_case_ : int = 0.0
y += (h / 2.0) * f(_UpperCamelCase )
for i in x_i:
# print(i)
y += h * f(_UpperCamelCase )
y += (h / 2.0) * f(_UpperCamelCase )
return y
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = a + h
while x < (b - h):
yield x
snake_case_ : List[Any] = x + h
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]: # enter your function here
"""simple docstring"""
snake_case_ : Any = (x - 0) * (x - 0)
return y
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : List[str] = 0.0 # Lower bound of integration
snake_case_ : Dict = 1.0 # Upper bound of integration
snake_case_ : List[Any] = 10.0 # define number of steps or resolution
snake_case_ : Tuple = [a, b] # define boundary of integration
snake_case_ : str = method_a(_UpperCamelCase , _UpperCamelCase )
print(f'''y = {y}''' )
if __name__ == "__main__":
main()
| 60 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , ) -> str:
"""simple docstring"""
snake_case_ : List[str] = {}
if train_file is not None:
snake_case_ : str = [train_file]
if eval_file is not None:
snake_case_ : Union[str, Any] = [eval_file]
if test_file is not None:
snake_case_ : Dict = [test_file]
snake_case_ : List[str] = datasets.load_dataset('''csv''' , data_files=_UpperCamelCase )
snake_case_ : Optional[int] = list(ds[list(files.keys() )[0]].features.keys() )
snake_case_ : int = features_name.pop(_UpperCamelCase )
snake_case_ : int = list(set(ds[list(files.keys() )[0]][label_name] ) )
snake_case_ : Union[str, Any] = {label: i for i, label in enumerate(_UpperCamelCase )}
snake_case_ : List[str] = tokenizer.model_input_names
snake_case_ : Union[str, Any] = {}
if len(_UpperCamelCase ) == 1:
for k in files.keys():
snake_case_ : Tuple = ds[k].map(
lambda _UpperCamelCase : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=_UpperCamelCase , max_length=_UpperCamelCase , padding='''max_length''' ) , batched=_UpperCamelCase , )
elif len(_UpperCamelCase ) == 2:
for k in files.keys():
snake_case_ : str = ds[k].map(
lambda _UpperCamelCase : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=_UpperCamelCase , max_length=_UpperCamelCase , padding='''max_length''' , ) , batched=_UpperCamelCase , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
snake_case_ : Dict = {k: v for k, v in ex.items() if k in input_names}
snake_case_ : List[Any] = labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
snake_case_ : Any = {k: v for k, v in ex.items() if k in input_names}
snake_case_ : List[Any] = labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
snake_case_ : List[Any] = {k: v for k, v in ex.items() if k in input_names}
snake_case_ : Tuple = labelaid[ex[label_name]]
yield (d, label)
snake_case_ : Optional[Any] = (
tf.data.Dataset.from_generator(
_UpperCamelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
snake_case_ : str = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
snake_case_ : str = (
tf.data.Dataset.from_generator(
_UpperCamelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
snake_case_ : Any = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
snake_case_ : Optional[int] = (
tf.data.Dataset.from_generator(
_UpperCamelCase , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
snake_case_ : Any = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
lowerCamelCase_ : int = field(metadata={'''help''': '''Which column contains the label'''} )
lowerCamelCase_ : str = field(default=_a, metadata={'''help''': '''The path of the training file'''} )
lowerCamelCase_ : Optional[str] = field(default=_a, metadata={'''help''': '''The path of the development file'''} )
lowerCamelCase_ : Optional[str] = field(default=_a, metadata={'''help''': '''The path of the test file'''} )
lowerCamelCase_ : int = 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.'''
)
}, )
lowerCamelCase_ : bool = field(
default=_a, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
@dataclass
class __lowerCAmelCase :
lowerCamelCase_ : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
lowerCamelCase_ : Optional[str] = field(
default=_a, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
lowerCamelCase_ : Optional[str] = field(
default=_a, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
lowerCamelCase_ : bool = field(default=_a, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
lowerCamelCase_ : Optional[str] = field(
default=_a, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Optional[int] = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
snake_case_ , snake_case_ , snake_case_ : Optional[int] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO , )
logger.info(
f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '''
f'''16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Optional[Any] = get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=_UpperCamelCase , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
snake_case_ : Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(_UpperCamelCase ) , labelaid=_UpperCamelCase , idalabel={id: label for label, id in labelaid.items()} , finetuning_task='''text-classification''' , cache_dir=model_args.cache_dir , )
with training_args.strategy.scope():
snake_case_ : str = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool('''.bin''' in model_args.model_name_or_path ) , config=_UpperCamelCase , cache_dir=model_args.cache_dir , )
def compute_metrics(_UpperCamelCase ) -> Dict:
snake_case_ : Union[str, Any] = np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
snake_case_ : List[Any] = TFTrainer(
model=_UpperCamelCase , args=_UpperCamelCase , train_dataset=_UpperCamelCase , eval_dataset=_UpperCamelCase , compute_metrics=_UpperCamelCase , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
snake_case_ : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
snake_case_ : Dict = trainer.evaluate()
snake_case_ : Dict = os.path.join(training_args.output_dir , '''eval_results.txt''' )
with open(_UpperCamelCase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(f''' {key} = {value}''' )
writer.write(f'''{key} = {value}\n''' )
results.update(_UpperCamelCase )
return results
if __name__ == "__main__":
main()
| 60 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class __lowerCAmelCase ( _a ):
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
return Dataset.from_dict(__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : int = self._create_example_records()
snake_case_ : List[str] = Dataset.from_list(__magic_name__ )
self.assertListEqual(dset.column_names , ['''col_1''', '''col_2'''] )
for i, r in enumerate(__magic_name__ ):
self.assertDictEqual(__magic_name__ , example_records[i] )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Dict = self._create_example_records()
snake_case_ : Tuple = Dataset.from_list(__magic_name__ )
snake_case_ : Optional[int] = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def lowerCamelCase (self ) -> Any: # checks what happens with missing columns
'''simple docstring'''
snake_case_ : Dict = [{'''col_1''': 1}, {'''col_2''': '''x'''}]
snake_case_ : Any = Dataset.from_list(__magic_name__ )
self.assertDictEqual(dset[0] , {'''col_1''': 1} )
self.assertDictEqual(dset[1] , {'''col_1''': None} ) # NB: first record is used for columns
def lowerCamelCase (self ) -> Optional[int]: # checks if the type can be inferred from the second record
'''simple docstring'''
snake_case_ : int = [{'''col_1''': []}, {'''col_1''': [1, 2]}]
snake_case_ : str = Dataset.from_list(__magic_name__ )
self.assertEqual(dset.info.features['''col_1'''] , Sequence(Value('''int64''' ) ) )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Union[str, Any] = Dataset.from_list([] )
self.assertEqual(len(__magic_name__ ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 60 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Dict = '''cvt'''
def __init__(self , __magic_name__=3 , __magic_name__=[7, 3, 3] , __magic_name__=[4, 2, 2] , __magic_name__=[2, 1, 1] , __magic_name__=[64, 192, 384] , __magic_name__=[1, 3, 6] , __magic_name__=[1, 2, 10] , __magic_name__=[4.0, 4.0, 4.0] , __magic_name__=[0.0, 0.0, 0.0] , __magic_name__=[0.0, 0.0, 0.0] , __magic_name__=[0.0, 0.0, 0.1] , __magic_name__=[True, True, True] , __magic_name__=[False, False, True] , __magic_name__=["dw_bn", "dw_bn", "dw_bn"] , __magic_name__=[3, 3, 3] , __magic_name__=[1, 1, 1] , __magic_name__=[2, 2, 2] , __magic_name__=[1, 1, 1] , __magic_name__=[1, 1, 1] , __magic_name__=0.02 , __magic_name__=1e-12 , **__magic_name__ , ) -> List[str]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : int = num_channels
snake_case_ : str = patch_sizes
snake_case_ : Dict = patch_stride
snake_case_ : str = patch_padding
snake_case_ : List[str] = embed_dim
snake_case_ : int = num_heads
snake_case_ : Union[str, Any] = depth
snake_case_ : Union[str, Any] = mlp_ratio
snake_case_ : List[str] = attention_drop_rate
snake_case_ : Tuple = drop_rate
snake_case_ : Any = drop_path_rate
snake_case_ : Optional[int] = qkv_bias
snake_case_ : Tuple = cls_token
snake_case_ : Dict = qkv_projection_method
snake_case_ : Dict = kernel_qkv
snake_case_ : List[Any] = padding_kv
snake_case_ : Dict = stride_kv
snake_case_ : List[str] = padding_q
snake_case_ : List[Any] = stride_q
snake_case_ : Dict = initializer_range
snake_case_ : Dict = layer_norm_eps
| 60 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ) -> float:
"""simple docstring"""
snake_case_ : Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('''All input parameters must be positive''' )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError('''Relative densities cannot be greater than one''' )
else:
snake_case_ : Optional[Any] = 1 - (matter_density + radiation_density + dark_energy)
snake_case_ : Union[str, Any] = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
snake_case_ : Tuple = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
lowerCAmelCase_ = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 60 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 60 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCAmelCase_ = TypeVar('''T''')
class __lowerCAmelCase ( Generic[T] ):
def __init__(self , __magic_name__ , __magic_name__ ) -> None:
'''simple docstring'''
snake_case_ : Any | T = None
snake_case_ : int = len(__magic_name__ )
snake_case_ : list[T] = [any_type for _ in range(self.N )] + arr
snake_case_ : Optional[int] = fnc
self.build()
def lowerCamelCase (self ) -> None:
'''simple docstring'''
for p in range(self.N - 1 , 0 , -1 ):
snake_case_ : Optional[Any] = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> None:
'''simple docstring'''
p += self.N
snake_case_ : Dict = v
while p > 1:
snake_case_ : List[str] = p // 2
snake_case_ : int = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> T | None: # noqa: E741
'''simple docstring'''
snake_case_ , snake_case_ : int = l + self.N, r + self.N
snake_case_ : T | None = None
while l <= r:
if l % 2 == 1:
snake_case_ : Optional[Any] = self.st[l] if res is None else self.fn(__magic_name__ , self.st[l] )
if r % 2 == 0:
snake_case_ : Optional[int] = self.st[r] if res is None else self.fn(__magic_name__ , self.st[r] )
snake_case_ , snake_case_ : Dict = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
lowerCAmelCase_ = [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]
lowerCAmelCase_ = {
0: 7,
1: 2,
2: 6,
3: -1_4,
4: 5,
5: 4,
6: 7,
7: -1_0,
8: 9,
9: 1_0,
1_0: 1_2,
1_1: 1,
}
lowerCAmelCase_ = SegmentTree(test_array, min)
lowerCAmelCase_ = SegmentTree(test_array, max)
lowerCAmelCase_ = SegmentTree(test_array, lambda a, b: a + b)
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
for j in range(_UpperCamelCase , len(_UpperCamelCase ) ):
snake_case_ : Dict = reduce(_UpperCamelCase , test_array[i : j + 1] )
snake_case_ : Tuple = reduce(_UpperCamelCase , test_array[i : j + 1] )
snake_case_ : Tuple = reduce(lambda _UpperCamelCase , _UpperCamelCase : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(_UpperCamelCase , _UpperCamelCase )
assert max_range == max_segment_tree.query(_UpperCamelCase , _UpperCamelCase )
assert sum_range == sum_segment_tree.query(_UpperCamelCase , _UpperCamelCase )
test_all_segments()
for index, value in test_updates.items():
lowerCAmelCase_ = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 60 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=7 , __magic_name__=True , __magic_name__=True , __magic_name__=True , __magic_name__=True , __magic_name__=99 , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=512 , __magic_name__=16 , __magic_name__=2 , __magic_name__=0.02 , __magic_name__=4 , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Any = seq_length
snake_case_ : Dict = is_training
snake_case_ : Union[str, Any] = use_attention_mask
snake_case_ : int = use_token_type_ids
snake_case_ : Optional[Any] = use_labels
snake_case_ : List[Any] = vocab_size
snake_case_ : Optional[int] = hidden_size
snake_case_ : Tuple = num_hidden_layers
snake_case_ : Optional[int] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : List[str] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Dict = max_position_embeddings
snake_case_ : int = type_vocab_size
snake_case_ : int = type_sequence_label_size
snake_case_ : str = initializer_range
snake_case_ : str = num_choices
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Optional[int] = None
if self.use_attention_mask:
snake_case_ : str = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Dict = None
if self.use_token_type_ids:
snake_case_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : str = 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=__magic_name__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : List[Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Optional[Any] = True
snake_case_ : Any = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : Tuple = True
lowerCamelCase_ : Union[str, Any] = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Any = FlaxBertModelTester(self )
@slow
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = FlaxBertModel.from_pretrained('''bert-base-cased''' )
snake_case_ : Optional[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__magic_name__ )
| 60 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : Any = IFInpaintingPipeline
lowerCamelCase_ : Dict = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
lowerCamelCase_ : Union[str, Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCamelCase_ : Optional[Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
return self._get_dummy_components()
def lowerCamelCase (self , __magic_name__ , __magic_name__=0 ) -> Tuple:
'''simple docstring'''
if str(__magic_name__ ).startswith('''mps''' ):
snake_case_ : Union[str, Any] = torch.manual_seed(__magic_name__ )
else:
snake_case_ : str = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
snake_case_ : Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
snake_case_ : str = floats_tensor((1, 3, 32, 32) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
snake_case_ : Dict = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''mask_image''': mask_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 lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
self._test_save_load_local()
def lowerCamelCase (self ) -> str:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 60 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCAmelCase_ = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase_ = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = numpy.dtype(numpy.uintaa ).newbyteorder('''>''' )
return numpy.frombuffer(bytestream.read(4 ) , dtype=_UpperCamelCase )[0]
@deprecated(_UpperCamelCase , '''Please use tf.data to implement this functionality.''' )
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=_UpperCamelCase ) as bytestream:
snake_case_ : List[Any] = _readaa(_UpperCamelCase )
if magic != 2_051:
raise ValueError(
'''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name) )
snake_case_ : Any = _readaa(_UpperCamelCase )
snake_case_ : str = _readaa(_UpperCamelCase )
snake_case_ : Union[str, Any] = _readaa(_UpperCamelCase )
snake_case_ : str = bytestream.read(rows * cols * num_images )
snake_case_ : Optional[Any] = numpy.frombuffer(_UpperCamelCase , dtype=numpy.uinta )
snake_case_ : Optional[Any] = data.reshape(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , 1 )
return data
@deprecated(_UpperCamelCase , '''Please use tf.one_hot on tensors.''' )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : List[str] = labels_dense.shape[0]
snake_case_ : Any = numpy.arange(_UpperCamelCase ) * num_classes
snake_case_ : str = numpy.zeros((num_labels, num_classes) )
snake_case_ : Dict = 1
return labels_one_hot
@deprecated(_UpperCamelCase , '''Please use tf.data to implement this functionality.''' )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=False , _UpperCamelCase=10 ) -> Union[str, Any]:
"""simple docstring"""
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=_UpperCamelCase ) as bytestream:
snake_case_ : Union[str, Any] = _readaa(_UpperCamelCase )
if magic != 2_049:
raise ValueError(
'''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name) )
snake_case_ : Union[str, Any] = _readaa(_UpperCamelCase )
snake_case_ : List[Any] = bytestream.read(_UpperCamelCase )
snake_case_ : Optional[Any] = numpy.frombuffer(_UpperCamelCase , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(_UpperCamelCase , _UpperCamelCase )
return labels
class __lowerCAmelCase :
@deprecated(
__magic_name__ , '''Please use alternatives such as official/mnist/_DataSet.py'''
''' from tensorflow/models.''' , )
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__=False , __magic_name__=False , __magic_name__=dtypes.floataa , __magic_name__=True , __magic_name__=None , ) -> str:
'''simple docstring'''
snake_case_ , snake_case_ : Optional[Any] = random_seed.get_seed(__magic_name__ )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
snake_case_ : List[Any] = dtypes.as_dtype(__magic_name__ ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype )
if fake_data:
snake_case_ : Any = 1_0000
snake_case_ : Tuple = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
snake_case_ : List[str] = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
snake_case_ : List[Any] = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
snake_case_ : Any = images.astype(numpy.floataa )
snake_case_ : str = numpy.multiply(__magic_name__ , 1.0 / 255.0 )
snake_case_ : Optional[int] = images
snake_case_ : Optional[int] = labels
snake_case_ : Optional[int] = 0
snake_case_ : List[str] = 0
@property
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return self._images
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return self._labels
@property
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
return self._num_examples
@property
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
return self._epochs_completed
def lowerCamelCase (self , __magic_name__ , __magic_name__=False , __magic_name__=True ) -> Union[str, Any]:
'''simple docstring'''
if fake_data:
snake_case_ : List[Any] = [1] * 784
snake_case_ : Union[str, Any] = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(__magic_name__ )],
[fake_label for _ in range(__magic_name__ )],
)
snake_case_ : str = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
snake_case_ : Union[str, Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(__magic_name__ )
snake_case_ : Any = self.images[perma]
snake_case_ : Tuple = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
snake_case_ : Optional[Any] = self._num_examples - start
snake_case_ : str = self._images[start : self._num_examples]
snake_case_ : Optional[int] = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
snake_case_ : Dict = numpy.arange(self._num_examples )
numpy.random.shuffle(__magic_name__ )
snake_case_ : List[Any] = self.images[perm]
snake_case_ : Union[str, Any] = self.labels[perm]
# Start next epoch
snake_case_ : str = 0
snake_case_ : Optional[int] = batch_size - rest_num_examples
snake_case_ : Optional[int] = self._index_in_epoch
snake_case_ : List[Any] = self._images[start:end]
snake_case_ : Tuple = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
snake_case_ : Dict = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(_UpperCamelCase , '''Please write your own downloading logic.''' )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
if not gfile.Exists(_UpperCamelCase ):
gfile.MakeDirs(_UpperCamelCase )
snake_case_ : Optional[int] = os.path.join(_UpperCamelCase , _UpperCamelCase )
if not gfile.Exists(_UpperCamelCase ):
urllib.request.urlretrieve(_UpperCamelCase , _UpperCamelCase ) # noqa: S310
with gfile.GFile(_UpperCamelCase ) as f:
snake_case_ : Optional[int] = f.size()
print('''Successfully downloaded''' , _UpperCamelCase , _UpperCamelCase , '''bytes.''' )
return filepath
@deprecated(
_UpperCamelCase , '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''' )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=dtypes.floataa , _UpperCamelCase=True , _UpperCamelCase=5_000 , _UpperCamelCase=None , _UpperCamelCase=DEFAULT_SOURCE_URL , ) -> Dict:
"""simple docstring"""
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=_UpperCamelCase , one_hot=_UpperCamelCase , dtype=_UpperCamelCase , seed=_UpperCamelCase )
snake_case_ : List[Any] = fake()
snake_case_ : Tuple = fake()
snake_case_ : str = fake()
return _Datasets(train=_UpperCamelCase , validation=_UpperCamelCase , test=_UpperCamelCase )
if not source_url: # empty string check
snake_case_ : int = DEFAULT_SOURCE_URL
snake_case_ : List[str] = '''train-images-idx3-ubyte.gz'''
snake_case_ : Optional[int] = '''train-labels-idx1-ubyte.gz'''
snake_case_ : int = '''t10k-images-idx3-ubyte.gz'''
snake_case_ : Tuple = '''t10k-labels-idx1-ubyte.gz'''
snake_case_ : List[str] = _maybe_download(
_UpperCamelCase , _UpperCamelCase , source_url + train_images_file )
with gfile.Open(_UpperCamelCase , '''rb''' ) as f:
snake_case_ : List[Any] = _extract_images(_UpperCamelCase )
snake_case_ : Optional[Any] = _maybe_download(
_UpperCamelCase , _UpperCamelCase , source_url + train_labels_file )
with gfile.Open(_UpperCamelCase , '''rb''' ) as f:
snake_case_ : Optional[Any] = _extract_labels(_UpperCamelCase , one_hot=_UpperCamelCase )
snake_case_ : Any = _maybe_download(
_UpperCamelCase , _UpperCamelCase , source_url + test_images_file )
with gfile.Open(_UpperCamelCase , '''rb''' ) as f:
snake_case_ : str = _extract_images(_UpperCamelCase )
snake_case_ : str = _maybe_download(
_UpperCamelCase , _UpperCamelCase , source_url + test_labels_file )
with gfile.Open(_UpperCamelCase , '''rb''' ) as f:
snake_case_ : Tuple = _extract_labels(_UpperCamelCase , one_hot=_UpperCamelCase )
if not 0 <= validation_size <= len(_UpperCamelCase ):
snake_case_ : Tuple = (
'''Validation size should be between 0 and '''
f'''{len(_UpperCamelCase )}. Received: {validation_size}.'''
)
raise ValueError(_UpperCamelCase )
snake_case_ : Dict = train_images[:validation_size]
snake_case_ : Union[str, Any] = train_labels[:validation_size]
snake_case_ : int = train_images[validation_size:]
snake_case_ : List[Any] = train_labels[validation_size:]
snake_case_ : Tuple = {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}
snake_case_ : Union[str, Any] = _DataSet(_UpperCamelCase , _UpperCamelCase , **_UpperCamelCase )
snake_case_ : Optional[Any] = _DataSet(_UpperCamelCase , _UpperCamelCase , **_UpperCamelCase )
snake_case_ : Tuple = _DataSet(_UpperCamelCase , _UpperCamelCase , **_UpperCamelCase )
return _Datasets(train=_UpperCamelCase , validation=_UpperCamelCase , test=_UpperCamelCase )
| 60 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase_ ( _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ , snake_case_ : Optional[Any] = analyze_text(_UpperCamelCase )
snake_case_ : Any = list(''' ''' + ascii_lowercase )
# what is our total sum of probabilities.
snake_case_ : Any = sum(single_char_strings.values() )
# one length string
snake_case_ : Optional[int] = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
snake_case_ : List[Any] = single_char_strings[ch]
snake_case_ : Optional[int] = my_str / all_sum
my_fir_sum += prob * math.loga(_UpperCamelCase ) # entropy formula.
# print entropy
print(f'''{round(-1 * my_fir_sum ):.1f}''' )
# two len string
snake_case_ : Tuple = sum(two_char_strings.values() )
snake_case_ : int = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
snake_case_ : Dict = cha + cha
if sequence in two_char_strings:
snake_case_ : Optional[Any] = two_char_strings[sequence]
snake_case_ : int = int(_UpperCamelCase ) / all_sum
my_sec_sum += prob * math.loga(_UpperCamelCase )
# print second entropy
print(f'''{round(-1 * my_sec_sum ):.1f}''' )
# print the difference between them
print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}''' )
def lowerCamelCase_ ( _UpperCamelCase ) -> tuple[dict, dict]:
"""simple docstring"""
snake_case_ : Optional[Any] = Counter() # type: ignore
snake_case_ : Any = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(_UpperCamelCase ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def lowerCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 60 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
lowerCAmelCase_ = None
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCAmelCase_ = {
'''vocab_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
),
},
}
lowerCAmelCase_ = {
'''moussaKam/mbarthez''': 1_0_2_4,
'''moussaKam/barthez''': 1_0_2_4,
'''moussaKam/barthez-orangesum-title''': 1_0_2_4,
}
lowerCAmelCase_ = '''▁'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = VOCAB_FILES_NAMES
lowerCamelCase_ : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ : List[Any] = ['''input_ids''', '''attention_mask''']
lowerCamelCase_ : Union[str, Any] = BarthezTokenizer
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__="<s>" , __magic_name__="</s>" , __magic_name__="</s>" , __magic_name__="<s>" , __magic_name__="<unk>" , __magic_name__="<pad>" , __magic_name__="<mask>" , **__magic_name__ , ) -> str:
'''simple docstring'''
snake_case_ : Any = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else mask_token
super().__init__(
__magic_name__ , tokenizer_file=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , unk_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , **__magic_name__ , )
snake_case_ : Tuple = vocab_file
snake_case_ : str = False if not self.vocab_file else True
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ : Tuple = [self.cls_token_id]
snake_case_ : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> List[int]:
'''simple docstring'''
snake_case_ : List[Any] = [self.sep_token_id]
snake_case_ : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None ) -> Tuple[str]:
'''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(__magic_name__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case_ : str = os.path.join(
__magic_name__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__magic_name__ ):
copyfile(self.vocab_file , __magic_name__ )
return (out_vocab_file,)
| 60 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('''TEST_SAGEMAKER''', '''False''' ) ) is not True, reason='''Skipping test because should only be run when releasing minor transformers version''', )
@pytest.mark.usefixtures('''sm_env''' )
@parameterized_class(
[
{
'''framework''': '''pytorch''',
'''script''': '''run_glue.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.p3.16xlarge''',
'''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
},
{
'''framework''': '''pytorch''',
'''script''': '''run_ddp.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.p3.16xlarge''',
'''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
},
{
'''framework''': '''tensorflow''',
'''script''': '''run_tf_dist.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.p3.16xlarge''',
'''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
},
] )
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split() , encoding='''utf-8''' , check=__magic_name__ , )
assert hasattr(self , '''env''' )
def lowerCamelCase (self , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = F'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
# distributed data settings
snake_case_ : int = {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=__magic_name__ , instance_count=__magic_name__ , instance_type=self.instance_type , debugger_hook_config=__magic_name__ , hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=__magic_name__ , py_version='''py36''' , )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
TrainingJobAnalytics(__magic_name__ ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )
@parameterized.expand([(2,)] )
def lowerCamelCase (self , __magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : str = self.create_estimator(__magic_name__ )
# run training
estimator.fit()
# result dataframe
snake_case_ : List[Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
snake_case_ : List[str] = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
snake_case_ : Optional[Any] = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
snake_case_ : Any = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F'''{estimator.latest_training_job.name}.json''' , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , __magic_name__ )
| 60 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = 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(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
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(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Dict = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch '''
'''helper utility that will spawn up '''
'''multiple distributed processes'''
) )
# Optional arguments for the launch helper
parser.add_argument('''--num_cores''' , type=_UpperCamelCase , default=1 , help='''Number of TPU cores to use (1 or 8).''' )
# positional
parser.add_argument(
'''training_script''' , type=_UpperCamelCase , help=(
'''The full path to the single TPU training '''
'''program/script to be launched in parallel, '''
'''followed by all the arguments for the '''
'''training script'''
) , )
# rest from the training program
parser.add_argument('''training_script_args''' , nargs=_UpperCamelCase )
return parser.parse_args()
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Dict = parse_args()
# Import training_script as a module.
snake_case_ : str = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
snake_case_ : str = script_fpath.stem
snake_case_ : Optional[int] = importlib.import_module(_UpperCamelCase )
# Patch sys.argv
snake_case_ : Any = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 60 |
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 ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = 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 lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : 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=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = 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=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
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''' , __magic_name__ , )
| 60 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
lowerCAmelCase_ = ''''''
if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
class __lowerCAmelCase ( tr.AbstractTransform ):
def __init__(self , __magic_name__ = " " ) -> int:
'''simple docstring'''
snake_case_ : Dict = sentence_delimiter
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
return list(__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Tuple:
'''simple docstring'''
snake_case_ : Any = []
for sent_idx, sentence in enumerate(__magic_name__ ):
chars.extend(self.process_string(__magic_name__ ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__magic_name__ ) - 1:
chars.append(self.sentence_delimiter )
return chars
lowerCAmelCase_ = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
lowerCAmelCase_ = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
lowerCAmelCase_ = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
lowerCAmelCase_ = '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.
CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.
Character error rate can be computed as:
CER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).
CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''
lowerCAmelCase_ = '''
Computes CER score of transcribed segments against references.
Args:
references: list of references for each speech input.
predictions: list of transcribtions to score.
concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
(float): the character error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> cer = datasets.load_metric("cer")
>>> cer_score = cer.compute(predictions=predictions, references=references)
>>> print(cer_score)
0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/jitsi/jiwer/'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
'''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
] , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=False ) -> Dict:
'''simple docstring'''
if concatenate_texts:
return jiwer.compute_measures(
__magic_name__ , __magic_name__ , truth_transform=__magic_name__ , hypothesis_transform=__magic_name__ , )["wer"]
snake_case_ : List[Any] = 0
snake_case_ : int = 0
for prediction, reference in zip(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = jiwer.compute_measures(
__magic_name__ , __magic_name__ , truth_transform=__magic_name__ , hypothesis_transform=__magic_name__ , )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 60 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 1 |
from math import factorial
lowerCAmelCase_ = {str(d): factorial(d) for d in range(1_0)}
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(_UpperCamelCase ) )
def lowerCamelCase_ ( ) -> int:
"""simple docstring"""
snake_case_ : Optional[int] = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , _UpperCamelCase ) if sum_of_digit_factorial(_UpperCamelCase ) == i )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 60 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 1 |
from __future__ import annotations
from collections import deque
class __lowerCAmelCase :
def __init__(self , __magic_name__ ) -> Tuple:
'''simple docstring'''
snake_case_ : list[dict] = []
self.adlist.append(
{'''value''': '''''', '''next_states''': [], '''fail_state''': 0, '''output''': []} )
for keyword in keywords:
self.add_keyword(__magic_name__ )
self.set_fail_transitions()
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> int | None:
'''simple docstring'''
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def lowerCamelCase (self , __magic_name__ ) -> None:
'''simple docstring'''
snake_case_ : Tuple = 0
for character in keyword:
snake_case_ : int = self.find_next_state(__magic_name__ , __magic_name__ )
if next_state is None:
self.adlist.append(
{
'''value''': character,
'''next_states''': [],
'''fail_state''': 0,
'''output''': [],
} )
self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
snake_case_ : int = len(self.adlist ) - 1
else:
snake_case_ : Dict = next_state
self.adlist[current_state]["output"].append(__magic_name__ )
def lowerCamelCase (self ) -> None:
'''simple docstring'''
snake_case_ : deque = deque()
for node in self.adlist[0]["next_states"]:
q.append(__magic_name__ )
snake_case_ : Union[str, Any] = 0
while q:
snake_case_ : Optional[int] = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(__magic_name__ )
snake_case_ : List[Any] = self.adlist[r]['''fail_state''']
while (
self.find_next_state(__magic_name__ , self.adlist[child]['''value'''] ) is None
and state != 0
):
snake_case_ : str = self.adlist[state]['''fail_state''']
snake_case_ : Tuple = self.find_next_state(
__magic_name__ , self.adlist[child]['''value'''] )
if self.adlist[child]["fail_state"] is None:
snake_case_ : Union[str, Any] = 0
snake_case_ : Optional[Any] = (
self.adlist[child]['''output''']
+ self.adlist[self.adlist[child]['''fail_state''']]['''output''']
)
def lowerCamelCase (self , __magic_name__ ) -> dict[str, list[int]]:
'''simple docstring'''
snake_case_ : dict = {} # returns a dict with keywords and list of its occurrences
snake_case_ : Optional[int] = 0
for i in range(len(__magic_name__ ) ):
while (
self.find_next_state(__magic_name__ , string[i] ) is None
and current_state != 0
):
snake_case_ : Tuple = self.adlist[current_state]['''fail_state''']
snake_case_ : Union[str, Any] = self.find_next_state(__magic_name__ , string[i] )
if next_state is None:
snake_case_ : int = 0
else:
snake_case_ : List[Any] = next_state
for key in self.adlist[current_state]["output"]:
if key not in result:
snake_case_ : int = []
result[key].append(i - len(__magic_name__ ) + 1 )
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 1 |
lowerCAmelCase_ = range(2, 2_0 + 1)
lowerCAmelCase_ = [1_0**k for k in range(ks[-1] + 1)]
lowerCAmelCase_ = {}
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : List[Any] = sum(a_i[j] for j in range(_UpperCamelCase , len(_UpperCamelCase ) ) )
snake_case_ : Optional[Any] = sum(a_i[j] * base[j] for j in range(min(len(_UpperCamelCase ) , _UpperCamelCase ) ) )
snake_case_ , snake_case_ : Any = 0, 0
snake_case_ : List[str] = n - i
snake_case_ : Any = memo.get(_UpperCamelCase )
if sub_memo is not None:
snake_case_ : Any = sub_memo.get(_UpperCamelCase )
if jumps is not None and len(_UpperCamelCase ) > 0:
# find and make the largest jump without going over
snake_case_ : int = -1
for _k in range(len(_UpperCamelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
snake_case_ : List[Any] = _k
break
if max_jump >= 0:
snake_case_ , snake_case_ , snake_case_ : Union[str, Any] = jumps[max_jump]
# since the difference between jumps is cached, add c
snake_case_ : List[str] = diff + c
for j in range(min(_UpperCamelCase , len(_UpperCamelCase ) ) ):
snake_case_ , snake_case_ : Optional[Any] = divmod(_UpperCamelCase , 10 )
if new_c > 0:
add(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
else:
snake_case_ : List[Any] = []
else:
snake_case_ : Optional[Any] = {c: []}
snake_case_ : Union[str, Any] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
snake_case_ , snake_case_ : List[str] = next_term(_UpperCamelCase , k - 1 , i + dn , _UpperCamelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
snake_case_ , snake_case_ : str = compute(_UpperCamelCase , _UpperCamelCase , i + dn , _UpperCamelCase )
diff += _diff
dn += terms_jumped
snake_case_ : str = sub_memo[c]
# keep jumps sorted by # of terms skipped
snake_case_ : List[Any] = 0
while j < len(_UpperCamelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_UpperCamelCase , (diff, dn, k) )
return (diff, dn)
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
if i >= n:
return 0, i
if k > len(_UpperCamelCase ):
a_i.extend([0 for _ in range(k - len(_UpperCamelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
snake_case_ : Union[str, Any] = i
snake_case_ , snake_case_ , snake_case_ : List[Any] = 0, 0, 0
for j in range(len(_UpperCamelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
snake_case_ : Union[str, Any] = ds_c + ds_b
diff += addend
snake_case_ : int = 0
for j in range(_UpperCamelCase ):
snake_case_ : List[str] = a_i[j] + addend
snake_case_ , snake_case_ : Optional[Any] = divmod(_UpperCamelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
return diff, i - start_i
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
for j in range(_UpperCamelCase , len(_UpperCamelCase ) ):
snake_case_ : Any = digits[j] + addend
if s >= 10:
snake_case_ , snake_case_ : Tuple = divmod(_UpperCamelCase , 10 )
snake_case_ : Optional[int] = addend // 10 + quotient
else:
snake_case_ : Optional[int] = s
snake_case_ : Optional[Any] = addend // 10
if addend == 0:
break
while addend > 0:
snake_case_ , snake_case_ : int = divmod(_UpperCamelCase , 10 )
digits.append(_UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase = 10**15 ) -> int:
"""simple docstring"""
snake_case_ : Tuple = [1]
snake_case_ : List[Any] = 1
snake_case_ : Union[str, Any] = 0
while True:
snake_case_ , snake_case_ : List[Any] = next_term(_UpperCamelCase , 20 , i + dn , _UpperCamelCase )
dn += terms_jumped
if dn == n - i:
break
snake_case_ : Any = 0
for j in range(len(_UpperCamelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F'''{solution() = }''')
| 60 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowerCAmelCase_ = '''__DUMMY_TRANSFORMERS_USER__'''
lowerCAmelCase_ = '''Dummy User'''
lowerCAmelCase_ = '''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'''
lowerCAmelCase_ = '''https://hub-ci.huggingface.co'''
lowerCAmelCase_ = CI_HUB_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
lowerCAmelCase_ = CI_HUB_ENDPOINT + '''/{repo_id}/resolve/{revision}/{filename}'''
lowerCAmelCase_ = Path('''~/.huggingface/hub_ci_token''').expanduser()
@pytest.fixture
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
monkeypatch.setattr(
'''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE''' , _UpperCamelCase )
@pytest.fixture
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
monkeypatch.setattr('''datasets.config.HF_ENDPOINT''' , _UpperCamelCase )
monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL''' , _UpperCamelCase )
@pytest.fixture
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token''' , _UpperCamelCase )
@pytest.fixture
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
HfFolder.save_token(_UpperCamelCase )
yield
HfFolder.delete_token()
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
return HfApi(endpoint=_UpperCamelCase )
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : List[Any] = HfFolder.get_token()
HfFolder.save_token(_UpperCamelCase )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(_UpperCamelCase )
@pytest.fixture
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
def _cleanup_repo(_UpperCamelCase ):
hf_api.delete_repo(_UpperCamelCase , token=_UpperCamelCase , repo_type='''dataset''' )
return _cleanup_repo
@pytest.fixture
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
@contextmanager
def _temporary_repo(_UpperCamelCase ):
try:
yield repo_id
finally:
cleanup_repo(_UpperCamelCase )
return _temporary_repo
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Any = f'''repo_txt_data-{int(time.time() * 10E3 )}'''
snake_case_ : Optional[int] = f'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(_UpperCamelCase , token=_UpperCamelCase , repo_type='''dataset''' , private=_UpperCamelCase )
hf_api.upload_file(
token=_UpperCamelCase , path_or_fileobj=str(_UpperCamelCase ) , path_in_repo='''data/text_data.txt''' , repo_id=_UpperCamelCase , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(_UpperCamelCase , token=_UpperCamelCase , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : str = f'''repo_zipped_txt_data-{int(time.time() * 10E3 )}'''
snake_case_ : Optional[Any] = f'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(_UpperCamelCase , token=_UpperCamelCase , repo_type='''dataset''' , private=_UpperCamelCase )
hf_api.upload_file(
token=_UpperCamelCase , path_or_fileobj=str(_UpperCamelCase ) , path_in_repo='''data.zip''' , repo_id=_UpperCamelCase , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(_UpperCamelCase , token=_UpperCamelCase , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='''session''' )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : int = f'''repo_zipped_img_data-{int(time.time() * 10E3 )}'''
snake_case_ : List[str] = f'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(_UpperCamelCase , token=_UpperCamelCase , repo_type='''dataset''' , private=_UpperCamelCase )
hf_api.upload_file(
token=_UpperCamelCase , path_or_fileobj=str(_UpperCamelCase ) , path_in_repo='''data.zip''' , repo_id=_UpperCamelCase , repo_type='''dataset''' , )
yield repo_id
try:
hf_api.delete_repo(_UpperCamelCase , token=_UpperCamelCase , repo_type='''dataset''' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
return hf_private_dataset_repo_zipped_img_data_
| 60 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''' )
print('''Labels: {labels}''' )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase = False ) -> str:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = f'''Expected string as input, found {type(_UpperCamelCase )}'''
raise ValueError(_UpperCamelCase )
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : str = f'''Expected boolean as use_pascal parameter, found {type(_UpperCamelCase )}'''
raise ValueError(_UpperCamelCase )
snake_case_ : Optional[int] = input_str.split('''_''' )
snake_case_ : Dict = 0 if use_pascal else 1
snake_case_ : Optional[Any] = words[start_index:]
snake_case_ : Any = [word[0].upper() + word[1:] for word in words_to_capitalize]
snake_case_ : Union[str, Any] = '''''' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 60 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.48_145_466, 0.4_578_275, 0.40_821_073] , __magic_name__=[0.26_862_954, 0.26_130_258, 0.27_577_711] , __magic_name__=True , ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Dict = size if size is not None else {'''height''': 224, '''width''': 224}
snake_case_ : List[str] = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : List[str] = parent
snake_case_ : Tuple = batch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Any = image_size
snake_case_ : List[Any] = min_resolution
snake_case_ : List[str] = max_resolution
snake_case_ : Dict = do_resize
snake_case_ : Optional[Any] = size
snake_case_ : Any = do_center_crop
snake_case_ : Optional[Any] = crop_size
snake_case_ : Any = do_normalize
snake_case_ : str = image_mean
snake_case_ : Any = image_std
snake_case_ : List[str] = do_convert_rgb
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def lowerCamelCase (self , __magic_name__=False , __magic_name__=False , __magic_name__=False ) -> Optional[int]:
'''simple docstring'''
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
snake_case_ : Optional[Any] = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
snake_case_ : Dict = []
for i in range(self.batch_size ):
snake_case_ , snake_case_ : Optional[Any] = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
snake_case_ : Union[str, Any] = [Image.fromarray(np.moveaxis(__magic_name__ , 0 , -1 ) ) for x in image_inputs]
if torchify:
snake_case_ : int = [torch.from_numpy(__magic_name__ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : Optional[int] = ChineseCLIPImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[Any] = ChineseCLIPImageProcessingTester(self , do_center_crop=__magic_name__ )
@property
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_convert_rgb''' ) )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 224, '''width''': 224} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
snake_case_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : int = self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Dict = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Tuple = self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[Any] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : int = self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : int = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[str] = ChineseCLIPImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=__magic_name__ )
snake_case_ : List[Any] = 3
@property
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_convert_rgb''' ) )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 60 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCAmelCase_ = 3
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
print('''Generating primitive root of p''' )
while True:
snake_case_ : Any = random.randrange(3 , _UpperCamelCase )
if pow(_UpperCamelCase , 2 , _UpperCamelCase ) == 1:
continue
if pow(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) == 1:
continue
return g
def lowerCamelCase_ ( _UpperCamelCase ) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
"""simple docstring"""
print('''Generating prime p...''' )
snake_case_ : Optional[int] = rabin_miller.generate_large_prime(_UpperCamelCase ) # select large prime number.
snake_case_ : str = primitive_root(_UpperCamelCase ) # one primitive root on modulo p.
snake_case_ : Union[str, Any] = random.randrange(3 , _UpperCamelCase ) # private_key -> have to be greater than 2 for safety.
snake_case_ : Optional[int] = cryptomath.find_mod_inverse(pow(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) , _UpperCamelCase )
snake_case_ : Dict = (key_size, e_a, e_a, p)
snake_case_ : Any = (key_size, d)
return public_key, private_key
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
if os.path.exists(f'''{name}_pubkey.txt''' ) or os.path.exists(f'''{name}_privkey.txt''' ):
print('''\nWARNING:''' )
print(
f'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
'''Use a different name or delete these files and re-run this program.''' )
sys.exit()
snake_case_ , snake_case_ : str = generate_key(_UpperCamelCase )
print(f'''\nWriting public key to file {name}_pubkey.txt...''' )
with open(f'''{name}_pubkey.txt''' , '''w''' ) as fo:
fo.write(f'''{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}''' )
print(f'''Writing private key to file {name}_privkey.txt...''' )
with open(f'''{name}_privkey.txt''' , '''w''' ) as fo:
fo.write(f'''{private_key[0]},{private_key[1]}''' )
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
print('''Making key files...''' )
make_key_files('''elgamal''' , 2_048 )
print('''Key files generation successful''' )
if __name__ == "__main__":
main()
| 60 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 1 |
class __lowerCAmelCase ( _a ):
pass
class __lowerCAmelCase ( _a ):
pass
class __lowerCAmelCase :
def __init__(self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[int] = [
[],
[],
[],
]
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> None:
'''simple docstring'''
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError('''Maximum queue size is 100''' )
self.queues[priority].append(__magic_name__ )
except IndexError:
raise ValueError('''Valid priorities are 0, 1, and 2''' )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('''All queues are empty''' )
def __str__(self ) -> str:
'''simple docstring'''
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class __lowerCAmelCase :
def __init__(self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Tuple = []
def lowerCamelCase (self , __magic_name__ ) -> None:
'''simple docstring'''
if len(self.queue ) == 100:
raise OverFlowError('''Maximum queue size is 100''' )
self.queue.append(__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
if not self.queue:
raise UnderFlowError('''The queue is empty''' )
else:
snake_case_ : Tuple = min(self.queue )
self.queue.remove(__magic_name__ )
return data
def __str__(self ) -> str:
'''simple docstring'''
return str(self.queue )
def lowerCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Optional[int] = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 100 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 128 )
print(_UpperCamelCase )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(_UpperCamelCase )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ : List[Any] = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(100 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(128 )
print(_UpperCamelCase )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(_UpperCamelCase )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 60 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : str = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ : Tuple = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
snake_case_ : List[str] = min(_UpperCamelCase , _UpperCamelCase )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=1_0, r=5))
| 60 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
('''input_proj.weight''', '''input_projection.weight'''),
('''input_proj.bias''', '''input_projection.bias'''),
('''query_embed.weight''', '''query_position_embeddings.weight'''),
('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
('''class_embed.weight''', '''class_labels_classifier.weight'''),
('''class_embed.bias''', '''class_labels_classifier.bias'''),
('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
]
)
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Any = state_dict.pop(_UpperCamelCase )
snake_case_ : Any = val
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Dict = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
snake_case_ : Optional[int] = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
snake_case_ : Dict = value
else:
snake_case_ : Dict = value
return new_state_dict
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=False ) -> List[Any]:
"""simple docstring"""
snake_case_ : Optional[int] = ''''''
if is_panoptic:
snake_case_ : List[str] = '''conditional_detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
snake_case_ : Any = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
snake_case_ : Optional[Any] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Any = in_proj_weight[:256, :]
snake_case_ : Union[str, Any] = in_proj_bias[:256]
snake_case_ : List[str] = in_proj_weight[256:512, :]
snake_case_ : Optional[int] = in_proj_bias[256:512]
snake_case_ : Dict = in_proj_weight[-256:, :]
snake_case_ : Tuple = in_proj_bias[-256:]
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ : Dict = Image.open(requests.get(_UpperCamelCase , stream=_UpperCamelCase ).raw )
return im
@torch.no_grad()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : str = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
snake_case_ : Optional[int] = '''resnet101'''
if "dc5" in model_name:
snake_case_ : Dict = True
snake_case_ : Tuple = '''panoptic''' in model_name
if is_panoptic:
snake_case_ : Tuple = 250
else:
snake_case_ : List[Any] = 91
snake_case_ : List[str] = '''huggingface/label-files'''
snake_case_ : Union[str, Any] = '''coco-detection-id2label.json'''
snake_case_ : Any = json.load(open(hf_hub_download(_UpperCamelCase , _UpperCamelCase , repo_type='''dataset''' ) , '''r''' ) )
snake_case_ : Optional[Any] = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : List[str] = idalabel
snake_case_ : Optional[int] = {v: k for k, v in idalabel.items()}
# load image processor
snake_case_ : List[str] = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
snake_case_ : Any = ConditionalDetrImageProcessor(format=_UpperCamelCase )
# prepare image
snake_case_ : Tuple = prepare_img()
snake_case_ : Union[str, Any] = image_processor(images=_UpperCamelCase , return_tensors='''pt''' )
snake_case_ : Optional[Any] = encoding['''pixel_values''']
logger.info(f'''Converting model {model_name}...''' )
# load original model from torch hub
snake_case_ : Union[str, Any] = torch.hub.load('''DeppMeng/ConditionalDETR''' , _UpperCamelCase , pretrained=_UpperCamelCase ).eval()
snake_case_ : Union[str, Any] = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
snake_case_ : Optional[Any] = '''conditional_detr.''' + src
rename_key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
snake_case_ : Union[str, Any] = rename_backbone_keys(_UpperCamelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(_UpperCamelCase , is_panoptic=_UpperCamelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
snake_case_ : Union[str, Any] = '''conditional_detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('''conditional_detr''' )
and not key.startswith('''class_labels_classifier''' )
and not key.startswith('''bbox_predictor''' )
):
snake_case_ : Optional[int] = state_dict.pop(_UpperCamelCase )
snake_case_ : List[Any] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
snake_case_ : str = state_dict.pop(_UpperCamelCase )
snake_case_ : List[Any] = val
elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
continue
else:
snake_case_ : str = state_dict.pop(_UpperCamelCase )
snake_case_ : Any = val
else:
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
snake_case_ : Dict = state_dict.pop(_UpperCamelCase )
snake_case_ : Tuple = val
# finally, create HuggingFace model and load state dict
snake_case_ : Optional[Any] = ConditionalDetrForSegmentation(_UpperCamelCase ) if is_panoptic else ConditionalDetrForObjectDetection(_UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
model.eval()
model.push_to_hub(repo_id=_UpperCamelCase , organization='''DepuMeng''' , commit_message='''Add model''' )
# verify our conversion
snake_case_ : Optional[int] = conditional_detr(_UpperCamelCase )
snake_case_ : Union[str, Any] = model(_UpperCamelCase )
assert torch.allclose(outputs.logits , original_outputs['''pred_logits'''] , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs['''pred_boxes'''] , atol=1E-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['''pred_masks'''] , atol=1E-4 )
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
image_processor.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''conditional_detr_resnet50''',
type=str,
help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
lowerCAmelCase_ = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 60 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 1 |
import os
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
with open(os.path.dirname(_UpperCamelCase ) + '''/grid.txt''' ) as f:
snake_case_ : str = [] # noqa: E741
for _ in range(20 ):
l.append([int(_UpperCamelCase ) for x in f.readline().split()] )
snake_case_ : str = 0
# right
for i in range(20 ):
for j in range(17 ):
snake_case_ : Dict = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
snake_case_ : Dict = temp
# down
for i in range(17 ):
for j in range(20 ):
snake_case_ : List[Any] = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
snake_case_ : List[Any] = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
snake_case_ : Tuple = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
snake_case_ : List[Any] = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
snake_case_ : Union[str, Any] = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
snake_case_ : Dict = temp
return maximum
if __name__ == "__main__":
print(solution())
| 60 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 60 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = ComputeEnvironment.AMAZON_SAGEMAKER
lowerCamelCase_ : int = True
lowerCamelCase_ : Tuple = '''ml.p3.2xlarge'''
lowerCamelCase_ : Any = '''accelerate_sagemaker_execution_role'''
lowerCamelCase_ : Optional[int] = '''hf-sm'''
lowerCamelCase_ : Optional[Any] = '''us-east-1'''
lowerCamelCase_ : Optional[int] = 1
lowerCamelCase_ : List[str] = '''accelerate-sagemaker-1'''
lowerCamelCase_ : List[Any] = '''1.6'''
lowerCamelCase_ : Optional[int] = '''4.4'''
lowerCamelCase_ : Dict = '''train.py'''
lowerCamelCase_ : str = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''False''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
lowerCamelCase_ : List[Any] = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''--do_test''',
'''False''',
'''--do_predict''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , __magic_name__ )
assert isinstance(converted_args['''do_train'''] , __magic_name__ )
assert isinstance(converted_args['''epochs'''] , __magic_name__ )
assert isinstance(converted_args['''learning_rate'''] , __magic_name__ )
assert isinstance(converted_args['''max_steps'''] , __magic_name__ )
with pytest.raises(__magic_name__ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 60 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=False , ) -> Any:
"""simple docstring"""
output_path.parent.mkdir(parents=_UpperCamelCase , exist_ok=_UpperCamelCase )
# 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(
_UpperCamelCase , _UpperCamelCase , f=output_path.as_posix() , input_names=_UpperCamelCase , output_names=_UpperCamelCase , dynamic_axes=_UpperCamelCase , do_constant_folding=_UpperCamelCase , use_external_data_format=_UpperCamelCase , enable_onnx_checker=_UpperCamelCase , opset_version=_UpperCamelCase , )
else:
export(
_UpperCamelCase , _UpperCamelCase , f=output_path.as_posix() , input_names=_UpperCamelCase , output_names=_UpperCamelCase , dynamic_axes=_UpperCamelCase , do_constant_folding=_UpperCamelCase , opset_version=_UpperCamelCase , )
@torch.no_grad()
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = False ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Dict = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
snake_case_ : Union[str, Any] = '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
else:
snake_case_ : Optional[int] = '''cpu'''
snake_case_ : List[str] = Path(_UpperCamelCase )
# VAE DECODER
snake_case_ : Dict = AutoencoderKL.from_pretrained(model_path + '''/vae''' )
snake_case_ : str = vae_decoder.config.latent_channels
# forward only through the decoder part
snake_case_ : int = vae_decoder.decode
onnx_export(
_UpperCamelCase , model_args=(
torch.randn(1 , _UpperCamelCase , 25 , 25 ).to(device=_UpperCamelCase , dtype=_UpperCamelCase ),
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=_UpperCamelCase , )
del vae_decoder
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=1_4,
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()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('''SD: Done: ONNX''')
| 60 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_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,
)
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = OrderedDict(
[
('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
('''beit''', '''BeitFeatureExtractor'''),
('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
('''clap''', '''ClapFeatureExtractor'''),
('''clip''', '''CLIPFeatureExtractor'''),
('''clipseg''', '''ViTFeatureExtractor'''),
('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
('''convnext''', '''ConvNextFeatureExtractor'''),
('''cvt''', '''ConvNextFeatureExtractor'''),
('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
('''data2vec-vision''', '''BeitFeatureExtractor'''),
('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
('''deit''', '''DeiTFeatureExtractor'''),
('''detr''', '''DetrFeatureExtractor'''),
('''dinat''', '''ViTFeatureExtractor'''),
('''donut-swin''', '''DonutFeatureExtractor'''),
('''dpt''', '''DPTFeatureExtractor'''),
('''encodec''', '''EncodecFeatureExtractor'''),
('''flava''', '''FlavaFeatureExtractor'''),
('''glpn''', '''GLPNFeatureExtractor'''),
('''groupvit''', '''CLIPFeatureExtractor'''),
('''hubert''', '''Wav2Vec2FeatureExtractor'''),
('''imagegpt''', '''ImageGPTFeatureExtractor'''),
('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
('''levit''', '''LevitFeatureExtractor'''),
('''maskformer''', '''MaskFormerFeatureExtractor'''),
('''mctct''', '''MCTCTFeatureExtractor'''),
('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
('''mobilevit''', '''MobileViTFeatureExtractor'''),
('''nat''', '''ViTFeatureExtractor'''),
('''owlvit''', '''OwlViTFeatureExtractor'''),
('''perceiver''', '''PerceiverFeatureExtractor'''),
('''poolformer''', '''PoolFormerFeatureExtractor'''),
('''regnet''', '''ConvNextFeatureExtractor'''),
('''resnet''', '''ConvNextFeatureExtractor'''),
('''segformer''', '''SegformerFeatureExtractor'''),
('''sew''', '''Wav2Vec2FeatureExtractor'''),
('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
('''speecht5''', '''SpeechT5FeatureExtractor'''),
('''swiftformer''', '''ViTFeatureExtractor'''),
('''swin''', '''ViTFeatureExtractor'''),
('''swinv2''', '''ViTFeatureExtractor'''),
('''table-transformer''', '''DetrFeatureExtractor'''),
('''timesformer''', '''VideoMAEFeatureExtractor'''),
('''tvlt''', '''TvltFeatureExtractor'''),
('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
('''van''', '''ConvNextFeatureExtractor'''),
('''videomae''', '''VideoMAEFeatureExtractor'''),
('''vilt''', '''ViltFeatureExtractor'''),
('''vit''', '''ViTFeatureExtractor'''),
('''vit_mae''', '''ViTFeatureExtractor'''),
('''vit_msn''', '''ViTFeatureExtractor'''),
('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
('''whisper''', '''WhisperFeatureExtractor'''),
('''xclip''', '''CLIPFeatureExtractor'''),
('''yolos''', '''YolosFeatureExtractor'''),
]
)
lowerCAmelCase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
snake_case_ : Union[str, Any] = model_type_to_module_name(_UpperCamelCase )
snake_case_ : Dict = importlib.import_module(f'''.{module_name}''' , '''transformers.models''' )
try:
return getattr(_UpperCamelCase , _UpperCamelCase )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(_UpperCamelCase , '''__name__''' , _UpperCamelCase ) == 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.
snake_case_ : Union[str, Any] = importlib.import_module('''transformers''' )
if hasattr(_UpperCamelCase , _UpperCamelCase ):
return getattr(_UpperCamelCase , _UpperCamelCase )
return None
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = False , _UpperCamelCase = False , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = False , **_UpperCamelCase , ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = get_file_from_repo(
_UpperCamelCase , _UpperCamelCase , cache_dir=_UpperCamelCase , force_download=_UpperCamelCase , resume_download=_UpperCamelCase , proxies=_UpperCamelCase , use_auth_token=_UpperCamelCase , revision=_UpperCamelCase , local_files_only=_UpperCamelCase , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(_UpperCamelCase , encoding='''utf-8''' ) as reader:
return json.load(_UpperCamelCase )
class __lowerCAmelCase :
def __init__(self ) -> Optional[Any]:
'''simple docstring'''
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(__magic_name__ )
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = kwargs.pop('''config''' , __magic_name__ )
snake_case_ : Union[str, Any] = kwargs.pop('''trust_remote_code''' , __magic_name__ )
snake_case_ : Tuple = True
snake_case_ , snake_case_ : Tuple = FeatureExtractionMixin.get_feature_extractor_dict(__magic_name__ , **__magic_name__ )
snake_case_ : int = config_dict.get('''feature_extractor_type''' , __magic_name__ )
snake_case_ : str = None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
snake_case_ : Any = config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Any = AutoConfig.from_pretrained(__magic_name__ , **__magic_name__ )
# It could be in `config.feature_extractor_type``
snake_case_ : List[Any] = getattr(__magic_name__ , '''feature_extractor_type''' , __magic_name__ )
if hasattr(__magic_name__ , '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
snake_case_ : List[str] = config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
snake_case_ : List[Any] = feature_extractor_class_from_name(__magic_name__ )
snake_case_ : Optional[int] = feature_extractor_auto_map is not None
snake_case_ : Tuple = feature_extractor_class is not None or type(__magic_name__ ) in FEATURE_EXTRACTOR_MAPPING
snake_case_ : Optional[int] = resolve_trust_remote_code(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if has_remote_code and trust_remote_code:
snake_case_ : int = get_class_from_dynamic_module(
__magic_name__ , __magic_name__ , **__magic_name__ )
snake_case_ : int = kwargs.pop('''code_revision''' , __magic_name__ )
if os.path.isdir(__magic_name__ ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__magic_name__ , **__magic_name__ )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__magic_name__ , **__magic_name__ )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__magic_name__ ) in FEATURE_EXTRACTOR_MAPPING:
snake_case_ : Optional[Any] = FEATURE_EXTRACTOR_MAPPING[type(__magic_name__ )]
return feature_extractor_class.from_dict(__magic_name__ , **__magic_name__ )
raise ValueError(
F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
FEATURE_EXTRACTOR_MAPPING.register(__magic_name__ , __magic_name__ )
| 60 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 1 |
import math
import tensorflow as tf
from packaging import version
def lowerCamelCase_ ( _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : List[Any] = tf.convert_to_tensor(_UpperCamelCase )
snake_case_ : Union[str, Any] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCamelCase_ ( _UpperCamelCase ) -> List[str]:
"""simple docstring"""
snake_case_ : int = tf.convert_to_tensor(_UpperCamelCase )
snake_case_ : List[str] = tf.cast(math.pi , x.dtype )
snake_case_ : Optional[Any] = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(_UpperCamelCase , 3 )) ))
return x * cdf
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : List[Any] = tf.convert_to_tensor(_UpperCamelCase )
return x * tf.tanh(tf.math.softplus(_UpperCamelCase ) )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Union[str, Any] = tf.convert_to_tensor(_UpperCamelCase )
snake_case_ : Optional[int] = tf.cast(0.044_715 , x.dtype )
snake_case_ : List[Any] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = tf.convert_to_tensor(_UpperCamelCase )
snake_case_ : Union[str, Any] = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
return tf.clip_by_value(_gelu(_UpperCamelCase ) , -10 , 10 )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=-1 ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ , snake_case_ : str = tf.split(_UpperCamelCase , 2 , axis=_UpperCamelCase )
return a * tf.math.sigmoid(_UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return tf.keras.activations.gelu(_UpperCamelCase , approximate=_UpperCamelCase )
lowerCAmelCase_ = tf.keras.activations.gelu
lowerCAmelCase_ = approximate_gelu_wrap
else:
lowerCAmelCase_ = _gelu
lowerCAmelCase_ = _gelu_new
lowerCAmelCase_ = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}''' )
| 60 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 1 |
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