code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE ( a ):
"""simple docstring"""
a_ : str ="align_text_model"
def __init__( self : List[str] , _snake_case : str=3_0522 , _snake_case : List[Any]=768 , _snake_case : Tuple=12 , _snake_case : Dict=12 , _snake_case : str=3072 , _snake_case : Optional[Any]="gelu" , _snake_case : Optional[Any]=0.1 , _snake_case : str=0.1 , _snake_case : int=512 , _snake_case : List[str]=2 , _snake_case : List[Any]=0.02 , _snake_case : Dict=1E-12 , _snake_case : Any=0 , _snake_case : Any="absolute" , _snake_case : Dict=True , **_snake_case : Any , ) -> List[Any]:
'''simple docstring'''
super().__init__(**_snake_case )
a__ = vocab_size
a__ = hidden_size
a__ = num_hidden_layers
a__ = num_attention_heads
a__ = hidden_act
a__ = intermediate_size
a__ = hidden_dropout_prob
a__ = attention_probs_dropout_prob
a__ = max_position_embeddings
a__ = type_vocab_size
a__ = initializer_range
a__ = layer_norm_eps
a__ = position_embedding_type
a__ = use_cache
a__ = pad_token_id
@classmethod
def _lowerCAmelCase ( cls : Any , _snake_case : Union[str, os.PathLike] , **_snake_case : Union[str, Any] ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_snake_case )
a__ , a__ = cls.get_config_dict(_snake_case , **_snake_case )
# get the text config dict if we are loading from AlignConfig
if config_dict.get('model_type' ) == "align":
a__ = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_snake_case , **_snake_case )
class SCREAMING_SNAKE_CASE ( a ):
"""simple docstring"""
a_ : Dict ="align_vision_model"
def __init__( self : Tuple , _snake_case : int = 3 , _snake_case : int = 600 , _snake_case : float = 2.0 , _snake_case : float = 3.1 , _snake_case : int = 8 , _snake_case : List[int] = [3, 3, 5, 3, 5, 5, 3] , _snake_case : List[int] = [32, 16, 24, 40, 80, 112, 192] , _snake_case : List[int] = [16, 24, 40, 80, 112, 192, 320] , _snake_case : List[int] = [] , _snake_case : List[int] = [1, 2, 2, 2, 1, 2, 1] , _snake_case : List[int] = [1, 2, 2, 3, 3, 4, 1] , _snake_case : List[int] = [1, 6, 6, 6, 6, 6, 6] , _snake_case : float = 0.25 , _snake_case : str = "swish" , _snake_case : int = 2560 , _snake_case : str = "mean" , _snake_case : float = 0.02 , _snake_case : float = 0.001 , _snake_case : float = 0.99 , _snake_case : float = 0.2 , **_snake_case : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**_snake_case )
a__ = num_channels
a__ = image_size
a__ = width_coefficient
a__ = depth_coefficient
a__ = depth_divisor
a__ = kernel_sizes
a__ = in_channels
a__ = out_channels
a__ = depthwise_padding
a__ = strides
a__ = num_block_repeats
a__ = expand_ratios
a__ = squeeze_expansion_ratio
a__ = hidden_act
a__ = hidden_dim
a__ = pooling_type
a__ = initializer_range
a__ = batch_norm_eps
a__ = batch_norm_momentum
a__ = drop_connect_rate
a__ = sum(_snake_case ) * 4
@classmethod
def _lowerCAmelCase ( cls : Tuple , _snake_case : Union[str, os.PathLike] , **_snake_case : Tuple ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_snake_case )
a__ , a__ = cls.get_config_dict(_snake_case , **_snake_case )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get('model_type' ) == "align":
a__ = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_snake_case , **_snake_case )
class SCREAMING_SNAKE_CASE ( a ):
"""simple docstring"""
a_ : Optional[Any] ="align"
a_ : List[str] =True
def __init__( self : Tuple , _snake_case : Optional[Any]=None , _snake_case : Union[str, Any]=None , _snake_case : Optional[Any]=640 , _snake_case : Any=1.0 , _snake_case : Optional[int]=0.02 , **_snake_case : List[Any] , ) -> int:
'''simple docstring'''
super().__init__(**_snake_case )
if text_config is None:
a__ = {}
logger.info('text_config is None. Initializing the AlignTextConfig with default values.' )
if vision_config is None:
a__ = {}
logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.' )
a__ = AlignTextConfig(**_snake_case )
a__ = AlignVisionConfig(**_snake_case )
a__ = projection_dim
a__ = temperature_init_value
a__ = initializer_range
@classmethod
def _lowerCAmelCase ( cls : Optional[Any] , _snake_case : AlignTextConfig , _snake_case : AlignVisionConfig , **_snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_snake_case )
def _lowerCAmelCase ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
a__ = copy.deepcopy(self.__dict__ )
a__ = self.text_config.to_dict()
a__ = self.vision_config.to_dict()
a__ = self.__class__.model_type
return output
| 232 | """simple docstring"""
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 SCREAMING_SNAKE_CASE ( a ):
"""simple docstring"""
def __init__( self : List[str] , _snake_case : str = "▁" , _snake_case : bool = True , _snake_case : Union[str, AddedToken] = "<unk>" , _snake_case : Union[str, AddedToken] = "</s>" , _snake_case : Union[str, AddedToken] = "<pad>" , ) -> Optional[int]:
'''simple docstring'''
a__ = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
a__ = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
a__ = token_dict['token']
a__ = Tokenizer(Unigram() )
a__ = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}' ) , ' ' ),
normalizers.Lowercase(),
] )
a__ = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=_snake_case , add_prefix_space=_snake_case ),
pre_tokenizers.Digits(individual_digits=_snake_case ),
pre_tokenizers.Punctuation(),
] )
a__ = decoders.Metaspace(replacement=_snake_case , add_prefix_space=_snake_case )
a__ = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
a__ = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(_snake_case , _snake_case )
def _lowerCAmelCase ( self : Dict , _snake_case : Union[str, List[str]] , _snake_case : int = 8000 , _snake_case : bool = True , ) -> str:
'''simple docstring'''
a__ = trainers.UnigramTrainer(
vocab_size=_snake_case , special_tokens=self.special_tokens_list , show_progress=_snake_case , )
if isinstance(_snake_case , _snake_case ):
a__ = [files]
self._tokenizer.train(_snake_case , trainer=_snake_case )
self.add_unk_id()
def _lowerCAmelCase ( self : Union[str, Any] , _snake_case : Union[Iterator[str], Iterator[Iterator[str]]] , _snake_case : int = 8000 , _snake_case : bool = True , ) -> Optional[Any]:
'''simple docstring'''
a__ = trainers.UnigramTrainer(
vocab_size=_snake_case , special_tokens=self.special_tokens_list , show_progress=_snake_case , )
self._tokenizer.train_from_iterator(_snake_case , trainer=_snake_case )
self.add_unk_id()
def _lowerCAmelCase ( self : List[Any] ) -> int:
'''simple docstring'''
a__ = json.loads(self._tokenizer.to_str() )
a__ = self.special_tokens['unk']['id']
a__ = Tokenizer.from_str(json.dumps(_snake_case ) )
| 232 | 1 |
import numpy as np
UpperCamelCase_ = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class snake_case_ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
UpperCAmelCase__ =np.array(UpperCamelCase__ )
def __UpperCAmelCase ( self, A_ ) -> int:
UpperCAmelCase__ =np.where(letter == self.SQUARE )
UpperCAmelCase__ =np.concatenate([indexa + 1, indexa + 1] )
return indexes
def __UpperCAmelCase ( self, A_, A_ ) -> str:
UpperCAmelCase__ =self.SQUARE[indexa - 1, indexa - 1]
return letter
def __UpperCAmelCase ( self, A_ ) -> Dict:
UpperCAmelCase__ =message.lower()
UpperCAmelCase__ =message.replace(" ", "" )
UpperCAmelCase__ =message.replace("j", "i" )
UpperCAmelCase__ =np.empty((2, len(UpperCamelCase__ )) )
for letter_index in range(len(UpperCamelCase__ ) ):
UpperCAmelCase__ =self.letter_to_numbers(message[letter_index] )
UpperCAmelCase__ =numbers[0]
UpperCAmelCase__ =numbers[1]
UpperCAmelCase__ =first_step.reshape(2 * len(UpperCamelCase__ ) )
UpperCAmelCase__ =''''''
for numbers_index in range(len(UpperCamelCase__ ) ):
UpperCAmelCase__ =int(second_step[numbers_index * 2] )
UpperCAmelCase__ =int(second_step[(numbers_index * 2) + 1] )
UpperCAmelCase__ =self.numbers_to_letter(UpperCamelCase__, UpperCamelCase__ )
UpperCAmelCase__ =encoded_message + letter
return encoded_message
def __UpperCAmelCase ( self, A_ ) -> List[str]:
UpperCAmelCase__ =message.lower()
message.replace(" ", "" )
UpperCAmelCase__ =np.empty(2 * len(UpperCamelCase__ ) )
for letter_index in range(len(UpperCamelCase__ ) ):
UpperCAmelCase__ =self.letter_to_numbers(message[letter_index] )
UpperCAmelCase__ =numbers[0]
UpperCAmelCase__ =numbers[1]
UpperCAmelCase__ =first_step.reshape((2, len(UpperCamelCase__ )) )
UpperCAmelCase__ =''''''
for numbers_index in range(len(UpperCamelCase__ ) ):
UpperCAmelCase__ =int(second_step[0, numbers_index] )
UpperCAmelCase__ =int(second_step[1, numbers_index] )
UpperCAmelCase__ =self.numbers_to_letter(UpperCamelCase__, UpperCamelCase__ )
UpperCAmelCase__ =decoded_message + letter
return decoded_message
| 711 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class snake_case_ ( TensorFormatter[Mapping, 'torch.Tensor', Mapping] ):
'''simple docstring'''
def __init__( self, A_=None, **A_ ) -> Tuple:
super().__init__(features=A_ )
UpperCAmelCase__ =torch_tensor_kwargs
import torch # noqa import torch at initialization
def __UpperCAmelCase ( self, A_ ) -> Dict:
import torch
if isinstance(A_, A_ ) and column:
if all(
isinstance(A_, torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(A_ )
return column
def __UpperCAmelCase ( self, A_ ) -> Dict:
import torch
if isinstance(A_, (str, bytes, type(A_ )) ):
return value
elif isinstance(A_, (np.character, np.ndarray) ) and np.issubdtype(value.dtype, np.character ):
return value.tolist()
UpperCAmelCase__ ={}
if isinstance(A_, (np.number, np.ndarray) ) and np.issubdtype(value.dtype, np.integer ):
UpperCAmelCase__ ={"dtype": torch.intaa}
elif isinstance(A_, (np.number, np.ndarray) ) and np.issubdtype(value.dtype, np.floating ):
UpperCAmelCase__ ={"dtype": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A_, PIL.Image.Image ):
UpperCAmelCase__ =np.asarray(A_ )
return torch.tensor(A_, **{**default_dtype, **self.torch_tensor_kwargs} )
def __UpperCAmelCase ( self, A_ ) -> int:
import torch
# support for torch, tf, jax etc.
if hasattr(A_, "__array__" ) and not isinstance(A_, torch.Tensor ):
UpperCAmelCase__ =data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A_, np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A_ ) for substruct in data_struct] )
elif isinstance(A_, (list, tuple) ):
return self._consolidate([self.recursive_tensorize(A_ ) for substruct in data_struct] )
return self._tensorize(A_ )
def __UpperCAmelCase ( self, A_ ) -> List[Any]:
return map_nested(self._recursive_tensorize, A_, map_list=A_ )
def __UpperCAmelCase ( self, A_ ) -> Mapping:
UpperCAmelCase__ =self.numpy_arrow_extractor().extract_row(A_ )
UpperCAmelCase__ =self.python_features_decoder.decode_row(A_ )
return self.recursive_tensorize(A_ )
def __UpperCAmelCase ( self, A_ ) -> "torch.Tensor":
UpperCAmelCase__ =self.numpy_arrow_extractor().extract_column(A_ )
UpperCAmelCase__ =self.python_features_decoder.decode_column(A_, pa_table.column_names[0] )
UpperCAmelCase__ =self.recursive_tensorize(A_ )
UpperCAmelCase__ =self._consolidate(A_ )
return column
def __UpperCAmelCase ( self, A_ ) -> Mapping:
UpperCAmelCase__ =self.numpy_arrow_extractor().extract_batch(A_ )
UpperCAmelCase__ =self.python_features_decoder.decode_batch(A_ )
UpperCAmelCase__ =self.recursive_tensorize(A_ )
for column_name in batch:
UpperCAmelCase__ =self._consolidate(batch[column_name] )
return batch
| 510 | 0 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
UpperCAmelCase_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
UpperCAmelCase_ = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = """maskformer"""
lowerCAmelCase_ : Tuple = {"""hidden_size""": """mask_feature_size"""}
lowerCAmelCase_ : Union[str, Any] = ["""resnet""", """swin"""]
lowerCAmelCase_ : Any = ["""detr"""]
def __init__( self : Optional[int] , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[Dict] = None , _UpperCAmelCase : Optional[Dict] = None , _UpperCAmelCase : float = 0.02 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 20.0 , _UpperCAmelCase : Optional[bool] = None , **_UpperCAmelCase : Union[str, Any] , ):
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
UpperCAmelCase__ = SwinConfig(
image_size=3_84 , in_channels=3 , patch_size=4 , embed_dim=1_28 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] , )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
UpperCAmelCase__ = backbone_config.pop("""model_type""" )
UpperCAmelCase__ = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ = config_class.from_dict(_UpperCAmelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
f'''Supported model types: {','.join(self.backbones_supported )}''' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
UpperCAmelCase__ = DetrConfig()
else:
# verify that the decoder is supported
UpperCAmelCase__ = (
decoder_config.pop("""model_type""" ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f'''Transformer Decoder {decoder_type} not supported, please use one of'''
f''' {','.join(self.decoders_supported )}''' )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
UpperCAmelCase__ = CONFIG_MAPPING[decoder_type]
UpperCAmelCase__ = config_class.from_dict(_UpperCAmelCase )
UpperCAmelCase__ = backbone_config
UpperCAmelCase__ = decoder_config
# main feature dimension for the model
UpperCAmelCase__ = fpn_feature_size
UpperCAmelCase__ = mask_feature_size
# initializer
UpperCAmelCase__ = init_std
UpperCAmelCase__ = init_xavier_std
# Hungarian matcher && loss
UpperCAmelCase__ = cross_entropy_weight
UpperCAmelCase__ = dice_weight
UpperCAmelCase__ = mask_weight
UpperCAmelCase__ = use_auxiliary_loss
UpperCAmelCase__ = no_object_weight
UpperCAmelCase__ = output_auxiliary_logits
UpperCAmelCase__ = self.decoder_config.encoder_attention_heads
UpperCAmelCase__ = self.decoder_config.num_hidden_layers
super().__init__(**_UpperCAmelCase )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple , _UpperCAmelCase : PretrainedConfig , _UpperCAmelCase : PretrainedConfig , **_UpperCAmelCase : List[str] ):
"""simple docstring"""
return cls(
backbone_config=_UpperCAmelCase , decoder_config=_UpperCAmelCase , **_UpperCAmelCase , )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ = self.backbone_config.to_dict()
UpperCAmelCase__ = self.decoder_config.to_dict()
UpperCAmelCase__ = self.__class__.model_type
return output
| 603 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ = get_tests_dir('fixtures/test_sentencepiece_no_bos.model')
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : List[Any] = PegasusTokenizer
lowerCAmelCase_ : Optional[Any] = PegasusTokenizerFast
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : List[str] = True
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase__ = PegasusTokenizer(_UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , **_UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : List[str] ):
"""simple docstring"""
return ("This is a test", "This is a test")
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = """</s>"""
UpperCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase ) , _UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase ) , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """</s>""" )
self.assertEqual(vocab_keys[-1] , """v""" )
self.assertEqual(len(_UpperCAmelCase ) , 11_03 )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 11_03 )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
UpperCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
""" </s> <pad> <pad> <pad>"""
)
UpperCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids[0]
UpperCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids[0]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
UpperCAmelCase__ = """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
UpperCAmelCase__ = [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
UpperCAmelCase__ = tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase ).input_ids[0]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_61_03
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_03
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 10_24
UpperCAmelCase__ = """To ensure a smooth flow of bank resolutions."""
UpperCAmelCase__ = [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
UpperCAmelCase__ = tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase ).input_ids[0]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = ["""This is going to be way too long.""" * 1_50, """short example"""]
UpperCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
UpperCAmelCase__ = self._large_tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors="""pt""" )
UpperCAmelCase__ = self._large_tokenizer(
text_target=_UpperCAmelCase , max_length=5 , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 10_24)
assert batch.attention_mask.shape == (2, 10_24)
assert targets["input_ids"].shape == (2, 5)
assert len(_UpperCAmelCase ) == 2 # input_ids, attention_mask.
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = {"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_UpperCAmelCase , model_name="""google/bigbird-pegasus-large-arxiv""" , revision="""ba85d0851d708441f91440d509690f1ab6353415""" , )
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = PegasusTokenizer
lowerCAmelCase_ : int = PegasusTokenizerFast
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : Union[str, Any] = True
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase__ = PegasusTokenizer(_UpperCAmelCase , offset=0 , mask_token_sent=_UpperCAmelCase , mask_token="""[MASK]""" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , **_UpperCAmelCase : str ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : Any ):
"""simple docstring"""
return ("This is a test", "This is a test")
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
UpperCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
""" <pad> <pad> <pad>"""
)
UpperCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids[0]
UpperCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids[0]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = ["""This is going to be way too long.""" * 10_00, """short example"""]
UpperCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
UpperCAmelCase__ = self._large_tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors="""pt""" )
UpperCAmelCase__ = self._large_tokenizer(
text_target=_UpperCAmelCase , max_length=5 , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 40_96)
assert batch.attention_mask.shape == (2, 40_96)
assert targets["input_ids"].shape == (2, 5)
assert len(_UpperCAmelCase ) == 2 # input_ids, attention_mask.
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = (
"""This is an example string that is used to test the original TF implementation against the HF"""
""" implementation"""
)
UpperCAmelCase__ = self._large_tokenizer(_UpperCAmelCase ).input_ids
self.assertListEqual(
_UpperCAmelCase , [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1] , )
| 603 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
a_ : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
a_ : Tuple = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
a_ : Optional[int] = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def __snake_case ( UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any] ):
lowerCamelCase_ = len([g for position, g in enumerate(_lowercase ) if g == main_target[position]] )
return (item, float(_lowercase ))
def __snake_case ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Any ):
lowerCamelCase_ = random.randint(0 , len(_lowercase ) - 1 )
lowerCamelCase_ = parent_a[:random_slice] + parent_a[random_slice:]
lowerCamelCase_ = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[Any] ):
lowerCamelCase_ = list(_lowercase )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
lowerCamelCase_ = random.choice(_lowercase )
return "".join(_lowercase )
def __snake_case ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Dict , ):
lowerCamelCase_ = []
# Generate more children proportionally to the fitness score.
lowerCamelCase_ = int(parent_a[1] * 100 ) + 1
lowerCamelCase_ = 10 if child_n >= 10 else child_n
for _ in range(_lowercase ):
lowerCamelCase_ = population_score[random.randint(0 , _lowercase )][0]
lowerCamelCase_ = crossover(parent_a[0] , _lowercase )
# Append new string to the population list.
pop.append(mutate(_lowercase , _lowercase ) )
pop.append(mutate(_lowercase , _lowercase ) )
return pop
def __snake_case ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[Any] = True ):
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
lowerCamelCase_ = F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(_lowercase )
# Verify that the target contains no genes besides the ones inside genes variable.
lowerCamelCase_ = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
lowerCamelCase_ = F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(_lowercase )
# Generate random starting population.
lowerCamelCase_ = []
for _ in range(_lowercase ):
population.append("".join([random.choice(_lowercase ) for i in range(len(_lowercase ) )] ) )
# Just some logs to know what the algorithms is doing.
lowerCamelCase_ = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(_lowercase )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
lowerCamelCase_ = [evaluate(_lowercase , _lowercase ) for item in population]
# Check if there is a matching evolution.
lowerCamelCase_ = sorted(_lowercase , key=lambda UpperCAmelCase_ : x[1] , reverse=_lowercase )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F'''\nGeneration: {generation}'''
F'''\nTotal Population:{total_population}'''
F'''\nBest score: {population_score[0][1]}'''
F'''\nBest string: {population_score[0][0]}''' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
lowerCamelCase_ = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(_lowercase )
# Normalize population score to be between 0 and 1.
lowerCamelCase_ = [
(item, score / len(_lowercase )) for item, score in population_score
]
# This is selection
for i in range(_lowercase ):
population.extend(select(population_score[int(_lowercase )] , _lowercase , _lowercase ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(_lowercase ) > N_POPULATION:
break
if __name__ == "__main__":
a_ : str = (
'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
)
a_ : Optional[int] = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\"""
)
a_ : Optional[Any] = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 712 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = None
class snake_case ( lowercase ):
"""simple docstring"""
def __init__( self , UpperCamelCase=1 , UpperCamelCase=0 , UpperCamelCase=2 , UpperCamelCase=512 , UpperCamelCase="cls" , UpperCamelCase=False , UpperCamelCase=True , **UpperCamelCase , ):
"""simple docstring"""
super().__init__(pad_token_id=UpperCamelCase , bos_token_id=UpperCamelCase , eos_token_id=UpperCamelCase , **UpperCamelCase )
lowerCamelCase_ = project_dim
lowerCamelCase_ = pooler_fn
lowerCamelCase_ = learn_encoder
lowerCamelCase_ = use_attention_mask
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = [r"pooler", r"logit_scale"]
_lowerCamelCase = [r"position_ids", r"predictions.decoder.bias"]
_lowerCamelCase = "roberta"
_lowerCamelCase = RobertaSeriesConfig
def __init__( self , UpperCamelCase ):
"""simple docstring"""
super().__init__(UpperCamelCase )
lowerCamelCase_ = XLMRobertaModel(UpperCamelCase )
lowerCamelCase_ = nn.Linear(config.hidden_size , config.project_dim )
lowerCamelCase_ = getattr(UpperCamelCase , "has_pre_transformation" , UpperCamelCase )
if self.has_pre_transformation:
lowerCamelCase_ = nn.Linear(config.hidden_size , config.project_dim )
lowerCamelCase_ = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def snake_case ( self , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , ):
"""simple docstring"""
lowerCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ = self.base_model(
input_ids=UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase , position_ids=UpperCamelCase , head_mask=UpperCamelCase , inputs_embeds=UpperCamelCase , encoder_hidden_states=UpperCamelCase , encoder_attention_mask=UpperCamelCase , output_attentions=UpperCamelCase , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=UpperCamelCase , )
if self.has_pre_transformation:
lowerCamelCase_ = outputs["hidden_states"][-2]
lowerCamelCase_ = self.pre_LN(UpperCamelCase )
lowerCamelCase_ = self.transformation_pre(UpperCamelCase )
return TransformationModelOutput(
projection_state=UpperCamelCase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
lowerCamelCase_ = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=UpperCamelCase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 445 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=_snake_case ):
UpperCAmelCase = ["note_seq"]
def __init__( self : List[str] , *__lowerCamelCase : int , **__lowerCamelCase : Optional[int] ):
requires_backends(self , ['''note_seq'''] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls : Optional[Any] , *__lowerCamelCase : Dict , **__lowerCamelCase : Optional[Any] ):
requires_backends(cls , ['''note_seq'''] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls : str , *__lowerCamelCase : Any , **__lowerCamelCase : Optional[Any] ):
requires_backends(cls , ['''note_seq'''] )
| 467 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__lowerCamelCase = False
class UpperCAmelCase ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __SCREAMING_SNAKE_CASE ( self : str ):
return 1_2
@property
def __SCREAMING_SNAKE_CASE ( self : List[Any] ):
return 1_2
@property
def __SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return 3_2
@property
def __SCREAMING_SNAKE_CASE ( self : List[str] ):
torch.manual_seed(0 )
UpperCAmelCase__ :List[str] = VQModel(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def __SCREAMING_SNAKE_CASE ( self : int ):
UpperCAmelCase__ :Tuple = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def __SCREAMING_SNAKE_CASE ( self : Tuple ):
torch.manual_seed(0 )
UpperCAmelCase__ :Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(__lowerCamelCase )
@property
def __SCREAMING_SNAKE_CASE ( self : List[str] ):
torch.manual_seed(0 )
UpperCAmelCase__ :List[str] = 1_2
UpperCAmelCase__ :Optional[int] = 1_2
UpperCAmelCase__ :Optional[int] = {
'''attention_bias''': True,
'''cross_attention_dim''': 3_2,
'''attention_head_dim''': height * width,
'''num_attention_heads''': 1,
'''num_vector_embeds''': self.num_embed,
'''num_embeds_ada_norm''': self.num_embeds_ada_norm,
'''norm_num_groups''': 3_2,
'''sample_size''': width,
'''activation_fn''': '''geglu-approximate''',
}
UpperCAmelCase__ :List[str] = TransformeraDModel(**__lowerCamelCase )
return model
def __SCREAMING_SNAKE_CASE ( self : List[Any] ):
UpperCAmelCase__ :Union[str, Any] = '''cpu'''
UpperCAmelCase__ :Optional[Any] = self.dummy_vqvae
UpperCAmelCase__ :Optional[int] = self.dummy_text_encoder
UpperCAmelCase__ :Union[str, Any] = self.dummy_tokenizer
UpperCAmelCase__ :List[Any] = self.dummy_transformer
UpperCAmelCase__ :str = VQDiffusionScheduler(self.num_embed )
UpperCAmelCase__ :Optional[Any] = LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCamelCase )
UpperCAmelCase__ :Any = VQDiffusionPipeline(
vqvae=__lowerCamelCase , text_encoder=__lowerCamelCase , tokenizer=__lowerCamelCase , transformer=__lowerCamelCase , scheduler=__lowerCamelCase , learned_classifier_free_sampling_embeddings=__lowerCamelCase , )
UpperCAmelCase__ :Any = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
UpperCAmelCase__ :str = '''teddy bear playing in the pool'''
UpperCAmelCase__ :Optional[int] = torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
UpperCAmelCase__ :Any = pipe([prompt] , generator=__lowerCamelCase , num_inference_steps=2 , output_type='''np''' )
UpperCAmelCase__ :str = output.images
UpperCAmelCase__ :List[Any] = torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
UpperCAmelCase__ :Dict = pipe(
[prompt] , generator=__lowerCamelCase , output_type='''np''' , return_dict=__lowerCamelCase , num_inference_steps=2 )[0]
UpperCAmelCase__ :int = image[0, -3:, -3:, -1]
UpperCAmelCase__ :List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
UpperCAmelCase__ :int = np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __SCREAMING_SNAKE_CASE ( self : Tuple ):
UpperCAmelCase__ :List[Any] = '''cpu'''
UpperCAmelCase__ :int = self.dummy_vqvae
UpperCAmelCase__ :List[str] = self.dummy_text_encoder
UpperCAmelCase__ :Union[str, Any] = self.dummy_tokenizer
UpperCAmelCase__ :Optional[Any] = self.dummy_transformer
UpperCAmelCase__ :List[str] = VQDiffusionScheduler(self.num_embed )
UpperCAmelCase__ :List[str] = LearnedClassifierFreeSamplingEmbeddings(
learnable=__lowerCamelCase , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
UpperCAmelCase__ :List[Any] = VQDiffusionPipeline(
vqvae=__lowerCamelCase , text_encoder=__lowerCamelCase , tokenizer=__lowerCamelCase , transformer=__lowerCamelCase , scheduler=__lowerCamelCase , learned_classifier_free_sampling_embeddings=__lowerCamelCase , )
UpperCAmelCase__ :str = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
UpperCAmelCase__ :str = '''teddy bear playing in the pool'''
UpperCAmelCase__ :str = torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
UpperCAmelCase__ :Union[str, Any] = pipe([prompt] , generator=__lowerCamelCase , num_inference_steps=2 , output_type='''np''' )
UpperCAmelCase__ :Any = output.images
UpperCAmelCase__ :Optional[int] = torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
UpperCAmelCase__ :int = pipe(
[prompt] , generator=__lowerCamelCase , output_type='''np''' , return_dict=__lowerCamelCase , num_inference_steps=2 )[0]
UpperCAmelCase__ :List[Any] = image[0, -3:, -3:, -1]
UpperCAmelCase__ :Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
UpperCAmelCase__ :Union[str, Any] = np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE ( self : Tuple ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __SCREAMING_SNAKE_CASE ( self : Optional[int] ):
UpperCAmelCase__ :Optional[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )
UpperCAmelCase__ :Optional[Any] = VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
UpperCAmelCase__ :List[str] = pipeline.to(__lowerCamelCase )
pipeline.set_progress_bar_config(disable=__lowerCamelCase )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
UpperCAmelCase__ :int = torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
UpperCAmelCase__ :Union[str, Any] = pipeline(
'''teddy bear playing in the pool''' , num_images_per_prompt=1 , generator=__lowerCamelCase , output_type='''np''' , )
UpperCAmelCase__ :List[str] = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 467 | 1 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __a ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
__lowercase : Tuple = VQModel
__lowercase : Union[str, Any] = 'sample'
@property
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=(32, 32) ) -> Optional[int]:
'''simple docstring'''
lowercase__: List[Any] = 4
lowercase__: int = 3
lowercase__: List[str] = floats_tensor((batch_size, num_channels) + sizes ).to(lowerCAmelCase__ )
return {"sample": image}
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
return (3, 32, 32)
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
return (3, 32, 32)
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: str = {
'block_out_channels': [32, 64],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 3,
}
lowercase__: Any = self.dummy_input
return init_dict, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
lowercase__ , lowercase__: Any = VQModel.from_pretrained('fusing/vqgan-dummy' , output_loading_info=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(lowerCAmelCase__ )
lowercase__: Tuple = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: List[Any] = VQModel.from_pretrained('fusing/vqgan-dummy' )
model.to(lowerCAmelCase__ ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
lowercase__: str = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
lowercase__: Dict = image.to(lowerCAmelCase__ )
with torch.no_grad():
lowercase__: str = model(lowerCAmelCase__ ).sample
lowercase__: Union[str, Any] = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
lowercase__: Dict = torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3] )
# fmt: on
self.assertTrue(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-3 ) )
| 702 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __a ( __UpperCamelCase ):
__lowercase : Optional[Any] = 'beit'
def __init__( self , lowerCAmelCase__=8_192 , lowerCAmelCase__=768 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=3_072 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__=1E-12 , lowerCAmelCase__=224 , lowerCAmelCase__=16 , lowerCAmelCase__=3 , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=True , lowerCAmelCase__=[3, 5, 7, 11] , lowerCAmelCase__=[1, 2, 3, 6] , lowerCAmelCase__=True , lowerCAmelCase__=0.4 , lowerCAmelCase__=256 , lowerCAmelCase__=1 , lowerCAmelCase__=False , lowerCAmelCase__=255 , **lowerCAmelCase__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowercase__: Optional[Any] = vocab_size
lowercase__: Dict = hidden_size
lowercase__: int = num_hidden_layers
lowercase__: List[Any] = num_attention_heads
lowercase__: List[str] = intermediate_size
lowercase__: Any = hidden_act
lowercase__: List[str] = hidden_dropout_prob
lowercase__: Dict = attention_probs_dropout_prob
lowercase__: Optional[Any] = initializer_range
lowercase__: Tuple = layer_norm_eps
lowercase__: Optional[Any] = image_size
lowercase__: List[str] = patch_size
lowercase__: List[str] = num_channels
lowercase__: List[Any] = use_mask_token
lowercase__: Tuple = use_absolute_position_embeddings
lowercase__: Tuple = use_relative_position_bias
lowercase__: int = use_shared_relative_position_bias
lowercase__: Dict = layer_scale_init_value
lowercase__: List[Any] = drop_path_rate
lowercase__: Optional[int] = use_mean_pooling
# decode head attributes (semantic segmentation)
lowercase__: Optional[Any] = out_indices
lowercase__: Tuple = pool_scales
# auxiliary head attributes (semantic segmentation)
lowercase__: Dict = use_auxiliary_head
lowercase__: Union[str, Any] = auxiliary_loss_weight
lowercase__: Tuple = auxiliary_channels
lowercase__: Any = auxiliary_num_convs
lowercase__: Optional[Any] = auxiliary_concat_input
lowercase__: Optional[int] = semantic_loss_ignore_index
class __a ( __UpperCamelCase ):
__lowercase : Optional[int] = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> float:
'''simple docstring'''
return 1E-4
| 335 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 662 |
UpperCAmelCase_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def A__ ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def A__ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase = '''Morse code here!'''
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = encrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = decrypt(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main() | 32 | 0 |
from collections import Counter
from timeit import timeit
def _lowerCamelCase ( SCREAMING_SNAKE_CASE = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def _lowerCamelCase ( SCREAMING_SNAKE_CASE = "" ):
if len(SCREAMING_SNAKE_CASE ) == 0:
return True
A_ = input_str.replace(''' ''' , '''''' ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
A_ = {}
for character in lower_case_input_str:
A_ = character_freq_dict.get(SCREAMING_SNAKE_CASE , 0 ) + 1
A_ = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def _lowerCamelCase ( SCREAMING_SNAKE_CASE = "" ):
print('''\nFor string = ''' , SCREAMING_SNAKE_CASE , ''':''' )
print(
'''> can_string_be_rearranged_as_palindrome_counter()''' , '''\tans =''' , can_string_be_rearranged_as_palindrome_counter(SCREAMING_SNAKE_CASE ) , '''\ttime =''' , timeit(
'''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)''' , setup='''import __main__ as z''' , ) , '''seconds''' , )
print(
'''> can_string_be_rearranged_as_palindrome()''' , '''\tans =''' , can_string_be_rearranged_as_palindrome(SCREAMING_SNAKE_CASE ) , '''\ttime =''' , timeit(
'''z.can_string_be_rearranged_as_palindrome(z.check_str)''' , setup='''import __main__ as z''' , ) , '''seconds''' , )
if __name__ == "__main__":
__lowercase = input(
"""Enter string to determine if it can be rearranged as a palindrome or not: """
).strip()
benchmark(check_str)
__lowercase = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f'{check_str} can {"" if status else "not "}be rearranged as a palindrome')
| 720 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = 0
while b > 0:
if b & 1:
A_ = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 563 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Any = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 44 |
"""simple docstring"""
_snake_case = 6_5521
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[str] = 1
_a : Optional[int] = 0
for plain_chr in plain_text:
_a : Dict = (a + ord(UpperCamelCase__ )) % MOD_ADLER
_a : List[Any] = (b + a) % MOD_ADLER
return (b << 1_6) | a
| 389 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase__ = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def SCREAMING_SNAKE_CASE( UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
for item in items:
if any(marker in item.keywords for marker in ['integration', 'unit'] ):
continue
item.add_marker(pytest.mark.unit )
def SCREAMING_SNAKE_CASE( UpperCamelCase ) -> Union[str, Any]:
config.addinivalue_line('markers' ,'torchaudio_latest: mark test to run with torchaudio>=0.12' )
@pytest.fixture(autouse=UpperCamelCase )
def SCREAMING_SNAKE_CASE( UpperCamelCase ,UpperCamelCase ) -> Dict:
# test_hf_cache_home = tmp_path_factory.mktemp("cache") # TODO: why a cache dir per test function does not work?
UpperCAmelCase_ : Any = tmp_path_factory.getbasetemp() / 'cache'
UpperCAmelCase_ : Optional[Any] = test_hf_cache_home / 'datasets'
UpperCAmelCase_ : Any = test_hf_cache_home / 'metrics'
UpperCAmelCase_ : Optional[int] = test_hf_cache_home / 'modules'
monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' ,str(UpperCamelCase ) )
monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' ,str(UpperCamelCase ) )
monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' ,str(UpperCamelCase ) )
UpperCAmelCase_ : Any = test_hf_datasets_cache / 'downloads'
monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' ,str(UpperCamelCase ) )
UpperCAmelCase_ : int = test_hf_datasets_cache / 'downloads' / 'extracted'
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' ,str(UpperCamelCase ) )
@pytest.fixture(autouse=UpperCamelCase ,scope='session' )
def SCREAMING_SNAKE_CASE( ) -> Optional[Any]:
datasets.disable_progress_bar()
@pytest.fixture(autouse=UpperCamelCase )
def SCREAMING_SNAKE_CASE( UpperCamelCase ) -> str:
# don't take tests into account when counting downloads
monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' ,UpperCamelCase )
@pytest.fixture
def SCREAMING_SNAKE_CASE( UpperCamelCase ) -> int:
# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
# To be removed once SQLAlchemy 2.0 supported
monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' ,UpperCamelCase )
| 471 |
'''simple docstring'''
import numpy
class lowercase :
def __init__( self , _snake_case , _snake_case) -> None:
UpperCAmelCase_ : Optional[Any] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
UpperCAmelCase_ : Tuple = numpy.random.rand(
self.input_array.shape[1] , 4)
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
UpperCAmelCase_ : List[str] = numpy.random.rand(
4 , 3)
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
UpperCAmelCase_ : Dict = numpy.random.rand(3 , 1)
# Real output values provided.
UpperCAmelCase_ : str = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
UpperCAmelCase_ : Union[str, Any] = numpy.zeros(output_array.shape)
def _snake_case ( self) -> numpy.ndarray:
UpperCAmelCase_ : Any = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights))
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
UpperCAmelCase_ : Tuple = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ))
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
UpperCAmelCase_ : int = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ))
return self.layer_between_second_hidden_layer_and_output
def _snake_case ( self) -> None:
UpperCAmelCase_ : Optional[int] = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output) , )
UpperCAmelCase_ : Any = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer) , )
UpperCAmelCase_ : Tuple = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def _snake_case ( self , _snake_case , _snake_case , _snake_case) -> None:
for iteration in range(1 , iterations + 1):
UpperCAmelCase_ : int = self.feedforward()
self.back_propagation()
if give_loss:
UpperCAmelCase_ : List[Any] = numpy.mean(numpy.square(output - self.feedforward()))
print(F"""Iteration {iteration} Loss: {loss}""")
def _snake_case ( self , _snake_case) -> int:
UpperCAmelCase_ : Optional[int] = input_arr
UpperCAmelCase_ : Tuple = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights))
UpperCAmelCase_ : Optional[int] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ))
UpperCAmelCase_ : int = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ))
return int(self.layer_between_second_hidden_layer_and_output > 0.6)
def SCREAMING_SNAKE_CASE( UpperCamelCase ) -> numpy.ndarray:
return 1 / (1 + numpy.exp(-value ))
def SCREAMING_SNAKE_CASE( UpperCamelCase ) -> numpy.ndarray:
return (value) * (1 - (value))
def SCREAMING_SNAKE_CASE( ) -> int:
UpperCAmelCase_ : Optional[int] = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) ,dtype=numpy.floataa ,)
# True output values for the given input values.
UpperCAmelCase_ : Dict = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) ,dtype=numpy.floataa )
# Calling neural network class.
UpperCAmelCase_ : List[str] = TwoHiddenLayerNeuralNetwork(
input_array=UpperCamelCase ,output_array=UpperCamelCase )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=UpperCamelCase ,iterations=1_0 ,give_loss=UpperCamelCase )
return neural_network.predict(numpy.array(([1, 1, 1]) ,dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 471 | 1 |
"""simple docstring"""
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = size
lowerCAmelCase = [0] * size
lowerCAmelCase = [0] * size
@staticmethod
def UpperCamelCase__ ( _snake_case ):
"""simple docstring"""
return index | (index + 1)
@staticmethod
def UpperCamelCase__ ( _snake_case ):
"""simple docstring"""
return (index & (index + 1)) - 1
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = value
while index < self.size:
lowerCAmelCase = self.get_prev(_snake_case ) + 1
if current_left_border == index:
lowerCAmelCase = value
else:
lowerCAmelCase = max(_snake_case , _snake_case , _snake_case )
lowerCAmelCase = self.get_next(_snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
right -= 1 # Because of right is exclusive
lowerCAmelCase = 0
while left <= right:
lowerCAmelCase = self.get_prev(_snake_case )
if left <= current_left:
lowerCAmelCase = max(_snake_case , self.tree[right] )
lowerCAmelCase = current_left
else:
lowerCAmelCase = max(_snake_case , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BridgeTowerImageProcessor
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Tuple , __lowerCamelCase : Dict , __lowerCamelCase : bool = True , __lowerCamelCase : Dict[str, int] = None , __lowerCamelCase : int = 3_2 , __lowerCamelCase : bool = True , __lowerCamelCase : Union[int, float] = 1 / 2_5_5 , __lowerCamelCase : bool = True , __lowerCamelCase : bool = True , __lowerCamelCase : Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , __lowerCamelCase : Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , __lowerCamelCase : bool = True , __lowerCamelCase : str=7 , __lowerCamelCase : Union[str, Any]=3_0 , __lowerCamelCase : Tuple=4_0_0 , __lowerCamelCase : List[Any]=3 , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = do_resize
_SCREAMING_SNAKE_CASE = size if size is not None else {"shortest_edge": 2_8_8}
_SCREAMING_SNAKE_CASE = size_divisor
_SCREAMING_SNAKE_CASE = do_rescale
_SCREAMING_SNAKE_CASE = rescale_factor
_SCREAMING_SNAKE_CASE = do_normalize
_SCREAMING_SNAKE_CASE = do_center_crop
_SCREAMING_SNAKE_CASE = image_mean
_SCREAMING_SNAKE_CASE = image_std
_SCREAMING_SNAKE_CASE = do_pad
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = min_resolution
_SCREAMING_SNAKE_CASE = max_resolution
def lowerCAmelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
}
def lowerCAmelCase_ ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : int=False ):
"""simple docstring"""
if not batched:
_SCREAMING_SNAKE_CASE = self.size["shortest_edge"]
_SCREAMING_SNAKE_CASE = image_inputs[0]
if isinstance(__lowerCamelCase , Image.Image ):
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = image.size
else:
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2]
_SCREAMING_SNAKE_CASE = size / min(__lowerCamelCase , __lowerCamelCase )
if h < w:
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = size, scale * w
else:
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = scale * h, size
_SCREAMING_SNAKE_CASE = int((1_3_3_3 / 8_0_0) * size )
if max(__lowerCamelCase , __lowerCamelCase ) > max_size:
_SCREAMING_SNAKE_CASE = max_size / max(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = newh * scale
_SCREAMING_SNAKE_CASE = neww * scale
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = int(newh + 0.5 ), int(neww + 0.5 )
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
_SCREAMING_SNAKE_CASE = []
for image in image_inputs:
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_SCREAMING_SNAKE_CASE = max(__lowerCamelCase , key=lambda __lowerCamelCase : item[0] )[0]
_SCREAMING_SNAKE_CASE = max(__lowerCamelCase , key=lambda __lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase_ ( A , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase_ = BridgeTowerImageProcessor if is_vision_available() else None
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = BridgeTowerImageProcessingTester(self )
@property
def lowerCAmelCase_ ( self : int ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCamelCase , "image_mean" ) )
self.assertTrue(hasattr(__lowerCamelCase , "image_std" ) )
self.assertTrue(hasattr(__lowerCamelCase , "do_normalize" ) )
self.assertTrue(hasattr(__lowerCamelCase , "do_resize" ) )
self.assertTrue(hasattr(__lowerCamelCase , "size" ) )
self.assertTrue(hasattr(__lowerCamelCase , "size_divisor" ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
# Initialize image processor
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase , Image.Image )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE = image_processing(__lowerCamelCase , return_tensors="pt" ).pixel_values
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
# Initialize image processor
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCamelCase , numpify=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase , np.ndarray )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE = image_processing(__lowerCamelCase , return_tensors="pt" ).pixel_values
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
# Initialize image processor
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCamelCase , torchify=__lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCamelCase , torch.Tensor )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE = image_processing(__lowerCamelCase , return_tensors="pt" ).pixel_values
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(__lowerCamelCase , batched=__lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
| 418 | 0 |
"""simple docstring"""
A : Optional[Any] = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental | 701 | """simple docstring"""
def snake_case__ ( _snake_case : int , _snake_case : int , _snake_case : int ):
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase__ = _modexpt(_snake_case , exponent // 2 , _snake_case ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(_snake_case , exponent - 1 , _snake_case )) % modulo_value
def snake_case__ ( _snake_case : int = 17_77 , _snake_case : int = 18_55 , _snake_case : int = 8 ):
"""simple docstring"""
UpperCamelCase__ = base
for _ in range(1 , _snake_case ):
UpperCamelCase__ = _modexpt(_snake_case , _snake_case , 10**digits )
return result
if __name__ == "__main__":
print(F"{solution() = }") | 304 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_case ( UpperCamelCase_ ):
_a = ['''image_processor''', '''tokenizer''']
_a = '''ViltImageProcessor'''
_a = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[Any] , A_ : str=None , A_ : Any=None , **A_ : Dict):
lowerCAmelCase_ : Optional[int] = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , A_ , )
lowerCAmelCase_ : Optional[int] = kwargs.pop('''feature_extractor''')
lowerCAmelCase_ : Optional[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''')
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''')
super().__init__(A_ , A_)
lowerCAmelCase_ : Any = self.image_processor
def __call__( self : str , A_ : str , A_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , A_ : bool = True , A_ : Union[bool, str, PaddingStrategy] = False , A_ : Union[bool, str, TruncationStrategy] = None , A_ : Optional[int] = None , A_ : int = 0 , A_ : Optional[int] = None , A_ : Optional[bool] = None , A_ : Optional[bool] = None , A_ : bool = False , A_ : bool = False , A_ : bool = False , A_ : bool = False , A_ : bool = True , A_ : Optional[Union[str, TensorType]] = None , **A_ : Optional[Any] , ):
lowerCAmelCase_ : List[str] = self.tokenizer(
text=A_ , add_special_tokens=A_ , padding=A_ , truncation=A_ , max_length=A_ , stride=A_ , pad_to_multiple_of=A_ , return_token_type_ids=A_ , return_attention_mask=A_ , return_overflowing_tokens=A_ , return_special_tokens_mask=A_ , return_offsets_mapping=A_ , return_length=A_ , verbose=A_ , return_tensors=A_ , **A_ , )
# add pixel_values + pixel_mask
lowerCAmelCase_ : List[Any] = self.image_processor(A_ , return_tensors=A_)
encoding.update(A_)
return encoding
def UpperCAmelCase__ ( self : Tuple , *A_ : Union[str, Any] , **A_ : Any):
return self.tokenizer.batch_decode(*A_ , **A_)
def UpperCAmelCase__ ( self : Optional[Any] , *A_ : List[str] , **A_ : Union[str, Any]):
return self.tokenizer.decode(*A_ , **A_)
@property
def UpperCAmelCase__ ( self : int):
lowerCAmelCase_ : List[str] = self.tokenizer.model_input_names
lowerCAmelCase_ : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@property
def UpperCAmelCase__ ( self : Union[str, Any]):
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , A_ , )
return self.image_processor_class
@property
def UpperCAmelCase__ ( self : Dict):
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , A_ , )
return self.image_processor
| 171 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A__ : List[str] = 16
A__ : Dict = 32
def UpperCamelCase( __UpperCamelCase : Accelerator ,__UpperCamelCase : int = 16 ):
lowerCAmelCase_ : Union[str, Any] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowerCAmelCase_ : int = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(__UpperCamelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ : Tuple = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowerCAmelCase_ : str = datasets.map(
__UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ : Any = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(__UpperCamelCase : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase_ : str = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase_ : Optional[int] = 16
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ : Optional[int] = 8
else:
lowerCAmelCase_ : Optional[int] = None
return tokenizer.pad(
__UpperCamelCase ,padding='''longest''' ,max_length=__UpperCamelCase ,pad_to_multiple_of=__UpperCamelCase ,return_tensors='''pt''' ,)
# Instantiate dataloaders.
lowerCAmelCase_ : Tuple = DataLoader(
tokenized_datasets['''train'''] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
lowerCAmelCase_ : List[Any] = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=__UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
A__ : Any = mocked_dataloaders # noqa: F811
def UpperCamelCase( __UpperCamelCase : Union[str, Any] ,__UpperCamelCase : str ):
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,__UpperCamelCase ) == "1":
lowerCAmelCase_ : Dict = 2
# New Code #
lowerCAmelCase_ : List[str] = int(args.gradient_accumulation_steps )
lowerCAmelCase_ : Union[str, Any] = int(args.local_sgd_steps )
# Initialize accelerator
lowerCAmelCase_ : Optional[Any] = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=__UpperCamelCase )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ : List[Any] = config['''lr''']
lowerCAmelCase_ : List[Any] = int(config['''num_epochs'''] )
lowerCAmelCase_ : int = int(config['''seed'''] )
lowerCAmelCase_ : Optional[Any] = int(config['''batch_size'''] )
lowerCAmelCase_ : Optional[Any] = evaluate.load('''glue''' ,'''mrpc''' )
set_seed(__UpperCamelCase )
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = get_dataloaders(__UpperCamelCase ,__UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,return_dict=__UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowerCAmelCase_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ : Tuple = AdamW(params=model.parameters() ,lr=__UpperCamelCase )
# Instantiate scheduler
lowerCAmelCase_ : Any = get_linear_schedule_with_warmup(
optimizer=__UpperCamelCase ,num_warmup_steps=100 ,num_training_steps=(len(__UpperCamelCase ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = accelerator.prepare(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# Now we train the model
for epoch in range(__UpperCamelCase ):
model.train()
with LocalSGD(
accelerator=__UpperCamelCase ,model=__UpperCamelCase ,local_sgd_steps=__UpperCamelCase ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(__UpperCamelCase ):
lowerCAmelCase_ : str = model(**__UpperCamelCase )
lowerCAmelCase_ : Tuple = output.loss
accelerator.backward(__UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(__UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**__UpperCamelCase )
lowerCAmelCase_ : int = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__UpperCamelCase ,references=__UpperCamelCase ,)
lowerCAmelCase_ : Optional[int] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" ,__UpperCamelCase )
def UpperCamelCase( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' ,type=__UpperCamelCase ,default=__UpperCamelCase ,choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' ,)
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' ,type=__UpperCamelCase ,default=1 ,help='''The number of minibatches to be ran before gradients are accumulated.''' ,)
parser.add_argument(
'''--local_sgd_steps''' ,type=__UpperCamelCase ,default=8 ,help='''Number of local SGD steps or None to disable local SGD''' )
parser.add_argument('''--cpu''' ,action='''store_true''' ,help='''If passed, will train on the CPU.''' )
lowerCAmelCase_ : List[Any] = parser.parse_args()
lowerCAmelCase_ : List[Any] = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__UpperCamelCase ,__UpperCamelCase )
if __name__ == "__main__":
main()
| 171 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCAmelCase__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ : Any = KandinskyInpaintPipeline
lowerCAmelCase__ : List[str] = ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
lowerCAmelCase__ : str = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
lowerCAmelCase__ : List[Any] = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowerCAmelCase__ : int = False
@property
def _UpperCAmelCase ( self: Union[str, Any] ) -> Any:
'''simple docstring'''
return 32
@property
def _UpperCAmelCase ( self: int ) -> Any:
'''simple docstring'''
return 32
@property
def _UpperCAmelCase ( self: Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim
@property
def _UpperCAmelCase ( self: Optional[int] ) -> Any:
'''simple docstring'''
return self.time_input_dim * 4
@property
def _UpperCAmelCase ( self: List[str] ) -> Any:
'''simple docstring'''
return 100
@property
def _UpperCAmelCase ( self: Optional[Any] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase = XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
return tokenizer
@property
def _UpperCAmelCase ( self: Optional[int] ) -> Tuple:
'''simple docstring'''
torch.manual_seed(0 )
__UpperCAmelCase = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_005 , )
__UpperCAmelCase = MultilingualCLIP(__lowerCAmelCase )
__UpperCAmelCase = text_encoder.eval()
return text_encoder
@property
def _UpperCAmelCase ( self: List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__UpperCAmelCase = {
"in_channels": 9,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "text_image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "text_image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
__UpperCAmelCase = UNetaDConditionModel(**__lowerCAmelCase )
return model
@property
def _UpperCAmelCase ( self: Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _UpperCAmelCase ( self: Optional[int] ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__UpperCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def _UpperCAmelCase ( self: int ) -> Dict:
'''simple docstring'''
__UpperCAmelCase = self.dummy_text_encoder
__UpperCAmelCase = self.dummy_tokenizer
__UpperCAmelCase = self.dummy_unet
__UpperCAmelCase = self.dummy_movq
__UpperCAmelCase = DDIMScheduler(
num_train_timesteps=1_000 , beta_schedule="linear" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__lowerCAmelCase , set_alpha_to_one=__lowerCAmelCase , steps_offset=1 , prediction_type="epsilon" , thresholding=__lowerCAmelCase , )
__UpperCAmelCase = {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def _UpperCAmelCase ( self: Optional[int] , __lowerCAmelCase: List[str] , __lowerCAmelCase: Optional[int]=0 ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
__UpperCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__lowerCAmelCase )
# create init_image
__UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
__UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__UpperCAmelCase = Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert("RGB" ).resize((256, 256) )
# create mask
__UpperCAmelCase = np.ones((64, 64) , dtype=np.floataa )
__UpperCAmelCase = 0
if str(__lowerCAmelCase ).startswith("mps" ):
__UpperCAmelCase = torch.manual_seed(__lowerCAmelCase )
else:
__UpperCAmelCase = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
__UpperCAmelCase = {
"prompt": "horse",
"image": init_image,
"mask_image": mask,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 2,
"guidance_scale": 4.0,
"output_type": "np",
}
return inputs
def _UpperCAmelCase ( self: Optional[int] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase = "cpu"
__UpperCAmelCase = self.get_dummy_components()
__UpperCAmelCase = self.pipeline_class(**__lowerCAmelCase )
__UpperCAmelCase = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
__UpperCAmelCase = pipe(**self.get_dummy_inputs(__lowerCAmelCase ) )
__UpperCAmelCase = output.images
__UpperCAmelCase = pipe(
**self.get_dummy_inputs(__lowerCAmelCase ) , return_dict=__lowerCAmelCase , )[0]
__UpperCAmelCase = image[0, -3:, -3:, -1]
__UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
print(F'''image.shape {image.shape}''' )
assert image.shape == (1, 64, 64, 3)
__UpperCAmelCase = np.array(
[0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
def _UpperCAmelCase ( self: List[str] ) -> Optional[int]:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self: str ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self: Dict ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )
__UpperCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
__UpperCAmelCase = np.ones((768, 768) , dtype=np.floataa )
__UpperCAmelCase = 0
__UpperCAmelCase = "a hat"
__UpperCAmelCase = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa )
pipe_prior.to(__lowerCAmelCase )
__UpperCAmelCase = KandinskyInpaintPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-1-inpaint" , torch_dtype=torch.floataa )
__UpperCAmelCase = pipeline.to(__lowerCAmelCase )
pipeline.set_progress_bar_config(disable=__lowerCAmelCase )
__UpperCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__UpperCAmelCase , __UpperCAmelCase = pipe_prior(
__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
__UpperCAmelCase = pipeline(
__lowerCAmelCase , image=__lowerCAmelCase , mask_image=__lowerCAmelCase , image_embeds=__lowerCAmelCase , negative_image_embeds=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=100 , height=768 , width=768 , output_type="np" , )
__UpperCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
| 286 | import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ : int = DiTPipeline
lowerCAmelCase__ : Any = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
lowerCAmelCase__ : Optional[Any] = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
lowerCAmelCase__ : List[Any] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
lowerCAmelCase__ : Optional[int] = False
def _UpperCAmelCase ( self: str ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=__lowerCAmelCase , activation_fn="gelu-approximate" , num_embeds_ada_norm=1_000 , norm_type="ada_norm_zero" , norm_elementwise_affine=__lowerCAmelCase , )
__UpperCAmelCase = AutoencoderKL()
__UpperCAmelCase = DDIMScheduler()
__UpperCAmelCase = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def _UpperCAmelCase ( self: str , __lowerCAmelCase: str , __lowerCAmelCase: Optional[int]=0 ) -> int:
'''simple docstring'''
if str(__lowerCAmelCase ).startswith("mps" ):
__UpperCAmelCase = torch.manual_seed(__lowerCAmelCase )
else:
__UpperCAmelCase = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
__UpperCAmelCase = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def _UpperCAmelCase ( self: Tuple ) -> Dict:
'''simple docstring'''
__UpperCAmelCase = "cpu"
__UpperCAmelCase = self.get_dummy_components()
__UpperCAmelCase = self.pipeline_class(**__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
__UpperCAmelCase = self.get_dummy_inputs(__lowerCAmelCase )
__UpperCAmelCase = pipe(**__lowerCAmelCase ).images
__UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
__UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
__UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__lowerCAmelCase , 1E-3 )
def _UpperCAmelCase ( self: int ) -> str:
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=__lowerCAmelCase , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _UpperCAmelCase ( self: Optional[int] ) -> str:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self: Any ) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self: Dict ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase = torch.manual_seed(0 )
__UpperCAmelCase = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
__UpperCAmelCase = ["vase", "umbrella", "white shark", "white wolf"]
__UpperCAmelCase = pipe.get_label_ids(__lowerCAmelCase )
__UpperCAmelCase = pipe(__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(__lowerCAmelCase , __lowerCAmelCase ):
__UpperCAmelCase = load_numpy(
F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy''' )
assert np.abs((expected_image - image).max() ) < 1E-2
def _UpperCAmelCase ( self: Any ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
__UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
__UpperCAmelCase = ["vase", "umbrella"]
__UpperCAmelCase = pipe.get_label_ids(__lowerCAmelCase )
__UpperCAmelCase = torch.manual_seed(0 )
__UpperCAmelCase = pipe(__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(__lowerCAmelCase , __lowerCAmelCase ):
__UpperCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
F'''/dit/{word}_512.npy''' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 286 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__snake_case :Optional[Any] =abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCamelCase_ ( lowerCAmelCase__ : Union[str, Any] ) -> Any:
'''simple docstring'''
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def lowerCamelCase_ ( lowerCAmelCase__ : List[str] ) -> Any:
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowerCAmelCase__ )
def lowerCamelCase_ ( lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
A = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(lowerCAmelCase__ , id=lowerCAmelCase__ )
def lowerCamelCase_ ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
if exitstatus == 5:
A = 0
# Doctest custom flag to ignore output.
__snake_case :int =doctest.register_optionflag('IGNORE_RESULT')
__snake_case :List[str] =doctest.OutputChecker
class lowerCAmelCase__ ( _lowerCamelCase ):
def __UpperCamelCase ( self : Tuple , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] , __UpperCamelCase : str ) -> List[Any]:
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
__snake_case :str =CustomOutputChecker
__snake_case :List[Any] =HfDoctestModule
__snake_case :str =HfDocTestParser | 106 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase ( lowercase__ ):
lowercase = ['''image_processor''', '''tokenizer''']
lowercase = '''CLIPImageProcessor'''
lowercase = ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')
def __init__(self : Union[str, Any] ,SCREAMING_SNAKE_CASE_ : int=None ,SCREAMING_SNAKE_CASE_ : str=None ,**SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> int:
"""simple docstring"""
lowerCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' ,SCREAMING_SNAKE_CASE_ ,)
lowerCAmelCase = kwargs.pop('''feature_extractor''' )
lowerCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def __call__(self : Union[str, Any] ,SCREAMING_SNAKE_CASE_ : List[str]=None ,SCREAMING_SNAKE_CASE_ : Optional[int]=None ,SCREAMING_SNAKE_CASE_ : Union[str, Any]=None ,**SCREAMING_SNAKE_CASE_ : Any ) -> List[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowerCAmelCase = self.tokenizer(SCREAMING_SNAKE_CASE_ ,return_tensors=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
if images is not None:
lowerCAmelCase = self.image_processor(SCREAMING_SNAKE_CASE_ ,return_tensors=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
if text is not None and images is not None:
lowerCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_ ) ,tensor_type=SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase (self : List[str] ,*SCREAMING_SNAKE_CASE_ : Union[str, Any] ,**SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase (self : Union[str, Any] ,*SCREAMING_SNAKE_CASE_ : int ,**SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Any:
"""simple docstring"""
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
@property
def UpperCAmelCase (self : List[Any] ) -> Dict:
"""simple docstring"""
lowerCAmelCase = self.tokenizer.model_input_names
lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 535 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Union[str, Any] = '''gpt_neox'''
def __init__( self , lowerCAmelCase_=5_04_32 , lowerCAmelCase_=61_44 , lowerCAmelCase_=44 , lowerCAmelCase_=64 , lowerCAmelCase_=2_45_76 , lowerCAmelCase_="gelu" , lowerCAmelCase_=0.25 , lowerCAmelCase_=1_00_00 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.1 , lowerCAmelCase_=20_48 , lowerCAmelCase_=0.02 , lowerCAmelCase_=1E-5 , lowerCAmelCase_=True , lowerCAmelCase_=0 , lowerCAmelCase_=2 , lowerCAmelCase_=False , lowerCAmelCase_=True , lowerCAmelCase_=None , **lowerCAmelCase_ , ) -> Dict:
super().__init__(bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
_A = vocab_size
_A = max_position_embeddings
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = rotary_pct
_A = rotary_emb_base
_A = attention_dropout
_A = hidden_dropout
_A = classifier_dropout
_A = initializer_range
_A = layer_norm_eps
_A = use_cache
_A = tie_word_embeddings
_A = use_parallel_residual
_A = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"""The hidden size is not divisble by the number of attention heads! Make sure to update them!""" )
def UpperCAmelCase ( self ) -> Dict:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , lowerCAmelCase_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
F'''got {self.rope_scaling}''' )
_A = self.rope_scaling.get("""type""" , lowerCAmelCase_ )
_A = self.rope_scaling.get("""factor""" , lowerCAmelCase_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
if rope_scaling_factor is None or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 83 | from collections import defaultdict
def snake_case ( snake_case__ :int) -> int:
_A = 1
_A = True
for v in tree[start]:
if v not in visited:
ret += dfs(snake_case__)
if ret % 2 == 0:
cuts.append(snake_case__)
return ret
def snake_case ( ) -> Any:
dfs(1)
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 10, 9
_SCREAMING_SNAKE_CASE = defaultdict(list)
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 83 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] ):
"""simple docstring"""
a_ : Optional[int] = (boundary[1] - boundary[0]) / steps
a_ : Optional[Any] = boundary[0]
a_ : Dict = boundary[1]
a_ : Dict = make_points(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
a_ : Any = 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 _SCREAMING_SNAKE_CASE ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : List[str] ):
"""simple docstring"""
a_ : str = a + h
while x < (b - h):
yield x
a_ : Optional[Any] = x + h
def _SCREAMING_SNAKE_CASE ( UpperCamelCase__ : str ): # enter your function here
"""simple docstring"""
a_ : int = (x - 0) * (x - 0)
return y
def _SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
a_ : Optional[int] = 0.0 # Lower bound of integration
a_ : List[Any] = 1.0 # Upper bound of integration
a_ : Union[str, Any] = 10.0 # define number of steps or resolution
a_ : List[str] = [a, b] # define boundary of integration
a_ : Optional[int] = method_a(UpperCamelCase__ , UpperCamelCase__ )
print(F"y = {y}" )
if __name__ == "__main__":
main()
| 442 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : int = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
lowerCAmelCase_ : Union[str, Any] = {
'vocab_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
),
},
'merges_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
),
},
'tokenizer_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
'roberta-base-openai-detector': (
'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
),
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase_ : Union[str, Any] = {
'roberta-base': 512,
'roberta-large': 512,
'roberta-large-mnli': 512,
'distilroberta-base': 512,
'roberta-base-openai-detector': 512,
'roberta-large-openai-detector': 512,
}
class SCREAMING_SNAKE_CASE ( snake_case_ ):
__magic_name__ : int = VOCAB_FILES_NAMES
__magic_name__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ : List[str] = ['''input_ids''', '''attention_mask''']
__magic_name__ : Any = RobertaTokenizer
def __init__( self : Any , lowercase__ : int=None , lowercase__ : Union[str, Any]=None , lowercase__ : List[Any]=None , lowercase__ : int="replace" , lowercase__ : Dict="<s>" , lowercase__ : int="</s>" , lowercase__ : Optional[Any]="</s>" , lowercase__ : List[Any]="<s>" , lowercase__ : Optional[int]="<unk>" , lowercase__ : Tuple="<pad>" , lowercase__ : Optional[Any]="<mask>" , lowercase__ : List[Any]=False , lowercase__ : Optional[Any]=True , **lowercase__ : List[str] , ):
'''simple docstring'''
super().__init__(
lowercase__ , lowercase__ , tokenizer_file=lowercase__ , errors=lowercase__ , bos_token=lowercase__ , eos_token=lowercase__ , sep_token=lowercase__ , cls_token=lowercase__ , unk_token=lowercase__ , pad_token=lowercase__ , mask_token=lowercase__ , add_prefix_space=lowercase__ , trim_offsets=lowercase__ , **lowercase__ , )
a_ : Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , lowercase__ ) != add_prefix_space:
a_ : List[Any] = getattr(lowercase__ , pre_tok_state.pop("""type""" ) )
a_ : Optional[Any] = add_prefix_space
a_ : List[str] = pre_tok_class(**lowercase__ )
a_ : List[Any] = add_prefix_space
a_ : Any = """post_processor"""
a_ : Dict = getattr(self.backend_tokenizer , lowercase__ , lowercase__ )
if tokenizer_component_instance:
a_ : Optional[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:
a_ : Dict = tuple(state["""sep"""] )
if "cls" in state:
a_ : List[str] = tuple(state["""cls"""] )
a_ : str = False
if state.get("""add_prefix_space""" , lowercase__ ) != add_prefix_space:
a_ : Any = add_prefix_space
a_ : List[str] = True
if state.get("""trim_offsets""" , lowercase__ ) != trim_offsets:
a_ : Optional[int] = trim_offsets
a_ : Tuple = True
if changes_to_apply:
a_ : str = getattr(lowercase__ , state.pop("""type""" ) )
a_ : Union[str, Any] = component_class(**lowercase__ )
setattr(self.backend_tokenizer , lowercase__ , lowercase__ )
@property
def lowercase_ ( self : List[str] ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def lowercase_ ( self : Union[str, Any] , lowercase__ : Tuple ):
'''simple docstring'''
a_ : List[Any] = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else value
a_ : Union[str, Any] = value
def lowercase_ ( self : Union[str, Any] , *lowercase__ : Union[str, Any] , **lowercase__ : Union[str, Any] ):
'''simple docstring'''
a_ : Any = kwargs.get("""is_split_into_words""" , lowercase__ )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*lowercase__ , **lowercase__ )
def lowercase_ ( self : Dict , *lowercase__ : int , **lowercase__ : Dict ):
'''simple docstring'''
a_ : Any = kwargs.get("""is_split_into_words""" , lowercase__ )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*lowercase__ , **lowercase__ )
def lowercase_ ( self : Dict , lowercase__ : str , lowercase__ : Optional[str] = None ):
'''simple docstring'''
a_ : str = self._tokenizer.model.save(lowercase__ , name=lowercase__ )
return tuple(lowercase__ )
def lowercase_ ( self : Dict , lowercase__ : List[Any] , lowercase__ : int=None ):
'''simple docstring'''
a_ : int = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase_ ( self : List[str] , lowercase__ : List[int] , lowercase__ : Optional[List[int]] = None ):
'''simple docstring'''
a_ : Optional[Any] = [self.sep_token_id]
a_ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 442 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
_a = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class __A ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCAmelCase_ = None
def lowerCAmelCase__(__snake_case ,__snake_case ,) -> Any:
'''simple docstring'''
import pyspark
def generate_fn():
lowerCamelCase__ = df.select('''*''' ,pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) )
for partition_id in partition_order:
lowerCamelCase__ = df_with_partition_id.select('''*''' ).where(F'part_id = {partition_id}' ).drop('''part_id''' )
lowerCamelCase__ = partition_df.collect()
lowerCamelCase__ = 0
for row in rows:
yield F'{partition_id}_{row_id}', row.asDict()
row_id += 1
return generate_fn
class __A ( _BaseExamplesIterable ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=None , ):
'''simple docstring'''
lowerCamelCase__ = df
lowerCamelCase__ = partition_order or range(self.df.rdd.getNumPartitions() )
lowerCamelCase__ = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self ):
'''simple docstring'''
yield from self.generate_examples_fn()
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(__lowerCAmelCase )
return SparkExamplesIterable(self.df , partition_order=__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = self.split_shard_indices_by_worker(__lowerCAmelCase , __lowerCAmelCase )
return SparkExamplesIterable(self.df , partition_order=__lowerCAmelCase )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return len(self.partition_order )
class __A ( datasets.DatasetBuilder ):
'''simple docstring'''
lowerCAmelCase_ = SparkConfig
def __init__( self , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , **__lowerCAmelCase , ):
'''simple docstring'''
import pyspark
lowerCamelCase__ = pyspark.sql.SparkSession.builder.getOrCreate()
lowerCamelCase__ = df
lowerCamelCase__ = working_dir
super().__init__(
cache_dir=__lowerCAmelCase , config_name=str(self.df.semanticHash() ) , **__lowerCAmelCase , )
def __lowerCamelCase ( self ):
'''simple docstring'''
def create_cache_and_write_probe(__lowerCAmelCase ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=__lowerCAmelCase )
lowerCamelCase__ = os.path.join(self._cache_dir , '''fs_test''' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(__lowerCAmelCase , '''a''' )
return [probe_file]
if self._spark.conf.get('''spark.master''' , '''''' ).startswith('''local''' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
lowerCamelCase__ = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(__lowerCAmelCase ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
import pyspark
def get_arrow_batch_size(__lowerCAmelCase ):
for batch in it:
yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} )
lowerCamelCase__ = self.df.count()
lowerCamelCase__ = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
lowerCamelCase__ = (
self.df.limit(__lowerCAmelCase )
.repartition(1 )
.mapInArrow(__lowerCAmelCase , '''batch_bytes: long''' )
.agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
lowerCamelCase__ = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
lowerCamelCase__ = min(__lowerCAmelCase , int(approx_total_size / max_shard_size ) )
lowerCamelCase__ = self.df.repartition(__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
'''simple docstring'''
import pyspark
lowerCamelCase__ = ParquetWriter if file_format == '''parquet''' else ArrowWriter
lowerCamelCase__ = os.path.join(self._working_dir , os.path.basename(__lowerCAmelCase ) ) if self._working_dir else fpath
lowerCamelCase__ = file_format == '''parquet'''
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
lowerCamelCase__ = self.config.features
lowerCamelCase__ = self._writer_batch_size
lowerCamelCase__ = self._fs.storage_options
def write_arrow(__lowerCAmelCase ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
lowerCamelCase__ = pyspark.TaskContext().taskAttemptId()
lowerCamelCase__ = next(__lowerCAmelCase , __lowerCAmelCase )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
lowerCamelCase__ = 0
lowerCamelCase__ = writer_class(
features=__lowerCAmelCase , path=working_fpath.replace('''SSSSS''' , F'{shard_id:05d}' ).replace('''TTTTT''' , F'{task_id:05d}' ) , writer_batch_size=__lowerCAmelCase , storage_options=__lowerCAmelCase , embed_local_files=__lowerCAmelCase , )
lowerCamelCase__ = pa.Table.from_batches([first_batch] )
writer.write_table(__lowerCAmelCase )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
lowerCamelCase__ , lowerCamelCase__ = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
shard_id += 1
lowerCamelCase__ = writer_class(
features=writer._features , path=working_fpath.replace('''SSSSS''' , F'{shard_id:05d}' ).replace('''TTTTT''' , F'{task_id:05d}' ) , writer_batch_size=__lowerCAmelCase , storage_options=__lowerCAmelCase , embed_local_files=__lowerCAmelCase , )
lowerCamelCase__ = pa.Table.from_batches([batch] )
writer.write_table(__lowerCAmelCase )
if writer._num_bytes > 0:
lowerCamelCase__ , lowerCamelCase__ = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(__lowerCAmelCase ) ):
lowerCamelCase__ = os.path.join(os.path.dirname(__lowerCAmelCase ) , os.path.basename(__lowerCAmelCase ) )
shutil.move(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = (
self.df.mapInArrow(__lowerCAmelCase , '''task_id: long, num_examples: long, num_bytes: long''' )
.groupBy('''task_id''' )
.agg(
pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' ) , pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' ) , pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' ) , pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = "arrow" , __lowerCAmelCase = None , __lowerCAmelCase = None , **__lowerCAmelCase , ):
'''simple docstring'''
self._validate_cache_dir()
lowerCamelCase__ = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(__lowerCAmelCase )
lowerCamelCase__ = not is_remote_filesystem(self._fs )
lowerCamelCase__ = os.path.join if is_local else posixpath.join
lowerCamelCase__ = '''-TTTTT-SSSSS-of-NNNNN'''
lowerCamelCase__ = F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
lowerCamelCase__ = path_join(self._output_dir , __lowerCAmelCase )
lowerCamelCase__ = 0
lowerCamelCase__ = 0
lowerCamelCase__ = 0
lowerCamelCase__ = []
lowerCamelCase__ = []
for task_id, content in self._prepare_split_single(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(__lowerCAmelCase )
lowerCamelCase__ = total_num_examples
lowerCamelCase__ = total_num_bytes
# should rename everything at the end
logger.debug(F'Renaming {total_shards} shards.' )
if total_shards > 1:
lowerCamelCase__ = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
lowerCamelCase__ = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ):
rename(
__lowerCAmelCase , fpath.replace('''SSSSS''' , F'{shard_id:05d}' ).replace('''TTTTT''' , F'{task_id:05d}' ) , fpath.replace('''TTTTT-SSSSS''' , F'{global_shard_id:05d}' ).replace('''NNNNN''' , F'{total_shards:05d}' ) , )
lowerCamelCase__ = []
lowerCamelCase__ = 0
for i in range(len(__lowerCAmelCase ) ):
lowerCamelCase__ , lowerCamelCase__ = task_id_and_num_shards[i]
for shard_id in range(__lowerCAmelCase ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(__lowerCAmelCase , len(__lowerCAmelCase ) ).map(lambda __lowerCAmelCase : _rename_shard(*__lowerCAmelCase ) ).collect()
else:
# don't use any pattern
lowerCamelCase__ = 0
lowerCamelCase__ = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('''SSSSS''' , F'{shard_id:05d}' ).replace('''TTTTT''' , F'{task_id:05d}' ) , fpath.replace(__lowerCAmelCase , '''''' ) , )
def __lowerCamelCase ( self , __lowerCAmelCase , ):
'''simple docstring'''
return SparkExamplesIterable(self.df )
| 29 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None ) -> None:
'''simple docstring'''
if start is None:
lowerCamelCase__ = 0
if end is None:
lowerCamelCase__ = len(__snake_case ) - 1
if start >= end:
return
lowerCamelCase__ = (start + end) // 2
slowsort(__snake_case ,__snake_case ,__snake_case )
slowsort(__snake_case ,mid + 1 ,__snake_case )
if sequence[end] < sequence[mid]:
lowerCamelCase__ , lowerCamelCase__ = sequence[mid], sequence[end]
slowsort(__snake_case ,__snake_case ,end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 29 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None ) -> List[str]:
"""simple docstring"""
if attention_mask is None:
__snake_case = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class __magic_name__ :
_SCREAMING_SNAKE_CASE : List[str] = OPTConfig
_SCREAMING_SNAKE_CASE : Union[str, Any] = {}
_SCREAMING_SNAKE_CASE : List[Any] = 'gelu'
def __init__( self : List[Any] , snake_case_ : List[Any] , snake_case_ : Dict=13 , snake_case_ : Optional[Any]=7 , snake_case_ : str=True , snake_case_ : Any=False , snake_case_ : int=99 , snake_case_ : Optional[int]=16 , snake_case_ : int=2 , snake_case_ : Dict=4 , snake_case_ : List[Any]=4 , snake_case_ : Union[str, Any]="gelu" , snake_case_ : Any=0.1 , snake_case_ : int=0.1 , snake_case_ : Optional[int]=20 , snake_case_ : int=2 , snake_case_ : Tuple=1 , snake_case_ : str=0 , snake_case_ : Dict=16 , snake_case_ : List[str]=16 , ):
__snake_case = parent
__snake_case = batch_size
__snake_case = seq_length
__snake_case = is_training
__snake_case = use_labels
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = max_position_embeddings
__snake_case = eos_token_id
__snake_case = pad_token_id
__snake_case = bos_token_id
__snake_case = embed_dim
__snake_case = word_embed_proj_dim
__snake_case = False
def lowerCAmelCase ( self : List[str] ):
__snake_case = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__snake_case = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__snake_case = tf.concat([input_ids, eos_tensor] , axis=1 )
__snake_case = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=snake_case_ , **self.config_updates , )
__snake_case = prepare_opt_inputs_dict(snake_case_ , snake_case_ )
return config, inputs_dict
def lowerCAmelCase ( self : Optional[int] , snake_case_ : Any , snake_case_ : Optional[int] ):
__snake_case = TFOPTModel(config=snake_case_ )
__snake_case = inputs_dict["input_ids"]
__snake_case = input_ids[:1, :]
__snake_case = inputs_dict["attention_mask"][:1, :]
__snake_case = 1
# first forward pass
__snake_case = model(snake_case_ , attention_mask=snake_case_ , use_cache=snake_case_ )
__snake_case , __snake_case = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__snake_case = ids_tensor((self.batch_size, 3) , config.vocab_size )
__snake_case = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__snake_case = tf.concat([input_ids, next_tokens] , axis=-1 )
__snake_case = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__snake_case = model(snake_case_ , attention_mask=snake_case_ )[0]
__snake_case = model(snake_case_ , attention_mask=snake_case_ , past_key_values=snake_case_ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__snake_case = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__snake_case = output_from_no_past[:, -3:, random_slice_idx]
__snake_case = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case_ , snake_case_ , rtol=1e-3 )
@require_tf
class __magic_name__ ( lowercase__ , lowercase__ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Tuple = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE : Tuple = (TFOPTForCausalLM,) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE : List[str] = (
{'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
)
_SCREAMING_SNAKE_CASE : List[str] = False
_SCREAMING_SNAKE_CASE : List[str] = False
_SCREAMING_SNAKE_CASE : List[Any] = False
_SCREAMING_SNAKE_CASE : Union[str, Any] = 10
def lowerCAmelCase ( self : Optional[int] ):
__snake_case = TFOPTModelTester(self )
__snake_case = ConfigTester(self , config_class=snake_case_ )
def lowerCAmelCase ( self : Tuple ):
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : List[Any] ):
__snake_case = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case_ )
def lowerCAmelCase ( self : Tuple ):
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(snake_case_ : Optional[Any] , snake_case_ : Optional[int] ):
if hasattr(snake_case_ , "weight" ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(snake_case_ , "weight" ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
__snake_case = model_class(config=snake_case_ )
__snake_case = _get_word_embedding_weight(snake_case_ , model.get_input_embeddings() )
__snake_case = _get_word_embedding_weight(snake_case_ , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(snake_case_ )
__snake_case = _get_word_embedding_weight(snake_case_ , model.get_input_embeddings() )
__snake_case = _get_word_embedding_weight(snake_case_ , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
__snake_case = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , snake_case_ )
# check that weights remain the same after resizing
__snake_case = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__snake_case = False
self.assertTrue(snake_case_ )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , snake_case_ )
__snake_case = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
__snake_case = False
self.assertTrue(snake_case_ )
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
return tf.constant(SCREAMING_SNAKE_CASE , dtype=tf.intaa )
@require_tf
class __magic_name__ ( unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = 99
def lowerCAmelCase ( self : Union[str, Any] ):
__snake_case = tf.ones((4, 1) , dtype=tf.intaa ) * 2
__snake_case = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
__snake_case = input_ids.shape[0]
__snake_case = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class __magic_name__ ( unittest.TestCase ):
@slow
def lowerCAmelCase ( self : Dict ):
__snake_case = TFOPTModel.from_pretrained("facebook/opt-350m" )
__snake_case = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__snake_case = tf.not_equal(snake_case_ , model.config.pad_token_id )
with tf.GradientTape():
__snake_case = model(input_ids=snake_case_ , attention_mask=snake_case_ ).last_hidden_state
__snake_case = (1, 11, 512)
self.assertEqual(output.shape , snake_case_ )
__snake_case = tf.constant(
[[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , snake_case_ , atol=4e-3 ) )
__snake_case = tf.function(snake_case_ , jit_compile=snake_case_ )
__snake_case = xla_generate(snake_case_ , snake_case_ )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , snake_case_ , atol=4e-2 ) )
@require_tf
@slow
class __magic_name__ ( unittest.TestCase ):
def lowerCAmelCase ( self : List[str] ):
super().setUp()
__snake_case = "facebook/opt-350m"
def lowerCAmelCase ( self : Any ):
__snake_case = TFOPTForCausalLM.from_pretrained(self.path_model )
__snake_case = GPTaTokenizer.from_pretrained(self.path_model )
__snake_case = [
"Today is a beautiful day and I want to",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
__snake_case = tokenizer(snake_case_ , return_tensors="tf" , padding=snake_case_ , add_special_tokens=snake_case_ )
__snake_case = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
__snake_case = tf.constant(
[
[1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
[-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
[0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
[6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
] )
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-4 ) )
__snake_case = tf.function(snake_case_ , jit_compile=snake_case_ )
__snake_case = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-4 ) )
@require_tf
@slow
class __magic_name__ ( unittest.TestCase ):
@property
def lowerCAmelCase ( self : Any ):
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def lowerCAmelCase ( self : List[str] ):
__snake_case = "facebook/opt-125m"
__snake_case = [
"Today is a beautiful day and I want to",
"In the city of New York, the city",
"Paris is the capital of France and the capital",
"Computers and mobile phones have taken over the",
]
__snake_case = []
__snake_case = GPTaTokenizer.from_pretrained(snake_case_ )
__snake_case = TFOPTForCausalLM.from_pretrained(snake_case_ )
for prompt in self.prompts:
__snake_case = tokenizer(snake_case_ , return_tensors="tf" ).input_ids
__snake_case = model.generate(snake_case_ , max_length=10 )
__snake_case = tokenizer.batch_decode(snake_case_ , skip_special_tokens=snake_case_ )
predicted_outputs += generated_string
self.assertListEqual(snake_case_ , snake_case_ )
def lowerCAmelCase ( self : Dict ):
__snake_case = "facebook/opt-350m"
__snake_case = GPTaTokenizer.from_pretrained(snake_case_ )
__snake_case = TFOPTForCausalLM.from_pretrained(snake_case_ )
__snake_case = "left"
# use different length sentences to test batching
__snake_case = [
"Hello, my dog is a little",
"Today, I",
]
__snake_case = tokenizer(snake_case_ , return_tensors="tf" , padding=snake_case_ )
__snake_case = inputs["input_ids"]
__snake_case = model.generate(input_ids=snake_case_ , attention_mask=inputs["attention_mask"] )
__snake_case = tokenizer(sentences[0] , return_tensors="tf" ).input_ids
__snake_case = model.generate(input_ids=snake_case_ )
__snake_case = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs["attention_mask"][-1] , tf.intaa ) )
__snake_case = tokenizer(sentences[1] , return_tensors="tf" ).input_ids
__snake_case = model.generate(input_ids=snake_case_ , max_length=model.config.max_length - num_paddings )
__snake_case = tokenizer.batch_decode(snake_case_ , skip_special_tokens=snake_case_ )
__snake_case = tokenizer.decode(output_non_padded[0] , skip_special_tokens=snake_case_ )
__snake_case = tokenizer.decode(output_padded[0] , skip_special_tokens=snake_case_ )
__snake_case = [
"Hello, my dog is a little bit of a dork.\nI'm a little bit",
"Today, I was in the middle of a conversation with a friend about the",
]
self.assertListEqual(snake_case_ , snake_case_ )
self.assertListEqual(snake_case_ , [non_padded_sentence, padded_sentence] )
def lowerCAmelCase ( self : int ):
__snake_case = "facebook/opt-350m"
__snake_case = [
"Today is a beautiful day and I want to",
"In the city of San Francisco, the city",
"Paris is the capital of France and the capital",
"Computers and mobile phones have taken over the",
]
__snake_case = []
__snake_case = GPTaTokenizer.from_pretrained(snake_case_ )
__snake_case = TFOPTForCausalLM.from_pretrained(snake_case_ )
for prompt in self.prompts:
__snake_case = tokenizer(snake_case_ , return_tensors="tf" ).input_ids
__snake_case = model.generate(snake_case_ , max_length=10 )
__snake_case = tokenizer.batch_decode(snake_case_ , skip_special_tokens=snake_case_ )
predicted_outputs += generated_string
self.assertListEqual(snake_case_ , snake_case_ )
| 163 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class __magic_name__ ( lowercase__ ):
def __init__( self : str , *snake_case_ : Optional[Any] , **snake_case_ : int ):
warnings.warn(
"The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use SegformerImageProcessor instead." , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 163 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
'''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
'''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
),
'''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'bert'
def __init__( self : List[str] ,lowerCAmelCase__ : List[Any]=3_05_22 ,lowerCAmelCase__ : List[str]=7_68 ,lowerCAmelCase__ : Optional[int]=12 ,lowerCAmelCase__ : List[Any]=12 ,lowerCAmelCase__ : int=30_72 ,lowerCAmelCase__ : List[Any]="gelu" ,lowerCAmelCase__ : Tuple=0.1 ,lowerCAmelCase__ : Tuple=0.1 ,lowerCAmelCase__ : List[str]=5_12 ,lowerCAmelCase__ : Optional[int]=2 ,lowerCAmelCase__ : Any=0.02 ,lowerCAmelCase__ : Union[str, Any]=1e-1_2 ,lowerCAmelCase__ : List[Any]=0 ,lowerCAmelCase__ : Any="absolute" ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[int]=None ,**lowerCAmelCase__ : Optional[Any] ,) -> str:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Any = vocab_size
lowerCAmelCase_ : Optional[Any] = hidden_size
lowerCAmelCase_ : Tuple = num_hidden_layers
lowerCAmelCase_ : int = num_attention_heads
lowerCAmelCase_ : str = hidden_act
lowerCAmelCase_ : List[str] = intermediate_size
lowerCAmelCase_ : Any = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : str = max_position_embeddings
lowerCAmelCase_ : int = type_vocab_size
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : Optional[int] = layer_norm_eps
lowerCAmelCase_ : Dict = position_embedding_type
lowerCAmelCase_ : Optional[Any] = use_cache
lowerCAmelCase_ : Tuple = classifier_dropout
class __snake_case ( snake_case__ ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
lowerCAmelCase_ : Any = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCAmelCase_ : Union[str, Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 683 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase = Lock()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
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(snake_case__)
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
lowerCAmelCase_ : Any = min(snake_case__ , snake_case__)
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(snake_case__)
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
lowerCAmelCase_ : str = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__)
# after all swaps are performed, send the values back to main
result_pipe[1].send(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : int = []
# 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
lowerCAmelCase_ : Tuple = Pipe()
lowerCAmelCase_ : Optional[int] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ))
lowerCAmelCase_ : int = temp_rs
lowerCAmelCase_ : List[Any] = temp_rr
for i in range(1 , len(snake_case__) - 1):
lowerCAmelCase_ : Dict = Pipe()
lowerCAmelCase_ : List[str] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ))
lowerCAmelCase_ : Dict = temp_rs
lowerCAmelCase_ : Optional[Any] = temp_rr
process_array_.append(
Process(
target=snake_case__ , args=(
len(snake_case__) - 1,
arr[len(snake_case__) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(snake_case__) - 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(snake_case__)):
lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1))
print("Initial List")
print(*snake_case__)
lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__)
print("Sorted List\n")
print(*snake_case__)
if __name__ == "__main__":
main()
| 683 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__UpperCAmelCase = random.Random()
def UpperCamelCase ( snake_case__ : List[str] , snake_case__ : Tuple=1.0 , snake_case__ : int=None , snake_case__ : Optional[Any]=None ) -> List[Any]:
if rng is None:
UpperCamelCase : Union[str, Any] = global_rng
UpperCamelCase : Union[str, Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowerCAmelCase_ ( unittest.TestCase ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=400, SCREAMING_SNAKE_CASE_=2000, SCREAMING_SNAKE_CASE_=10, SCREAMING_SNAKE_CASE_=160, SCREAMING_SNAKE_CASE_=8, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=4000, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=True, ) -> str:
UpperCamelCase : Dict = parent
UpperCamelCase : Optional[Any] = batch_size
UpperCamelCase : Optional[int] = min_seq_length
UpperCamelCase : Optional[Any] = max_seq_length
UpperCamelCase : Union[str, Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase : Optional[int] = padding_value
UpperCamelCase : Optional[Any] = sampling_rate
UpperCamelCase : List[Any] = return_attention_mask
UpperCamelCase : Union[str, Any] = do_normalize
UpperCamelCase : Tuple = feature_size
UpperCamelCase : Dict = chunk_length
UpperCamelCase : Dict = hop_length
def snake_case_ ( self ) -> List[Any]:
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def snake_case_ ( self, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=False ) -> Union[str, Any]:
def _flatten(SCREAMING_SNAKE_CASE_ ):
return list(itertools.chain(*SCREAMING_SNAKE_CASE_ ) )
if equal_length:
UpperCamelCase : Optional[int] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase : Optional[Any] = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
UpperCamelCase : Dict = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowerCAmelCase_ ( a__ , unittest.TestCase ):
UpperCAmelCase__ : str = WhisperFeatureExtractor if is_speech_available() else None
def snake_case_ ( self ) -> str:
UpperCamelCase : int = WhisperFeatureExtractionTester(self )
def snake_case_ ( self ) -> Any:
UpperCamelCase : List[str] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase : Union[str, Any] = feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_ )[0]
check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = feat_extract_first.to_dict()
UpperCamelCase : int = feat_extract_second.to_dict()
UpperCamelCase : Union[str, Any] = feat_extract_first.mel_filters
UpperCamelCase : Optional[Any] = feat_extract_second.mel_filters
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> Tuple:
UpperCamelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase : List[Any] = os.path.join(SCREAMING_SNAKE_CASE_, 'feat_extract.json' )
feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = feat_extract_first.to_dict()
UpperCamelCase : List[str] = feat_extract_second.to_dict()
UpperCamelCase : int = feat_extract_first.mel_filters
UpperCamelCase : Union[str, Any] = feat_extract_second.mel_filters
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self ) -> List[str]:
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCamelCase : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase : Dict = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase : int = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
# Test feature size
UpperCamelCase : List[Any] = feature_extractor(SCREAMING_SNAKE_CASE_, padding='max_length', return_tensors='np' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
UpperCamelCase : List[str] = feature_extractor(speech_inputs[0], return_tensors='np' ).input_features
UpperCamelCase : Tuple = feature_extractor(np_speech_inputs[0], return_tensors='np' ).input_features
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, atol=1e-3 ) )
# Test batched
UpperCamelCase : Optional[Any] = feature_extractor(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_features
UpperCamelCase : List[str] = feature_extractor(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase : Tuple = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase : Optional[Any] = np.asarray(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = feature_extractor(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_features
UpperCamelCase : Optional[Any] = feature_extractor(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, atol=1e-3 ) )
# Test truncation required
UpperCamelCase : List[Any] = [floats_list((1, x) )[0] for x in range(200, (feature_extractor.n_samples + 500), 200 )]
UpperCamelCase : List[str] = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
UpperCamelCase : List[str] = [x[: feature_extractor.n_samples] for x in speech_inputs]
UpperCamelCase : Optional[int] = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs_truncated]
UpperCamelCase : List[str] = feature_extractor(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_features
UpperCamelCase : Tuple = feature_extractor(SCREAMING_SNAKE_CASE_, return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, atol=1e-3 ) )
def snake_case_ ( self ) -> Dict:
import torch
UpperCamelCase : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Optional[int] = np.random.rand(100, 32 ).astype(np.floataa )
UpperCamelCase : Optional[Any] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase : List[str] = feature_extractor.pad([{'input_features': inputs}], return_tensors='np' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
UpperCamelCase : List[Any] = feature_extractor.pad([{'input_features': inputs}], return_tensors='pt' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
UpperCamelCase : List[Any] = load_dataset('hf-internal-testing/librispeech_asr_dummy', 'clean', split='validation' )
# automatic decoding with librispeech
UpperCamelCase : Dict = ds.sort('id' ).select(range(SCREAMING_SNAKE_CASE_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def snake_case_ ( self ) -> int:
# fmt: off
UpperCamelCase : Dict = torch.tensor(
[
0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
-0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
] )
# fmt: on
UpperCamelCase : List[Any] = self._load_datasamples(1 )
UpperCamelCase : Dict = WhisperFeatureExtractor()
UpperCamelCase : Any = feature_extractor(SCREAMING_SNAKE_CASE_, return_tensors='pt' ).input_features
self.assertEqual(input_features.shape, (1, 80, 3000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30], SCREAMING_SNAKE_CASE_, atol=1e-4 ) )
def snake_case_ ( self ) -> Union[str, Any]:
UpperCamelCase : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : str = self._load_datasamples(1 )[0]
UpperCamelCase : Optional[Any] = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535 # Rescale to [0, 65535] to show issue
UpperCamelCase : Any = feat_extract.zero_mean_unit_var_norm([audio], attention_mask=SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE_ ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE_ ) - 1 ) < 1e-3 ) )
| 40 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a__ :
def __init__( self , A = None ) -> None:
'''simple docstring'''
if components is None:
a = []
a = list(A )
def __len__( self ) -> int:
'''simple docstring'''
return len(self.__components )
def __str__( self ) -> str:
'''simple docstring'''
return "(" + ",".join(map(A , self.__components ) ) + ")"
def __add__( self , A ) -> Vector:
'''simple docstring'''
a = len(self )
if size == len(A ):
a = [self.__components[i] + other.component(A ) for i in range(A )]
return Vector(A )
else:
raise Exception("must have the same size" )
def __sub__( self , A ) -> Vector:
'''simple docstring'''
a = len(self )
if size == len(A ):
a = [self.__components[i] - other.component(A ) for i in range(A )]
return Vector(A )
else: # error case
raise Exception("must have the same size" )
@overload
def __mul__( self , A ) -> Vector:
'''simple docstring'''
...
@overload
def __mul__( self , A ) -> float:
'''simple docstring'''
...
def __mul__( self , A ) -> float | Vector:
'''simple docstring'''
if isinstance(A , (float, int) ):
a = [c * other for c in self.__components]
return Vector(A )
elif isinstance(A , A ) and len(self ) == len(A ):
a = len(self )
a = [self.__components[i] * other.component(A ) for i in range(A )]
return sum(A )
else: # error case
raise Exception("invalid operand!" )
def lowerCAmelCase_ ( self ) -> Vector:
'''simple docstring'''
return Vector(self.__components )
def lowerCAmelCase_ ( self , A ) -> float:
'''simple docstring'''
if isinstance(A , A ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception("index out of range" )
def lowerCAmelCase_ ( self , A , A ) -> None:
'''simple docstring'''
assert -len(self.__components ) <= pos < len(self.__components )
a = value
def lowerCAmelCase_ ( self ) -> float:
'''simple docstring'''
if len(self.__components ) == 0:
raise Exception("Vector is empty" )
a = [c**2 for c in self.__components]
return math.sqrt(sum(A ) )
def lowerCAmelCase_ ( self , A , A = False ) -> float:
'''simple docstring'''
a = self * other
a = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> Vector:
assert isinstance(__UpperCamelCase , __UpperCamelCase)
return Vector([0] * dimension)
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase) -> Vector:
assert isinstance(__UpperCamelCase , __UpperCamelCase) and (isinstance(__UpperCamelCase , __UpperCamelCase))
a = [0] * dimension
a = 1
return Vector(__UpperCamelCase)
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase) -> Vector:
assert (
isinstance(__UpperCamelCase , __UpperCamelCase)
and isinstance(__UpperCamelCase , __UpperCamelCase)
and (isinstance(__UpperCamelCase , (int, float)))
)
return x * scalar + y
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase) -> Vector:
random.seed(__UpperCamelCase)
a = [random.randint(__UpperCamelCase , __UpperCamelCase) for _ in range(__UpperCamelCase)]
return Vector(__UpperCamelCase)
class a__ :
def __init__( self , A , A , A ) -> None:
'''simple docstring'''
a = matrix
a = w
a = h
def __str__( self ) -> str:
'''simple docstring'''
a = ""
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self , A ) -> Matrix:
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
a = []
for i in range(self.__height ):
a = [
self.__matrix[i][j] + other.component(A , A )
for j in range(self.__width )
]
matrix.append(A )
return Matrix(A , self.__width , self.__height )
else:
raise Exception("matrix must have the same dimension!" )
def __sub__( self , A ) -> Matrix:
'''simple docstring'''
if self.__width == other.width() and self.__height == other.height():
a = []
for i in range(self.__height ):
a = [
self.__matrix[i][j] - other.component(A , A )
for j in range(self.__width )
]
matrix.append(A )
return Matrix(A , self.__width , self.__height )
else:
raise Exception("matrices must have the same dimension!" )
@overload
def __mul__( self , A ) -> Matrix:
'''simple docstring'''
...
@overload
def __mul__( self , A ) -> Vector:
'''simple docstring'''
...
def __mul__( self , A ) -> Vector | Matrix:
'''simple docstring'''
if isinstance(A , A ): # matrix-vector
if len(A ) == self.__width:
a = zero_vector(self.__height )
for i in range(self.__height ):
a = [
self.__matrix[i][j] * other.component(A )
for j in range(self.__width )
]
ans.change_component(A , sum(A ) )
return ans
else:
raise Exception(
"vector must have the same size as the "
"number of columns of the matrix!" )
elif isinstance(A , (int, float) ): # matrix-scalar
a = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(A , self.__width , self.__height )
return None
def lowerCAmelCase_ ( self ) -> int:
'''simple docstring'''
return self.__height
def lowerCAmelCase_ ( self ) -> int:
'''simple docstring'''
return self.__width
def lowerCAmelCase_ ( self , A , A ) -> float:
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception("change_component: indices out of bounds" )
def lowerCAmelCase_ ( self , A , A , A ) -> None:
'''simple docstring'''
if 0 <= x < self.__height and 0 <= y < self.__width:
a = value
else:
raise Exception("change_component: indices out of bounds" )
def lowerCAmelCase_ ( self , A , A ) -> float:
'''simple docstring'''
if self.__height != self.__width:
raise Exception("Matrix is not square" )
a = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(A ) ):
a = minor[i][:y] + minor[i][y + 1 :]
return Matrix(A , self.__width - 1 , self.__height - 1 ).determinant()
def lowerCAmelCase_ ( self , A , A ) -> float:
'''simple docstring'''
if self.__height != self.__width:
raise Exception("Matrix is not square" )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(A , A )
else:
raise Exception("Indices out of bounds" )
def lowerCAmelCase_ ( self ) -> float:
'''simple docstring'''
if self.__height != self.__width:
raise Exception("Matrix is not square" )
if self.__height < 1:
raise Exception("Matrix has no element" )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
a = [
self.__matrix[0][y] * self.cofactor(0 , A ) for y in range(self.__width )
]
return sum(A )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> Matrix:
a = [[0] * n for _ in range(__UpperCamelCase)]
return Matrix(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase)
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase) -> Matrix:
random.seed(__UpperCamelCase)
a = [
[random.randint(__UpperCamelCase , __UpperCamelCase) for _ in range(__UpperCamelCase)] for _ in range(__UpperCamelCase)
]
return Matrix(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase)
| 515 | 0 |
import numpy as np
def _A ( lowerCAmelCase_ : np.array ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 125 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = ["input_features", "attention_mask"]
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : Any=80 , SCREAMING_SNAKE_CASE__ : List[str]=16_000 , SCREAMING_SNAKE_CASE__ : Tuple=80 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.0 , SCREAMING_SNAKE_CASE__ : Optional[Any]=True , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : List[str]=True , **SCREAMING_SNAKE_CASE__ : int , ) -> List[str]:
super().__init__(feature_size=SCREAMING_SNAKE_CASE__ , sampling_rate=SCREAMING_SNAKE_CASE__ , padding_value=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = num_mel_bins
lowerCAmelCase__ = do_ceptral_normalize
lowerCAmelCase__ = normalize_means
lowerCAmelCase__ = normalize_vars
lowerCAmelCase__ = True
def a ( self : List[str] , SCREAMING_SNAKE_CASE__ : np.ndarray , ) -> np.ndarray:
lowerCAmelCase__ = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
lowerCAmelCase__ = torch.from_numpy(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 )
lowerCAmelCase__ = ta_kaldi.fbank(SCREAMING_SNAKE_CASE__ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def a ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[bool] = True , SCREAMING_SNAKE_CASE__ : Optional[bool] = True , SCREAMING_SNAKE_CASE__ : float = 0.0 , ) -> np.ndarray:
# make sure we normalize float32 arrays
if normalize_means:
lowerCAmelCase__ = x[:input_length].mean(axis=0 )
lowerCAmelCase__ = np.subtract(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if normalize_vars:
lowerCAmelCase__ = x[:input_length].std(axis=0 )
lowerCAmelCase__ = np.divide(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if input_length < x.shape[0]:
lowerCAmelCase__ = padding_value
# make sure array is in float32
lowerCAmelCase__ = x.astype(np.floataa )
return x
def a ( self : Tuple , SCREAMING_SNAKE_CASE__ : List[np.ndarray] , SCREAMING_SNAKE_CASE__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
lowerCAmelCase__ = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
]
def __call__( self : Tuple , SCREAMING_SNAKE_CASE__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , SCREAMING_SNAKE_CASE__ : Union[bool, str, PaddingStrategy] = False , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , **SCREAMING_SNAKE_CASE__ : List[Any] , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
f' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
lowerCAmelCase__ = isinstance(SCREAMING_SNAKE_CASE__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
lowerCAmelCase__ = is_batched_numpy or (
isinstance(SCREAMING_SNAKE_CASE__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase__ = [np.asarray(SCREAMING_SNAKE_CASE__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE__ , np.ndarray ):
lowerCAmelCase__ = np.asarray(SCREAMING_SNAKE_CASE__ , dtype=np.floataa )
elif isinstance(SCREAMING_SNAKE_CASE__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase__ = [raw_speech]
# extract fbank features
lowerCAmelCase__ = [self._extract_fbank_features(SCREAMING_SNAKE_CASE__ ) for waveform in raw_speech]
# convert into correct format for padding
lowerCAmelCase__ = BatchFeature({"input_features": features} )
lowerCAmelCase__ = self.pad(
SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , pad_to_multiple_of=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
# make sure list is in array format
lowerCAmelCase__ = padded_inputs.get("input_features" )
if isinstance(input_features[0] , SCREAMING_SNAKE_CASE__ ):
lowerCAmelCase__ = [np.asarray(SCREAMING_SNAKE_CASE__ , dtype=np.floataa ) for feature in input_features]
lowerCAmelCase__ = padded_inputs.get("attention_mask" )
if attention_mask is not None:
lowerCAmelCase__ = [np.asarray(SCREAMING_SNAKE_CASE__ , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
lowerCAmelCase__ = (
np.array(SCREAMING_SNAKE_CASE__ , dtype=np.intaa )
if self._get_padding_strategies(SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
lowerCAmelCase__ = self.normalize(
padded_inputs["input_features"] , attention_mask=SCREAMING_SNAKE_CASE__ )
if return_tensors is not None:
lowerCAmelCase__ = padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE__ )
return padded_inputs
| 125 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class UpperCAmelCase :
def __init__( self , __A , ):
__UpperCAmelCase = parent
__UpperCAmelCase = 13
__UpperCAmelCase = 7
__UpperCAmelCase = 30
__UpperCAmelCase = self.seq_length + self.mem_len
__UpperCAmelCase = 15
__UpperCAmelCase = True
__UpperCAmelCase = True
__UpperCAmelCase = 99
__UpperCAmelCase = [10, 50, 80]
__UpperCAmelCase = 32
__UpperCAmelCase = 32
__UpperCAmelCase = 4
__UpperCAmelCase = 8
__UpperCAmelCase = 128
__UpperCAmelCase = 2
__UpperCAmelCase = 2
__UpperCAmelCase = None
__UpperCAmelCase = 1
__UpperCAmelCase = 0
__UpperCAmelCase = 3
__UpperCAmelCase = self.vocab_size - 1
__UpperCAmelCase = 0.0_1
def __lowerCamelCase ( self ):
__UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase = None
if self.use_labels:
__UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def __lowerCamelCase ( self ):
random.seed(self.seed )
tf.random.set_seed(self.seed )
def __lowerCamelCase ( self , __A , __A , __A , __A ):
__UpperCAmelCase = TFTransfoXLModel(__A )
__UpperCAmelCase , __UpperCAmelCase = model(__A ).to_tuple()
__UpperCAmelCase = {'input_ids': input_ids_a, 'mems': mems_a}
__UpperCAmelCase , __UpperCAmelCase = model(__A ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __lowerCamelCase ( self , __A , __A , __A , __A ):
__UpperCAmelCase = TFTransfoXLLMHeadModel(__A )
__UpperCAmelCase , __UpperCAmelCase = model(__A ).to_tuple()
__UpperCAmelCase = {'input_ids': input_ids_a, 'labels': lm_labels}
__UpperCAmelCase , __UpperCAmelCase = model(__A ).to_tuple()
__UpperCAmelCase , __UpperCAmelCase = model([input_ids_a, mems_a] ).to_tuple()
__UpperCAmelCase = {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}
__UpperCAmelCase , __UpperCAmelCase = model(__A ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __lowerCamelCase ( self , __A , __A , __A , __A ):
__UpperCAmelCase = TFTransfoXLForSequenceClassification(__A )
__UpperCAmelCase = model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.prepare_config_and_inputs()
((__UpperCAmelCase) , (__UpperCAmelCase) , (__UpperCAmelCase) , (__UpperCAmelCase)) = config_and_inputs
__UpperCAmelCase = {'input_ids': input_ids_a}
return config, inputs_dict
@require_tf
class UpperCAmelCase ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
_A : Union[str, Any] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
_A : Union[str, Any] = () if is_tf_available() else ()
_A : Union[str, Any] = (
{
"""feature-extraction""": TFTransfoXLModel,
"""text-classification""": TFTransfoXLForSequenceClassification,
"""text-generation""": TFTransfoXLLMHeadModel,
"""zero-shot""": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
_A : str = False
_A : Any = False
_A : str = False
_A : str = False
def __lowerCamelCase ( self , __A , __A , __A , __A , __A ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def __lowerCamelCase ( self ):
__UpperCAmelCase = TFTransfoXLModelTester(self )
__UpperCAmelCase = ConfigTester(self , config_class=__A , d_embed=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
self.model_tester.set_seed()
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*__A )
def __lowerCamelCase ( self ):
self.model_tester.set_seed()
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*__A )
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__A )
def __lowerCamelCase ( self ):
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(__A )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
__UpperCAmelCase = model.get_output_embeddings()
assert isinstance(__A , tf.keras.layers.Layer )
__UpperCAmelCase = model.get_bias()
assert name is None
else:
__UpperCAmelCase = model.get_output_embeddings()
assert x is None
__UpperCAmelCase = model.get_bias()
assert name is None
def __lowerCamelCase ( self ):
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def __lowerCamelCase ( self ):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase = TFTransfoXLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.' )
def __lowerCamelCase ( self ):
pass
@require_tf
class UpperCAmelCase ( unittest.TestCase ):
@unittest.skip('Skip test until #12651 is resolved.' )
@slow
def __lowerCamelCase ( self ):
__UpperCAmelCase = TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103' )
# fmt: off
__UpperCAmelCase = tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
__UpperCAmelCase = [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
__UpperCAmelCase = model.generate(__A , max_length=200 , do_sample=__A )
self.assertListEqual(output_ids[0].numpy().tolist() , __A )
| 126 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN'] )
__UpperCAmelCase = g.get_repo('huggingface/accelerate' )
__UpperCAmelCase = repo.get_issues(state='open' )
for issue in open_issues:
__UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda _lowerCAmelCase : i.created_at , reverse=_lowerCAmelCase )
__UpperCAmelCase = comments[0] if len(_lowerCAmelCase ) > 0 else None
__UpperCAmelCase = dt.utcnow()
__UpperCAmelCase = (current_time - issue.updated_at).days
__UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 126 | 1 |
from math import sqrt
def __UpperCamelCase ( lowerCAmelCase__ : int ):
__a : int = 0
for i in range(1 , int(sqrt(lowerCAmelCase__ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCAmelCase__ ):
total += i + n // i
elif i == sqrt(lowerCAmelCase__ ):
total += i
return total - n
def __UpperCamelCase ( lowerCAmelCase__ : int = 1_0_0_0_0 ):
__a : Union[str, Any] = sum(
i
for i in range(1 , lowerCAmelCase__ )
if sum_of_divisors(sum_of_divisors(lowerCAmelCase__ ) ) == i and sum_of_divisors(lowerCAmelCase__ ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 326 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __UpperCamelCase ( lowerCAmelCase__ : Any ):
__a : Dict = filter(lambda lowerCAmelCase__ : p.requires_grad , model.parameters() )
__a : Tuple = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowercase__ =logging.getLogger(__name__)
def __UpperCamelCase ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[Any] ):
if metric == "rouge2":
__a : List[Any] = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
__a : List[str] = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
__a : Optional[Any] = '''{val_avg_em:.4f}-{step_count}'''
else:
raise NotImplementedError(
f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
''' function.''' )
__a : List[Any] = ModelCheckpoint(
dirpath=lowerCAmelCase__ , filename=lowerCAmelCase__ , monitor=f"val_{metric}" , mode='''max''' , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def __UpperCamelCase ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] ):
return EarlyStopping(
monitor=f"val_{metric}" , mode='''min''' if '''loss''' in metric else '''max''' , patience=lowerCAmelCase__ , verbose=lowerCAmelCase__ , )
class UpperCamelCase__ ( pl.Callback ):
def lowerCAmelCase (self : List[str] , snake_case_ : Any , snake_case_ : Any ):
__a : Optional[int] = {f"lr_group_{i}": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(snake_case_ )
@rank_zero_only
def lowerCAmelCase (self : str , snake_case_ : pl.Trainer , snake_case_ : pl.LightningModule , snake_case_ : str , snake_case_ : Dict=True ):
logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****" )
__a : List[str] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
__a : Union[str, Any] = Path(pl_module.hparams.output_dir )
if type_path == "test":
__a : Union[str, Any] = od / '''test_results.txt'''
__a : Optional[Any] = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__a : Optional[int] = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
__a : List[str] = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=snake_case_ )
generations_file.parent.mkdir(exist_ok=snake_case_ )
with open(snake_case_ , '''a+''' ) as writer:
for key in sorted(snake_case_ ):
if key in ["log", "progress_bar", "preds"]:
continue
__a : Tuple = metrics[key]
if isinstance(snake_case_ , torch.Tensor ):
__a : Optional[int] = val.item()
__a : List[str] = f"{key}: {val:.6f}\n"
writer.write(snake_case_ )
if not save_generations:
return
if "preds" in metrics:
__a : Optional[Any] = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(snake_case_ )
@rank_zero_only
def lowerCAmelCase (self : List[Any] , snake_case_ : Union[str, Any] , snake_case_ : Tuple ):
try:
__a : Union[str, Any] = pl_module.model.model.num_parameters()
except AttributeError:
__a : int = pl_module.model.num_parameters()
__a : Any = count_trainable_parameters(snake_case_ )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6} )
@rank_zero_only
def lowerCAmelCase (self : Optional[int] , snake_case_ : pl.Trainer , snake_case_ : pl.LightningModule ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(snake_case_ , snake_case_ , '''test''' )
@rank_zero_only
def lowerCAmelCase (self : Union[str, Any] , snake_case_ : pl.Trainer , snake_case_ : str ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 326 | 1 |
def UpperCamelCase_( _A :list )-> list:
for i in range(len(_A ) - 1 , 0 , -1 ):
UpperCamelCase__ = False
for j in range(_A , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
UpperCamelCase__, UpperCamelCase__ = unsorted[j - 1], unsorted[j]
UpperCamelCase__ = True
for j in range(_A ):
if unsorted[j] > unsorted[j + 1]:
UpperCamelCase__, UpperCamelCase__ = unsorted[j + 1], unsorted[j]
UpperCamelCase__ = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase = input('Enter numbers separated by a comma:\n').strip()
__UpperCamelCase = [int(item) for item in user_input.split(',')]
print(f'''{cocktail_shaker_sort(unsorted) = }''')
| 551 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class lowerCamelCase__ ( UpperCAmelCase ):
"""simple docstring"""
_UpperCamelCase : Tuple = CustomTokenizer
pass
| 551 | 1 |
import math
def __lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
return math.sqrt(_UpperCamelCase ) * math.sqrt(_UpperCamelCase ) == num
def __lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = n
while left <= right:
SCREAMING_SNAKE_CASE = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
SCREAMING_SNAKE_CASE = mid - 1
else:
SCREAMING_SNAKE_CASE = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 714 |
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
__UpperCamelCase ="facebook/bart-large-mnli"
__UpperCamelCase =(
"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
"should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
"It returns the most likely label in the list of provided `labels` for the input text."
)
__UpperCamelCase ="text_classifier"
__UpperCamelCase =AutoTokenizer
__UpperCamelCase =AutoModelForSequenceClassification
__UpperCamelCase =["text", ["text"]]
__UpperCamelCase =["text"]
def UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
super().setup()
SCREAMING_SNAKE_CASE = self.model.config
SCREAMING_SNAKE_CASE = -1
for idx, label in config.idalabel.items():
if label.lower().startswith('entail' ):
SCREAMING_SNAKE_CASE = int(snake_case__ )
if self.entailment_id == -1:
raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.' )
def UpperCamelCase ( self : Optional[Any] , snake_case__ : List[str] , snake_case__ : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = labels
return self.pre_processor(
[text] * len(snake_case__ ) , [F"""This example is {label}""" for label in labels] , return_tensors='pt' , padding='max_length' , )
def UpperCamelCase ( self : Dict , snake_case__ : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = outputs.logits
SCREAMING_SNAKE_CASE = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 673 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCamelCase__ =16
UpperCamelCase__ =32
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase = 16 ):
_SCREAMING_SNAKE_CASE : int = AutoTokenizer.from_pretrained("bert-base-cased" )
_SCREAMING_SNAKE_CASE : str = load_dataset("glue", "mrpc" )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
_SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(examples["sentence1"], examples["sentence2"], truncation=__lowerCamelCase, max_length=__lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_SCREAMING_SNAKE_CASE : Tuple = datasets.map(
__lowerCamelCase, batched=__lowerCamelCase, remove_columns=["idx", "sentence1", "sentence2"], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_SCREAMING_SNAKE_CASE : List[str] = tokenized_datasets.rename_column("label", "labels" )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_SCREAMING_SNAKE_CASE : List[str] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_SCREAMING_SNAKE_CASE : Dict = 16
elif accelerator.mixed_precision != "no":
_SCREAMING_SNAKE_CASE : str = 8
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = None
return tokenizer.pad(
__lowerCamelCase, padding="longest", max_length=__lowerCamelCase, pad_to_multiple_of=__lowerCamelCase, return_tensors="pt", )
# Instantiate dataloaders.
_SCREAMING_SNAKE_CASE : Tuple = DataLoader(
tokenized_datasets["train"], shuffle=__lowerCamelCase, collate_fn=__lowerCamelCase, batch_size=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = DataLoader(
tokenized_datasets["validation"], shuffle=__lowerCamelCase, collate_fn=__lowerCamelCase, batch_size=__lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCamelCase__ =mocked_dataloaders # noqa: F811
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", __lowerCamelCase ) == "1":
_SCREAMING_SNAKE_CASE : Optional[Any] = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
_SCREAMING_SNAKE_CASE : int = Accelerator(
cpu=args.cpu, mixed_precision=args.mixed_precision, log_with="all", project_dir=args.project_dir )
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_SCREAMING_SNAKE_CASE : Optional[Any] = config["lr"]
_SCREAMING_SNAKE_CASE : str = int(config["num_epochs"] )
_SCREAMING_SNAKE_CASE : int = int(config["seed"] )
_SCREAMING_SNAKE_CASE : Optional[Any] = int(config["batch_size"] )
set_seed(__lowerCamelCase )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : str = get_dataloaders(__lowerCamelCase, __lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = evaluate.load("glue", "mrpc" )
# If the batch size is too big we use gradient accumulation
_SCREAMING_SNAKE_CASE : Optional[int] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_SCREAMING_SNAKE_CASE : Optional[int] = batch_size // MAX_GPU_BATCH_SIZE
_SCREAMING_SNAKE_CASE : Dict = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=__lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_SCREAMING_SNAKE_CASE : Tuple = model.to(accelerator.device )
# Instantiate optimizer
_SCREAMING_SNAKE_CASE : List[str] = AdamW(params=model.parameters(), lr=__lowerCamelCase )
# Instantiate scheduler
_SCREAMING_SNAKE_CASE : List[str] = get_linear_schedule_with_warmup(
optimizer=__lowerCamelCase, num_warmup_steps=100, num_training_steps=(len(__lowerCamelCase ) * num_epochs) // gradient_accumulation_steps, )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[int] = accelerator.prepare(
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
_SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.split(__lowerCamelCase )[-1].split("." )[0]
accelerator.init_trackers(__lowerCamelCase, __lowerCamelCase )
# Now we train the model
for epoch in range(__lowerCamelCase ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
_SCREAMING_SNAKE_CASE : List[Any] = 0
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_SCREAMING_SNAKE_CASE : Tuple = model(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
_SCREAMING_SNAKE_CASE : Any = loss / gradient_accumulation_steps
accelerator.backward(__lowerCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Optional[Any] = model(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.logits.argmax(dim=-1 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__lowerCamelCase, references=__lowerCamelCase, )
_SCREAMING_SNAKE_CASE : List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""", __lowerCamelCase )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(__lowerCamelCase ),
"epoch": epoch,
}, step=__lowerCamelCase, )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision", type=__lowerCamelCase, default=__lowerCamelCase, choices=["no", "fp16", "bf16", "fp8"], help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU.", )
parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU." )
parser.add_argument(
"--with_tracking", action="store_true", help="Whether to load in all available experiment trackers from the environment and use them for logging.", )
parser.add_argument(
"--project_dir", type=__lowerCamelCase, default="logs", help="Location on where to store experiment tracking logs` and relevent project information", )
_SCREAMING_SNAKE_CASE : Optional[int] = parser.parse_args()
_SCREAMING_SNAKE_CASE : List[str] = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__lowerCamelCase, __lowerCamelCase )
if __name__ == "__main__":
main() | 249 | """simple docstring"""
import sys
__lowercase : Union[str, Any] = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def SCREAMING_SNAKE_CASE ( snake_case):
__snake_case = 1
for digit in s:
product *= int(snake_case)
return product
def SCREAMING_SNAKE_CASE ( snake_case = N):
__snake_case = -sys.maxsize - 1
__snake_case = n[:13]
__snake_case = 13
while cur_index < len(snake_case) - 13:
if int(n[cur_index]) >= int(substr[0]):
__snake_case = substr[1:] + n[cur_index]
cur_index += 1
else:
__snake_case = max(snake_case, str_eval(snake_case))
__snake_case = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""") | 564 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Any = {'''vocab_file''': '''vocab.txt'''}
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
_SCREAMING_SNAKE_CASE : int = {
'''YituTech/conv-bert-base''': 512,
'''YituTech/conv-bert-medium-small''': 512,
'''YituTech/conv-bert-small''': 512,
}
_SCREAMING_SNAKE_CASE : Dict = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class a ( __lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE : Tuple = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE : Optional[int] = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE : List[str] = ConvBertTokenizer
def __init__( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : int="[UNK]" , __SCREAMING_SNAKE_CASE : Optional[int]="[SEP]" , __SCREAMING_SNAKE_CASE : Tuple="[PAD]" , __SCREAMING_SNAKE_CASE : Optional[Any]="[CLS]" , __SCREAMING_SNAKE_CASE : Dict="[MASK]" , __SCREAMING_SNAKE_CASE : List[str]=True , __SCREAMING_SNAKE_CASE : List[str]=None , **__SCREAMING_SNAKE_CASE : Tuple , ) -> Union[str, Any]:
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __A ) != do_lower_case
or normalizer_state.get('strip_accents' , __A ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __A ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(__A , normalizer_state.pop('type' ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**__A )
lowerCamelCase_ = do_lower_case
def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Dict=None ) -> int:
lowerCamelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Dict = None ) -> List[int]:
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int] = None ) -> Tuple[str]:
lowerCamelCase_ = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 714 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a ( __snake_case ):
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Distribution , __SCREAMING_SNAKE_CASE : List[str]=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Tuple=0 ) -> Optional[Any]:
lowerCamelCase_ = 1.0 if scale is None else scale
lowerCamelCase_ = 0.0 if loc is None else loc
super().__init__(__SCREAMING_SNAKE_CASE , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=__SCREAMING_SNAKE_CASE )] )
@property
def UpperCamelCase ( self : Optional[Any] ) -> Tuple:
return self.base_dist.mean * self.scale + self.loc
@property
def UpperCamelCase ( self : int ) -> Any:
return self.base_dist.variance * self.scale**2
@property
def UpperCamelCase ( self : List[Any] ) -> str:
return self.variance.sqrt()
class a ( nn.Module ):
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Dict[str, int] , __SCREAMING_SNAKE_CASE : Callable[..., Tuple[torch.Tensor]] , **__SCREAMING_SNAKE_CASE : str ) -> None:
super().__init__(**__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = args_dim
lowerCamelCase_ = nn.ModuleList([nn.Linear(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for dim in args_dim.values()] )
lowerCamelCase_ = domain_map
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : torch.Tensor ) -> Tuple[torch.Tensor]:
lowerCamelCase_ = [proj(__SCREAMING_SNAKE_CASE ) for proj in self.proj]
return self.domain_map(*__SCREAMING_SNAKE_CASE )
class a ( nn.Module ):
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
super().__init__()
lowerCamelCase_ = function
def UpperCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : int , *__SCREAMING_SNAKE_CASE : List[Any] ) -> List[Any]:
return self.function(__SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE )
class a :
SCREAMING_SNAKE_CASE : type
SCREAMING_SNAKE_CASE : int
SCREAMING_SNAKE_CASE : Dict[str, int]
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int = 1 ) -> None:
lowerCamelCase_ = dim
lowerCamelCase_ = {k: dim * self.args_dim[k] for k in self.args_dim}
def UpperCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] ) -> List[Any]:
if self.dim == 1:
return self.distribution_class(*__SCREAMING_SNAKE_CASE )
else:
return Independent(self.distribution_class(*__SCREAMING_SNAKE_CASE ) , 1 )
def UpperCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[torch.Tensor] = None , __SCREAMING_SNAKE_CASE : Optional[torch.Tensor] = None , ) -> Distribution:
lowerCamelCase_ = self._base_distribution(__SCREAMING_SNAKE_CASE )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(__SCREAMING_SNAKE_CASE , loc=__SCREAMING_SNAKE_CASE , scale=__SCREAMING_SNAKE_CASE , event_dim=self.event_dim )
@property
def UpperCamelCase ( self : int ) -> Tuple:
return () if self.dim == 1 else (self.dim,)
@property
def UpperCamelCase ( self : Optional[Any] ) -> int:
return len(self.event_shape )
@property
def UpperCamelCase ( self : List[Any] ) -> float:
return 0.0
def UpperCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> nn.Module:
return ParameterProjection(
in_features=__SCREAMING_SNAKE_CASE , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def UpperCamelCase ( self : List[str] , *__SCREAMING_SNAKE_CASE : torch.Tensor ) -> List[str]:
raise NotImplementedError()
@staticmethod
def UpperCamelCase ( __SCREAMING_SNAKE_CASE : torch.Tensor ) -> torch.Tensor:
return (x + torch.sqrt(torch.square(__SCREAMING_SNAKE_CASE ) + 4.0 )) / 2.0
class a ( __snake_case ):
SCREAMING_SNAKE_CASE : Dict[str, int] = {"df": 1, "loc": 1, "scale": 1}
SCREAMING_SNAKE_CASE : type = StudentT
@classmethod
def UpperCamelCase ( cls : str , __SCREAMING_SNAKE_CASE : torch.Tensor , __SCREAMING_SNAKE_CASE : torch.Tensor , __SCREAMING_SNAKE_CASE : torch.Tensor ) -> Optional[Any]:
lowerCamelCase_ = cls.squareplus(__SCREAMING_SNAKE_CASE ).clamp_min(torch.finfo(scale.dtype ).eps )
lowerCamelCase_ = 2.0 + cls.squareplus(__SCREAMING_SNAKE_CASE )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class a ( __snake_case ):
SCREAMING_SNAKE_CASE : Dict[str, int] = {"loc": 1, "scale": 1}
SCREAMING_SNAKE_CASE : type = Normal
@classmethod
def UpperCamelCase ( cls : Dict , __SCREAMING_SNAKE_CASE : torch.Tensor , __SCREAMING_SNAKE_CASE : torch.Tensor ) -> Tuple:
lowerCamelCase_ = cls.squareplus(__SCREAMING_SNAKE_CASE ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class a ( __snake_case ):
SCREAMING_SNAKE_CASE : Dict[str, int] = {"total_count": 1, "logits": 1}
SCREAMING_SNAKE_CASE : type = NegativeBinomial
@classmethod
def UpperCamelCase ( cls : Dict , __SCREAMING_SNAKE_CASE : torch.Tensor , __SCREAMING_SNAKE_CASE : torch.Tensor ) -> Optional[Any]:
lowerCamelCase_ = cls.squareplus(__SCREAMING_SNAKE_CASE )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def UpperCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Distribution:
lowerCamelCase_ , lowerCamelCase_ = distr_args
if self.dim == 1:
return self.distribution_class(total_count=__SCREAMING_SNAKE_CASE , logits=__SCREAMING_SNAKE_CASE )
else:
return Independent(self.distribution_class(total_count=__SCREAMING_SNAKE_CASE , logits=__SCREAMING_SNAKE_CASE ) , 1 )
def UpperCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[torch.Tensor] = None , __SCREAMING_SNAKE_CASE : Optional[torch.Tensor] = None ) -> Distribution:
lowerCamelCase_ , lowerCamelCase_ = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 137 | 0 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : Any = 0
for ch in input_str:
A_ : Union[str, Any] = ord(lowerCamelCase_ )
A_ : Tuple = pow(2 , lowerCamelCase_ )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 590 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : int = TypeVar('''DatasetType''', Dataset, IterableDataset)
def __UpperCAmelCase ( lowerCamelCase_ : List[DatasetType] , lowerCamelCase_ : Optional[List[float]] = None , lowerCamelCase_ : Optional[int] = None , lowerCamelCase_ : Optional[DatasetInfo] = None , lowerCamelCase_ : Optional[NamedSplit] = None , lowerCamelCase_ : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(lowerCamelCase_ ):
if not isinstance(lowerCamelCase_ , (Dataset, IterableDataset) ):
if isinstance(lowerCamelCase_ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
F'Dataset at position {i} has at least one split: {list(lowerCamelCase_ )}\n'
F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase_ ) )}\']' )
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase_ ).__name__}.' )
if i == 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = (
(Dataset, IterableDataset) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(
F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , info=lowerCamelCase_ , split=lowerCamelCase_ , stopping_strategy=lowerCamelCase_ )
else:
return _interleave_iterable_datasets(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , info=lowerCamelCase_ , split=lowerCamelCase_ , stopping_strategy=lowerCamelCase_ )
def __UpperCAmelCase ( lowerCamelCase_ : List[DatasetType] , lowerCamelCase_ : Optional[DatasetInfo] = None , lowerCamelCase_ : Optional[NamedSplit] = None , lowerCamelCase_ : int = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(lowerCamelCase_ ):
if not isinstance(lowerCamelCase_ , (Dataset, IterableDataset) ):
if isinstance(lowerCamelCase_ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
F'Dataset at position {i} has at least one split: {list(lowerCamelCase_ )}\n'
F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase_ ) )}\']' )
raise ValueError(
F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase_ ).__name__}.' )
if i == 0:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = (
(Dataset, IterableDataset) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(
F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(lowerCamelCase_ , info=lowerCamelCase_ , split=lowerCamelCase_ , axis=lowerCamelCase_ )
else:
return _concatenate_iterable_datasets(lowerCamelCase_ , info=lowerCamelCase_ , split=lowerCamelCase_ , axis=lowerCamelCase_ )
| 105 | 0 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def lowercase__ ( lowercase_ ) -> List[str]:
"""simple docstring"""
_UpperCamelCase : int = model.config
_UpperCamelCase : Optional[int] = DonutSwinConfig(
image_size=original_config.input_size ,patch_size=4 ,depths=original_config.encoder_layer ,num_heads=[4, 8, 16, 32] ,window_size=original_config.window_size ,embed_dim=128 ,)
_UpperCamelCase : Any = MBartConfig(
is_decoder=lowercase_ ,is_encoder_decoder=lowercase_ ,add_cross_attention=lowercase_ ,decoder_layers=original_config.decoder_layer ,max_position_embeddings=original_config.max_position_embeddings ,vocab_size=len(
model.decoder.tokenizer ) ,scale_embedding=lowercase_ ,add_final_layer_norm=lowercase_ ,)
return encoder_config, decoder_config
def lowercase__ ( lowercase_ ) -> Optional[int]:
"""simple docstring"""
if "encoder.model" in name:
_UpperCamelCase : str = name.replace("encoder.model" ,"encoder" )
if "decoder.model" in name:
_UpperCamelCase : Dict = name.replace("decoder.model" ,"decoder" )
if "patch_embed.proj" in name:
_UpperCamelCase : List[Any] = name.replace("patch_embed.proj" ,"embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
_UpperCamelCase : str = name.replace("patch_embed.norm" ,"embeddings.norm" )
if name.startswith("encoder" ):
if "layers" in name:
_UpperCamelCase : Union[str, Any] = "encoder." + name
if "attn.proj" in name:
_UpperCamelCase : Any = name.replace("attn.proj" ,"attention.output.dense" )
if "attn" in name and "mask" not in name:
_UpperCamelCase : int = name.replace("attn" ,"attention.self" )
if "norm1" in name:
_UpperCamelCase : int = name.replace("norm1" ,"layernorm_before" )
if "norm2" in name:
_UpperCamelCase : int = name.replace("norm2" ,"layernorm_after" )
if "mlp.fc1" in name:
_UpperCamelCase : List[str] = name.replace("mlp.fc1" ,"intermediate.dense" )
if "mlp.fc2" in name:
_UpperCamelCase : Optional[Any] = name.replace("mlp.fc2" ,"output.dense" )
if name == "encoder.norm.weight":
_UpperCamelCase : Union[str, Any] = "encoder.layernorm.weight"
if name == "encoder.norm.bias":
_UpperCamelCase : Tuple = "encoder.layernorm.bias"
return name
def lowercase__ ( lowercase_ ,lowercase_ ) -> str:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_UpperCamelCase : Optional[Any] = orig_state_dict.pop(lowercase_ )
if "qkv" in key:
_UpperCamelCase : Tuple = key.split("." )
_UpperCamelCase : Dict = int(key_split[3] )
_UpperCamelCase : Optional[Any] = int(key_split[5] )
_UpperCamelCase : int = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_UpperCamelCase : int = val[:dim, :]
_UpperCamelCase : Tuple = val[dim : dim * 2, :]
_UpperCamelCase : Optional[Any] = val[-dim:, :]
else:
_UpperCamelCase : Union[str, Any] = val[:dim]
_UpperCamelCase : Tuple = val[dim : dim * 2]
_UpperCamelCase : Any = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
_UpperCamelCase : Dict = val
return orig_state_dict
def lowercase__ ( lowercase_ ,lowercase_=None ,lowercase_=False ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase : List[str] = DonutModel.from_pretrained(lowercase_ ).eval()
# load HuggingFace model
_UpperCamelCase, _UpperCamelCase : Dict = get_configs(lowercase_ )
_UpperCamelCase : Dict = DonutSwinModel(lowercase_ )
_UpperCamelCase : int = MBartForCausalLM(lowercase_ )
_UpperCamelCase : str = VisionEncoderDecoderModel(encoder=lowercase_ ,decoder=lowercase_ )
model.eval()
_UpperCamelCase : str = original_model.state_dict()
_UpperCamelCase : Union[str, Any] = convert_state_dict(lowercase_ ,lowercase_ )
model.load_state_dict(lowercase_ )
# verify results on scanned document
_UpperCamelCase : Any = load_dataset("hf-internal-testing/example-documents" )
_UpperCamelCase : Any = dataset["test"][0]["image"].convert("RGB" )
_UpperCamelCase : Dict = XLMRobertaTokenizerFast.from_pretrained(lowercase_ ,from_slow=lowercase_ )
_UpperCamelCase : Any = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis ,size=original_model.config.input_size[::-1] )
_UpperCamelCase : int = DonutProcessor(lowercase_ ,lowercase_ )
_UpperCamelCase : List[str] = processor(lowercase_ ,return_tensors="pt" ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
_UpperCamelCase : Optional[Any] = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
_UpperCamelCase : str = "When is the coffee break?"
_UpperCamelCase : str = task_prompt.replace("{user_input}" ,lowercase_ )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
_UpperCamelCase : int = "<s_rvlcdip>"
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
_UpperCamelCase : Any = "<s_cord>"
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
_UpperCamelCase : Tuple = "s_cord-v2>"
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
_UpperCamelCase : Tuple = "<s_zhtrainticket>"
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
_UpperCamelCase : List[Any] = "hello world"
else:
raise ValueError("Model name not supported" )
_UpperCamelCase : str = original_model.decoder.tokenizer(lowercase_ ,add_special_tokens=lowercase_ ,return_tensors="pt" )[
"input_ids"
]
_UpperCamelCase : List[Any] = original_model.encoder.model.patch_embed(lowercase_ )
_UpperCamelCase, _UpperCamelCase : List[Any] = model.encoder.embeddings(lowercase_ )
assert torch.allclose(lowercase_ ,lowercase_ ,atol=1e-3 )
# verify encoder hidden states
_UpperCamelCase : Any = original_model.encoder(lowercase_ )
_UpperCamelCase : Any = model.encoder(lowercase_ ).last_hidden_state
assert torch.allclose(lowercase_ ,lowercase_ ,atol=1e-2 )
# verify decoder hidden states
_UpperCamelCase : Any = original_model(lowercase_ ,lowercase_ ,lowercase_ ).logits
_UpperCamelCase : List[str] = model(lowercase_ ,decoder_input_ids=lowercase_ ).logits
assert torch.allclose(lowercase_ ,lowercase_ ,atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase_ )
processor.save_pretrained(lowercase_ )
if push_to_hub:
model.push_to_hub("nielsr/" + model_name.split("/" )[-1] ,commit_message="Update model" )
processor.push_to_hub("nielsr/" + model_name.split("/" )[-1] ,commit_message="Update model" )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub.",
)
lowerCamelCase__ = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 51 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowerCamelCase__ = TypeVar("KEY")
lowerCamelCase__ = TypeVar("VAL")
@dataclass(frozen=_UpperCamelCase , slots=_UpperCamelCase )
class __SCREAMING_SNAKE_CASE ( Generic[KEY, VAL] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :KEY
SCREAMING_SNAKE_CASE__ :VAL
class __SCREAMING_SNAKE_CASE ( _Item ):
'''simple docstring'''
def __init__( self : List[str] ) -> None:
super().__init__(__a , __a )
def __bool__( self : Dict ) -> bool:
return False
lowerCamelCase__ = _DeletedItem()
class __SCREAMING_SNAKE_CASE ( MutableMapping[KEY, VAL] ):
'''simple docstring'''
def __init__( self : int , __a : int = 8 , __a : float = 0.75 ) -> None:
_UpperCamelCase : str = initial_block_size
_UpperCamelCase : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
_UpperCamelCase : List[str] = capacity_factor
_UpperCamelCase : Dict = 0
def __SCREAMING_SNAKE_CASE ( self : int , __a : KEY ) -> int:
return hash(__a ) % len(self._buckets )
def __SCREAMING_SNAKE_CASE ( self : List[Any] , __a : int ) -> int:
return (ind + 1) % len(self._buckets )
def __SCREAMING_SNAKE_CASE ( self : Tuple , __a : int , __a : KEY , __a : VAL ) -> bool:
_UpperCamelCase : List[Any] = self._buckets[ind]
if not stored:
_UpperCamelCase : Tuple = _Item(__a , __a )
self._len += 1
return True
elif stored.key == key:
_UpperCamelCase : Union[str, Any] = _Item(__a , __a )
return True
else:
return False
def __SCREAMING_SNAKE_CASE ( self : List[str] ) -> bool:
_UpperCamelCase : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(__a )
def __SCREAMING_SNAKE_CASE ( self : str ) -> bool:
if len(self._buckets ) <= self._initial_block_size:
return False
_UpperCamelCase : List[str] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __a : int ) -> None:
_UpperCamelCase : Any = self._buckets
_UpperCamelCase : List[Any] = [None] * new_size
_UpperCamelCase : List[str] = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def __SCREAMING_SNAKE_CASE ( self : int ) -> None:
self._resize(len(self._buckets ) * 2 )
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> None:
self._resize(len(self._buckets ) // 2 )
def __SCREAMING_SNAKE_CASE ( self : List[Any] , __a : KEY ) -> Iterator[int]:
_UpperCamelCase : str = self._get_bucket_index(__a )
for _ in range(len(self._buckets ) ):
yield ind
_UpperCamelCase : Tuple = self._get_next_ind(__a )
def __SCREAMING_SNAKE_CASE ( self : Optional[int] , __a : KEY , __a : VAL ) -> None:
for ind in self._iterate_buckets(__a ):
if self._try_set(__a , __a , __a ):
break
def __setitem__( self : int , __a : KEY , __a : VAL ) -> None:
if self._is_full():
self._size_up()
self._add_item(__a , __a )
def __delitem__( self : str , __a : KEY ) -> None:
for ind in self._iterate_buckets(__a ):
_UpperCamelCase : Tuple = self._buckets[ind]
if item is None:
raise KeyError(__a )
if item is _deleted:
continue
if item.key == key:
_UpperCamelCase : List[Any] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : str , __a : KEY ) -> VAL:
for ind in self._iterate_buckets(__a ):
_UpperCamelCase : Tuple = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(__a )
def __len__( self : List[Any] ) -> int:
return self._len
def __iter__( self : List[str] ) -> Iterator[KEY]:
yield from (item.key for item in self._buckets if item)
def __repr__( self : List[str] ) -> str:
_UpperCamelCase : Optional[int] = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 51 | 1 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_SCREAMING_SNAKE_CASE = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
_SCREAMING_SNAKE_CASE = 50_003
_SCREAMING_SNAKE_CASE = 50_002
@require_sentencepiece
@require_tokenizers
class a ( UpperCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase :Tuple = PLBartTokenizer
lowerCamelCase :str = None
lowerCamelCase :Optional[Any] = False
def UpperCAmelCase ( self ) -> List[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_A = PLBartTokenizer(_a , language_codes="""base""" , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase ( self ) -> Optional[int]:
_A = PLBartTokenizer(_a , language_codes="""base""" , keep_accents=_a )
_A = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_a , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
_A = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_A = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_A = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
_A = tokenizer.vocab_size
_A = [tokenizer.convert_ids_to_tokens(_a ) for x in range(end - 4 , _a )]
self.assertListEqual(_a , ["""__java__""", """__python__""", """__en_XX__""", """<mask>"""] )
_A = """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
_A = tokenizer(_a ).input_ids
self.assertEqual(
tokenizer.decode(_a , skip_special_tokens=_a , clean_up_tokenization_spaces=_a ) , _a , )
def UpperCAmelCase ( self ) -> Tuple:
_A = PLBartTokenizer(_a , language_codes="""multi""" , keep_accents=_a )
_A = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_a , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
_A = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_A = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_A = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
_A = tokenizer.vocab_size
_A = [tokenizer.convert_ids_to_tokens(_a ) for x in range(end - 7 , _a )]
self.assertListEqual(
_a , ["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""] )
_A = """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
_A = tokenizer(_a ).input_ids
self.assertEqual(
tokenizer.decode(_a , skip_special_tokens=_a , clean_up_tokenization_spaces=_a ) , _a , )
@require_torch
@require_sentencepiece
@require_tokenizers
class a ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase :Tuple = '''uclanlp/plbart-python-en_XX'''
lowerCamelCase :Optional[int] = [
'''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
'''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
]
lowerCamelCase :str = [
'''Returns the maximum value of a b c.''',
'''Sums the values of a b c.''',
]
lowerCamelCase :List[str] = [
134,
5452,
33460,
33441,
33463,
33465,
33463,
33449,
988,
20,
33456,
19,
33456,
771,
39,
4258,
889,
3318,
33441,
33463,
33465,
33463,
33449,
2471,
2,
PYTHON_CODE,
]
@classmethod
def UpperCAmelCase ( cls ) -> int:
_A = PLBartTokenizer.from_pretrained(
cls.checkpoint_name , language_codes="""base""" , src_lang="""python""" , tgt_lang="""en_XX""" )
_A = 1
return cls
def UpperCAmelCase ( self ) -> Optional[int]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""] , 5_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""] , 5_00_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""] , 5_00_03 )
def UpperCAmelCase ( self ) -> Optional[Any]:
_A = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _a )
def UpperCAmelCase ( self ) -> int:
self.assertIn(_a , self.tokenizer.all_special_ids )
_A = [EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
_A = self.tokenizer.decode(_a , skip_special_tokens=_a )
_A = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_a )
self.assertEqual(_a , _a )
self.assertNotIn(self.tokenizer.eos_token , _a )
def UpperCAmelCase ( self ) -> List[str]:
_A = ["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 20]
self.assertIsInstance(src_text[0] , _a )
_A = 10
_A = self.tokenizer(_a , max_length=_a , truncation=_a ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , _a )
self.assertEqual(len(_a ) , _a )
def UpperCAmelCase ( self ) -> Any:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""] ) , [5_00_04, 5_00_01] )
def UpperCAmelCase ( self ) -> Dict:
_A = tempfile.mkdtemp()
_A = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(_a )
_A = PLBartTokenizer.from_pretrained(_a )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , _a )
@require_torch
def UpperCAmelCase ( self ) -> Optional[Any]:
_A = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_a , return_tensors="""pt""" )
_A = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist() , [2, PYTHON_CODE] )
self.assertEqual(batch.decoder_input_ids[1][0] , _a )
self.assertEqual(batch.decoder_input_ids[1][-1] , 2 )
self.assertEqual(batch.labels[1][-2:].tolist() , [2, EN_CODE] )
@require_torch
def UpperCAmelCase ( self ) -> Tuple:
_A = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=_a , truncation=_a , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
_A = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(_a , _a )
self.assertEqual((2, 26) , batch.input_ids.shape )
self.assertEqual((2, 26) , batch.attention_mask.shape )
_A = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , _a )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, PYTHON_CODE] )
def UpperCAmelCase ( self ) -> Any:
_A = self.tokenizer(self.src_text , padding=_a , truncation=_a , max_length=3 , return_tensors="""pt""" )
_A = self.tokenizer(
text_target=self.tgt_text , padding=_a , truncation=_a , max_length=10 , return_tensors="""pt""" )
_A = targets["""input_ids"""]
_A = shift_tokens_right(_a , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCAmelCase ( self ) -> str:
_A = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""java""" )
self.assertEqual(
nested_simplify(_a ) , {
# A, test, EOS, en_XX
"""input_ids""": [[1_50, 2_42, 2, 5_00_03]],
"""attention_mask""": [[1, 1, 1, 1]],
# java
"""forced_bos_token_id""": 5_00_01,
} , )
| 401 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowerCAmelCase : List[str] = object()
# For specifying empty leaf dict `{}`
lowerCAmelCase : Any = object()
def a__ ( snake_case__ , snake_case__ ) -> List[Any]:
lowerCamelCase = tuple((re.compile(x + """$""" ) for x in qs) )
for i in range(len(snake_case__ ) - len(snake_case__ ) + 1 ):
lowerCamelCase = [x.match(snake_case__ ) for x, y in zip(snake_case__ , ks[i:] )]
if matches and all(snake_case__ ):
return True
return False
def a__ ( snake_case__ ) -> str:
def replace(snake_case__ , snake_case__ ):
for rule, replacement in rules:
if _match(snake_case__ , snake_case__ ):
return replacement
return val
return replace
def a__ ( ) -> Union[str, Any]:
return [
# embeddings
(("transformer", "wpe", "embedding"), P("""mp""" , snake_case__ )),
(("transformer", "wte", "embedding"), P("""mp""" , snake_case__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(snake_case__ , """mp""" )),
(("attention", "out_proj", "kernel"), P("""mp""" , snake_case__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(snake_case__ , """mp""" )),
(("mlp", "c_fc", "bias"), P("""mp""" )),
(("mlp", "c_proj", "kernel"), P("""mp""" , snake_case__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def a__ ( snake_case__ ) -> Optional[Any]:
lowerCamelCase = _get_partition_rules()
lowerCamelCase = _replacement_rules(snake_case__ )
lowerCamelCase = {k: _unmatched for k in flatten_dict(snake_case__ )}
lowerCamelCase = {k: replace(snake_case__ , snake_case__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(snake_case__ ) )
| 543 | 0 |
'''simple docstring'''
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 A ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=7 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=18 , SCREAMING_SNAKE_CASE=30 , SCREAMING_SNAKE_CASE=400 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=[0.48_145_466, 0.4_578_275, 0.40_821_073] , SCREAMING_SNAKE_CASE=[0.26_862_954, 0.26_130_258, 0.27_577_711] , SCREAMING_SNAKE_CASE=True , ) -> Optional[int]:
"""simple docstring"""
A : Dict = size if size is not None else {'''height''': 224, '''width''': 224}
A : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
A : Tuple = parent
A : int = batch_size
A : Optional[int] = num_channels
A : str = image_size
A : Optional[int] = min_resolution
A : List[str] = max_resolution
A : List[Any] = do_resize
A : Optional[int] = size
A : Optional[int] = do_center_crop
A : Any = crop_size
A : Any = do_normalize
A : Optional[Any] = image_mean
A : str = image_std
A : Dict = do_convert_rgb
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_convert_rgb": self.do_convert_rgb,
}
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
A : Dict = []
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:
A : Union[str, Any] = []
for i in range(self.batch_size ):
A : str = 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
A : Optional[int] = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
if torchify:
A : Dict = [torch.from_numpy(SCREAMING_SNAKE_CASE ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class A ( __snake_case , unittest.TestCase ):
__magic_name__ = ChineseCLIPImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Union[str, Any] = ChineseCLIPImageProcessingTester(self , do_center_crop=SCREAMING_SNAKE_CASE )
@property
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_resize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''size''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_center_crop''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''center_crop''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''image_mean''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''image_std''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_convert_rgb''' ) )
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Optional[int] = 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} )
A : 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 ) -> List[str]:
"""simple docstring"""
pass
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A : List[str] = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
A : 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
A : List[str] = image_processing(SCREAMING_SNAKE_CASE , 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 ) -> Any:
"""simple docstring"""
A : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A : Optional[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE , numpify=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
A : Optional[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
A : Tuple = image_processing(SCREAMING_SNAKE_CASE , 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"""
A : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A : Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE , torchify=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
A : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
A : str = image_processing(SCREAMING_SNAKE_CASE , 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 A ( __snake_case , unittest.TestCase ):
__magic_name__ = ChineseCLIPImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Optional[int] = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=SCREAMING_SNAKE_CASE )
A : Tuple = 3
@property
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_resize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''size''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_center_crop''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''center_crop''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''image_mean''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''image_std''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''do_convert_rgb''' ) )
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
pass
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A : List[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
A : Any = 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
A : Union[str, Any] = image_processing(SCREAMING_SNAKE_CASE , 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'''],
) , )
| 704 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase : str = logging.get_logger(__name__)
# TODO: upload to AWS
lowercase : Optional[Any] = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
),
}
class A ( __snake_case ):
__magic_name__ = '''retribert'''
def __init__( self , SCREAMING_SNAKE_CASE=30522 , SCREAMING_SNAKE_CASE=768 , SCREAMING_SNAKE_CASE=8 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=3072 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=512 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=1e-12 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=128 , SCREAMING_SNAKE_CASE=0 , **SCREAMING_SNAKE_CASE , ) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
A : List[Any] = vocab_size
A : Dict = hidden_size
A : Any = num_hidden_layers
A : Any = num_attention_heads
A : List[Any] = hidden_act
A : Any = intermediate_size
A : str = hidden_dropout_prob
A : int = attention_probs_dropout_prob
A : List[Any] = max_position_embeddings
A : Tuple = type_vocab_size
A : Optional[Any] = initializer_range
A : Union[str, Any] = layer_norm_eps
A : Dict = share_encoders
A : Dict = projection_dim
| 343 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
__lowerCamelCase = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)
def a ( __UpperCAmelCase : str , __UpperCAmelCase : int = 1 , __UpperCAmelCase : str = "new" , __UpperCAmelCase : list | None = None ) -> dict:
__magic_name__: Optional[Any] = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(__UpperCAmelCase ) - valid_terms ) ):
__magic_name__: Optional[int] = f'Invalid search term: {invalid_search_terms}'
raise ValueError(__UpperCAmelCase )
__magic_name__: Optional[int] = requests.get(
f'https://reddit.com/r/{subreddit}/{age}.json?limit={limit}' , headers={"""User-agent""": """A random string"""} , )
if response.status_code == 4_2_9:
raise requests.HTTPError
__magic_name__: Optional[int] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(__UpperCAmelCase )}
__magic_name__: str = {}
for id_ in range(__UpperCAmelCase ):
__magic_name__: str = {
item: data["""data"""]["""children"""][id_]["""data"""][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))
| 96 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25,
"""L""": 4.03,
"""C""": 2.78,
"""U""": 2.76,
"""M""": 2.41,
"""W""": 2.36,
"""F""": 2.23,
"""G""": 2.02,
"""Y""": 1.97,
"""P""": 1.93,
"""B""": 1.29,
"""V""": 0.98,
"""K""": 0.77,
"""J""": 0.15,
"""X""": 0.15,
"""Q""": 0.10,
"""Z""": 0.07,
}
_A: List[Any] = """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
_A: str = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def _lowerCAmelCase ( _lowerCAmelCase )-> dict[str, int]:
__UpperCAmelCase = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def _lowerCAmelCase ( _lowerCAmelCase )-> str:
return x[0]
def _lowerCAmelCase ( _lowerCAmelCase )-> str:
__UpperCAmelCase = get_letter_count(_lowerCAmelCase )
__UpperCAmelCase = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_lowerCAmelCase )
__UpperCAmelCase = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_lowerCAmelCase )
__UpperCAmelCase = ''.join(freq_to_letter[freq] )
__UpperCAmelCase = list(freq_to_letter_str.items() )
freq_pairs.sort(key=_lowerCAmelCase , reverse=_lowerCAmelCase )
__UpperCAmelCase = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
__UpperCAmelCase = get_frequency_order(_lowerCAmelCase )
__UpperCAmelCase = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 126 | 0 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
A = 'src/transformers'
# Matches is_xxx_available()
A = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
A = re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A = re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
A = re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
A = re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A = re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
A = re.compile(r'^\s+"([^"]+)",')
# Catches a line with objects between brackets only: ["foo", "bar"],
A = re.compile(r'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
A = re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
A = re.compile(r'^\s*try:')
# Catches a line with else:
A = re.compile(r'^\s*else:')
def lowerCAmelCase__ ( lowerCamelCase__ ) -> Optional[int]:
if _re_test_backend.search(_SCREAMING_SNAKE_CASE ) is None:
return None
A = [b[0] for b in _re_backend.findall(_SCREAMING_SNAKE_CASE )]
backends.sort()
return "_and_".join(_SCREAMING_SNAKE_CASE )
def lowerCAmelCase__ ( lowerCamelCase__ ) -> Optional[int]:
with open(_SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
A = f.readlines()
A = 0
while line_index < len(_SCREAMING_SNAKE_CASE ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(_SCREAMING_SNAKE_CASE ):
return None
# First grab the objects without a specific backend in _import_structure
A = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
A = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(_SCREAMING_SNAKE_CASE ):
A = _re_one_line_import_struct.search(_SCREAMING_SNAKE_CASE ).groups()[0]
A = re.findall(R'\[([^\]]+)\]' , _SCREAMING_SNAKE_CASE )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
A = _re_import_struct_key_value.search(_SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
A = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(_SCREAMING_SNAKE_CASE ) > 0]
objects.extend(_SCREAMING_SNAKE_CASE )
elif line.startswith(' ' * 8 + '\"' ):
objects.append(line[9:-3] )
line_index += 1
A = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
A = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
A = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
A = lines[line_index]
if _re_import_struct_add_one.search(_SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_import_struct_add_one.search(_SCREAMING_SNAKE_CASE ).groups()[0] )
elif _re_import_struct_add_many.search(_SCREAMING_SNAKE_CASE ) is not None:
A = _re_import_struct_add_many.search(_SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
A = [obj[1:-1] for obj in imports if len(_SCREAMING_SNAKE_CASE ) > 0]
objects.extend(_SCREAMING_SNAKE_CASE )
elif _re_between_brackets.search(_SCREAMING_SNAKE_CASE ) is not None:
A = _re_between_brackets.search(_SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
A = [obj[1:-1] for obj in imports if len(_SCREAMING_SNAKE_CASE ) > 0]
objects.extend(_SCREAMING_SNAKE_CASE )
elif _re_quote_object.search(_SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_quote_object.search(_SCREAMING_SNAKE_CASE ).groups()[0] )
elif line.startswith(' ' * 8 + '\"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '\"' ):
objects.append(line[13:-3] )
line_index += 1
A = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
A = []
while (
line_index < len(_SCREAMING_SNAKE_CASE )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
A = lines[line_index]
A = _re_import.search(_SCREAMING_SNAKE_CASE )
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
A = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(_SCREAMING_SNAKE_CASE ):
# If the line is an if is_backend_available, we grab all objects associated.
A = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
A = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
A = lines[line_index]
A = _re_import.search(_SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
A = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
def find_duplicates(lowerCamelCase__ ):
return [k for k, v in collections.Counter(_SCREAMING_SNAKE_CASE ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
A = []
for key in import_dict_objects.keys():
A = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
A = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}""" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
A = 'base imports' if key == 'none' else f"""{key} backend"""
errors.append(f"""Differences for {name}:""" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f""" {a} in TYPE_HINT but not in _import_structure.""" )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f""" {a} in _import_structure but not in TYPE_HINT.""" )
return errors
def lowerCAmelCase__ ( ) -> str:
A = []
for root, _, files in os.walk(_SCREAMING_SNAKE_CASE ):
if "__init__.py" in files:
A = os.path.join(_SCREAMING_SNAKE_CASE , '__init__.py' )
A = parse_init(_SCREAMING_SNAKE_CASE )
if objects is not None:
A = analyze_results(*_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
A = f"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append('\n'.join(_SCREAMING_SNAKE_CASE ) )
if len(_SCREAMING_SNAKE_CASE ) > 0:
raise ValueError('\n\n'.join(_SCREAMING_SNAKE_CASE ) )
def lowerCAmelCase__ ( ) -> int:
A = []
for path, directories, files in os.walk(_SCREAMING_SNAKE_CASE ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(_SCREAMING_SNAKE_CASE )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(_SCREAMING_SNAKE_CASE ) / folder).glob('*.py' ) ) ) == 0:
continue
A = str((Path(_SCREAMING_SNAKE_CASE ) / folder).relative_to(_SCREAMING_SNAKE_CASE ) )
A = short_path.replace(os.path.sep , '.' )
submodules.append(_SCREAMING_SNAKE_CASE )
for fname in files:
if fname == "__init__.py":
continue
A = str((Path(_SCREAMING_SNAKE_CASE ) / fname).relative_to(_SCREAMING_SNAKE_CASE ) )
A = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(_SCREAMING_SNAKE_CASE )
return submodules
A = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
'models.esm.openfold_utils',
]
def lowerCAmelCase__ ( ) -> Tuple:
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
A = direct_transformers_import(_SCREAMING_SNAKE_CASE )
A = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_SCREAMING_SNAKE_CASE , '__init__.py' ) , 'r' ) as f:
A = f.read()
import_structure_keys.update(set(re.findall(R'import_structure\[\"([^\"]*)\"\]' , _SCREAMING_SNAKE_CASE ) ) )
A = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_SCREAMING_SNAKE_CASE ) > 0:
A = '\n'.join(f"""- {module}""" for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registed in the main init of Transformers:\n'
f"""{list_of_modules}\n"""
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 702 |
"""simple docstring"""
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,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class UpperCAmelCase__ ( unittest.TestCase ):
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
A = tempfile.mkdtemp()
A = BlipImageProcessor()
A = GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model' )
A = BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert' )
A = InstructBlipProcessor(snake_case , snake_case , snake_case )
processor.save_pretrained(self.tmpdirname )
def A_ ( self : List[str] , **snake_case : str ) -> Dict:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case ).tokenizer
def A_ ( self : int , **snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case ).image_processor
def A_ ( self : Any , **snake_case : Union[str, Any] ) -> Any:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case ).qformer_tokenizer
def A_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
A = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A = [Image.fromarray(np.moveaxis(snake_case , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
A = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
A = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
A = self.get_image_processor(do_normalize=snake_case , padding_value=1.0 )
A = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=snake_case , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case )
self.assertIsInstance(processor.qformer_tokenizer , snake_case )
def A_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
A = self.get_image_processor()
A = self.get_tokenizer()
A = self.get_qformer_tokenizer()
A = InstructBlipProcessor(
tokenizer=snake_case , image_processor=snake_case , qformer_tokenizer=snake_case )
A = self.prepare_image_inputs()
A = image_processor(snake_case , return_tensors='np' )
A = processor(images=snake_case , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
A = self.get_image_processor()
A = self.get_tokenizer()
A = self.get_qformer_tokenizer()
A = InstructBlipProcessor(
tokenizer=snake_case , image_processor=snake_case , qformer_tokenizer=snake_case )
A = 'lower newer'
A = processor(text=snake_case )
A = tokenizer(snake_case , return_token_type_ids=snake_case )
A = qformer_tokenizer(snake_case , return_token_type_ids=snake_case )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor['qformer_' + key] )
def A_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
A = self.get_image_processor()
A = self.get_tokenizer()
A = self.get_qformer_tokenizer()
A = InstructBlipProcessor(
tokenizer=snake_case , image_processor=snake_case , qformer_tokenizer=snake_case )
A = 'lower newer'
A = self.prepare_image_inputs()
A = processor(text=snake_case , images=snake_case )
self.assertListEqual(
list(inputs.keys() ) , ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'] , )
# test if it raises when no input is passed
with pytest.raises(snake_case ):
processor()
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
A = self.get_image_processor()
A = self.get_tokenizer()
A = self.get_qformer_tokenizer()
A = InstructBlipProcessor(
tokenizer=snake_case , image_processor=snake_case , qformer_tokenizer=snake_case )
A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A = processor.batch_decode(snake_case )
A = tokenizer.batch_decode(snake_case )
self.assertListEqual(snake_case , snake_case )
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
A = self.get_image_processor()
A = self.get_tokenizer()
A = self.get_qformer_tokenizer()
A = InstructBlipProcessor(
tokenizer=snake_case , image_processor=snake_case , qformer_tokenizer=snake_case )
A = 'lower newer'
A = self.prepare_image_inputs()
A = processor(text=snake_case , images=snake_case )
self.assertListEqual(
list(inputs.keys() ) , ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'] , )
| 109 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__A : Optional[int] = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ['BeitFeatureExtractor']
__A : int = ['BeitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BeitForImageClassification',
'BeitForMaskedImageModeling',
'BeitForSemanticSegmentation',
'BeitModel',
'BeitPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'FlaxBeitForImageClassification',
'FlaxBeitForMaskedImageModeling',
'FlaxBeitModel',
'FlaxBeitPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
__A : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 334 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError("""'str' object cannot be interpreted as an integer""" )
if num == 0:
return "0b0"
snake_case_ : str = False
if num < 0:
snake_case_ : Optional[int] = True
snake_case_ : List[str] = -num
snake_case_ : list[int] = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(lowerCamelCase_ ) for e in binary )
return "0b" + "".join(str(lowerCamelCase_ ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod() | 334 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Union[str, Any] =logging.get_logger(__name__)
A__ : Dict ={
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
),
}
class __A ( _SCREAMING_SNAKE_CASE ):
lowerCamelCase ='''xlm-prophetnet'''
lowerCamelCase =['''past_key_values''']
lowerCamelCase ={
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self : List[Any] , lowerCamelCase : Optional[float] = 0.1 , lowerCamelCase : Optional[Union[str, Callable]] = "gelu" , lowerCamelCase : Optional[int] = 3_05_22 , lowerCamelCase : Optional[int] = 10_24 , lowerCamelCase : Optional[int] = 40_96 , lowerCamelCase : Optional[int] = 12 , lowerCamelCase : Optional[int] = 16 , lowerCamelCase : Optional[int] = 40_96 , lowerCamelCase : Optional[int] = 12 , lowerCamelCase : Optional[int] = 16 , lowerCamelCase : Optional[float] = 0.1 , lowerCamelCase : Optional[float] = 0.1 , lowerCamelCase : Optional[int] = 5_12 , lowerCamelCase : Optional[float] = 0.02 , lowerCamelCase : Optional[bool] = True , lowerCamelCase : Optional[bool] = True , lowerCamelCase : Optional[int] = 0 , lowerCamelCase : Optional[int] = 2 , lowerCamelCase : Optional[int] = 32 , lowerCamelCase : Optional[int] = 1_28 , lowerCamelCase : Optional[bool] = False , lowerCamelCase : Optional[float] = 0.0 , lowerCamelCase : Optional[bool] = True , lowerCamelCase : Optional[int] = 0 , lowerCamelCase : Optional[int] = 1 , lowerCamelCase : Optional[int] = 2 , **lowerCamelCase : List[Any] , ):
"""simple docstring"""
__A : List[Any] = vocab_size
__A : Union[str, Any] = hidden_size
__A : int = encoder_ffn_dim
__A : Optional[int] = num_encoder_layers
__A : Any = num_encoder_attention_heads
__A : Optional[int] = decoder_ffn_dim
__A : Optional[Any] = num_decoder_layers
__A : Any = num_decoder_attention_heads
__A : List[str] = max_position_embeddings
__A : Tuple = init_std # Normal(0, this parameter)
__A : str = activation_function
# parameters for xlmprophetnet
__A : Tuple = ngram
__A : Dict = num_buckets
__A : Optional[Any] = relative_max_distance
__A : str = disable_ngram_loss
__A : List[str] = eps
# 3 Types of Dropout
__A : int = attention_dropout
__A : Union[str, Any] = activation_dropout
__A : List[str] = dropout
__A : List[str] = use_cache
super().__init__(
pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , is_encoder_decoder=lowerCamelCase , add_cross_attention=lowerCamelCase , decoder_start_token_id=lowerCamelCase , **lowerCamelCase , )
@property
def lowercase_( self : Union[str, Any] ):
"""simple docstring"""
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def lowercase_( self : Any , lowerCamelCase : List[str] ):
"""simple docstring"""
raise NotImplementedError(
"""This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
""" `num_decoder_layers`.""" ) | 716 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : int =logging.get_logger(__name__)
A__ : Union[str, Any] ={
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
),
}
class __A ( _SCREAMING_SNAKE_CASE ):
lowerCamelCase ='''dpr'''
def __init__( self : Optional[int] , lowerCamelCase : Tuple=3_05_22 , lowerCamelCase : List[str]=7_68 , lowerCamelCase : Union[str, Any]=12 , lowerCamelCase : Tuple=12 , lowerCamelCase : Dict=30_72 , lowerCamelCase : List[Any]="gelu" , lowerCamelCase : List[Any]=0.1 , lowerCamelCase : Union[str, Any]=0.1 , lowerCamelCase : Optional[Any]=5_12 , lowerCamelCase : List[Any]=2 , lowerCamelCase : int=0.02 , lowerCamelCase : Union[str, Any]=1e-1_2 , lowerCamelCase : int=0 , lowerCamelCase : List[str]="absolute" , lowerCamelCase : int = 0 , **lowerCamelCase : List[Any] , ):
"""simple docstring"""
super().__init__(pad_token_id=lowerCamelCase , **lowerCamelCase )
__A : Dict = vocab_size
__A : int = hidden_size
__A : str = num_hidden_layers
__A : Union[str, Any] = num_attention_heads
__A : Union[str, Any] = hidden_act
__A : Optional[Any] = intermediate_size
__A : str = hidden_dropout_prob
__A : int = attention_probs_dropout_prob
__A : int = max_position_embeddings
__A : Dict = type_vocab_size
__A : Dict = initializer_range
__A : Union[str, Any] = layer_norm_eps
__A : int = projection_dim
__A : List[Any] = position_embedding_type
| 499 | 0 |
"""simple docstring"""
def __A ( a_ :List[Any]) -> Optional[int]:
__a , __a : int = [], []
while len(a_) > 1:
__a , __a : Any = min(a_), max(a_)
start.append(a_)
end.append(a_)
collection.remove(a_)
collection.remove(a_)
end.reverse()
return start + collection + end
if __name__ == "__main__":
A = input('''Enter numbers separated by a comma:\n''').strip()
A = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''') | 52 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = '''hf-internal-testing/tiny-random-t5'''
lowerCamelCase__ = AutoTokenizer.from_pretrained(__lowerCAmelCase )
lowerCamelCase__ = AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase )
lowerCamelCase__ = tokenizer('''This is me''' , return_tensors='''pt''' )
lowerCamelCase__ = model.to_bettertransformer()
self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
lowerCamelCase__ = model.generate(**__lowerCAmelCase )
lowerCamelCase__ = model.reverse_bettertransformer()
self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowerCAmelCase )
lowerCamelCase__ = AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase )
self.assertFalse(
any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
lowerCamelCase__ = model_reloaded.generate(**__lowerCAmelCase )
self.assertTrue(torch.allclose(__lowerCAmelCase , __lowerCAmelCase ) )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = '''hf-internal-testing/tiny-random-t5'''
lowerCamelCase__ = AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase )
lowerCamelCase__ = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(__lowerCAmelCase ):
model.save_pretrained(__lowerCAmelCase )
lowerCamelCase__ = model.reverse_bettertransformer()
model.save_pretrained(__lowerCAmelCase )
| 481 | 0 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=1e-12 ):
'''simple docstring'''
UpperCAmelCase = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(lowerCAmelCase , axis=1 ) , a_min=lowerCAmelCase ) ).T
UpperCAmelCase = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(lowerCAmelCase , axis=1 ) , a_min=lowerCAmelCase ) ).T
return jnp.matmul(lowerCAmelCase , norm_emb_a.T )
class UpperCamelCase_ ( nn.Module ):
_A : CLIPConfig
_A : jnp.dtype = jnp.floataa
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = FlaxCLIPVisionModule(self.config.vision_config )
UpperCAmelCase = nn.Dense(self.config.projection_dim , use_bias=snake_case__ , dtype=self.dtype )
UpperCAmelCase = self.param("""concept_embeds""" , jax.nn.initializers.ones , (17, self.config.projection_dim) )
UpperCAmelCase = self.param(
"""special_care_embeds""" , jax.nn.initializers.ones , (3, self.config.projection_dim) )
UpperCAmelCase = self.param("""concept_embeds_weights""" , jax.nn.initializers.ones , (17,) )
UpperCAmelCase = self.param("""special_care_embeds_weights""" , jax.nn.initializers.ones , (3,) )
def __call__( self , snake_case__ ) -> Any:
"""simple docstring"""
UpperCAmelCase = self.vision_model(snake_case__ )[1]
UpperCAmelCase = self.visual_projection(snake_case__ )
UpperCAmelCase = jax_cosine_distance(snake_case__ , self.special_care_embeds )
UpperCAmelCase = jax_cosine_distance(snake_case__ , self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
UpperCAmelCase = 0.0
UpperCAmelCase = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
UpperCAmelCase = jnp.round(snake_case__ , 3 )
UpperCAmelCase = jnp.any(special_scores > 0 , axis=1 , keepdims=snake_case__ )
# Use a lower threshold if an image has any special care concept
UpperCAmelCase = is_special_care * 0.01
UpperCAmelCase = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
UpperCAmelCase = jnp.round(snake_case__ , 3 )
UpperCAmelCase = jnp.any(concept_scores > 0 , axis=1 )
return has_nsfw_concepts
class UpperCamelCase_ ( a_ ):
_A : str = CLIPConfig
_A : List[Any] = 'clip_input'
_A : Dict = FlaxStableDiffusionSafetyCheckerModule
def __init__( self , snake_case__ , snake_case__ = None , snake_case__ = 0 , snake_case__ = jnp.floataa , snake_case__ = True , **snake_case__ , ) -> Optional[int]:
"""simple docstring"""
if input_shape is None:
UpperCAmelCase = (1, 2_24, 2_24, 3)
UpperCAmelCase = self.module_class(config=snake_case__ , dtype=snake_case__ , **snake_case__ )
super().__init__(snake_case__ , snake_case__ , input_shape=snake_case__ , seed=snake_case__ , dtype=snake_case__ , _do_init=_do_init )
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ = None ) -> FrozenDict:
"""simple docstring"""
UpperCAmelCase = jax.random.normal(snake_case__ , snake_case__ )
UpperCAmelCase , UpperCAmelCase = jax.random.split(snake_case__ )
UpperCAmelCase = {"""params""": params_rng, """dropout""": dropout_rng}
UpperCAmelCase = self.module.init(snake_case__ , snake_case__ )["""params"""]
return random_params
def __call__( self , snake_case__ , snake_case__ = None , ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = jnp.transpose(snake_case__ , (0, 2, 3, 1) )
return self.module.apply(
{"""params""": params or self.params} , jnp.array(snake_case__ , dtype=jnp.floataa ) , rngs={} , )
| 715 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase_ : Optional[int] = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : str = [
'''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GraphormerForGraphClassification''',
'''GraphormerModel''',
'''GraphormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 378 | 0 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> List[Any]:
_lowercase : Optional[int] = WavaVecaForSequenceClassification.from_pretrained(__lowerCAmelCase , config=__lowerCAmelCase )
_lowercase : str = downstream_dict['projector.weight']
_lowercase : Dict = downstream_dict['projector.bias']
_lowercase : Optional[Any] = downstream_dict['model.post_net.linear.weight']
_lowercase : Union[str, Any] = downstream_dict['model.post_net.linear.bias']
return model
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
_lowercase : Dict = WavaVecaForAudioFrameClassification.from_pretrained(__lowerCAmelCase , config=__lowerCAmelCase )
_lowercase : Tuple = downstream_dict['model.linear.weight']
_lowercase : int = downstream_dict['model.linear.bias']
return model
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> str:
_lowercase : List[Any] = WavaVecaForXVector.from_pretrained(__lowerCAmelCase , config=__lowerCAmelCase )
_lowercase : Optional[int] = downstream_dict['connector.weight']
_lowercase : Union[str, Any] = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_lowercase : Optional[Any] = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_lowercase : Tuple = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_lowercase : Optional[int] = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
_lowercase : Optional[int] = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
_lowercase : Union[str, Any] = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
_lowercase : Tuple = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
_lowercase : List[str] = downstream_dict['objective.W']
return model
@torch.no_grad()
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
_lowercase : Dict = torch.load(__lowerCAmelCase , map_location='cpu' )
_lowercase : Optional[Any] = checkpoint['Downstream']
_lowercase : Any = WavaVecaConfig.from_pretrained(__lowerCAmelCase )
_lowercase : List[Any] = WavaVecaFeatureExtractor.from_pretrained(
__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , do_normalize=__lowerCAmelCase )
_lowercase : Tuple = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
_lowercase : int = convert_classification(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
elif arch.endswith('ForAudioFrameClassification' ):
_lowercase : Tuple = convert_diarization(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
elif arch.endswith('ForXVector' ):
_lowercase : str = convert_xvector(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_lowercase : List[str] = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(__lowerCAmelCase )
hf_model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Optional[int] = 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.")
SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 89 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
UpperCAmelCase : List[str] = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = "timesformer"
def __init__( self : int , lowerCAmelCase_ : Dict=2_2_4 , lowerCAmelCase_ : Any=1_6 , lowerCAmelCase_ : Union[str, Any]=3 , lowerCAmelCase_ : Union[str, Any]=8 , lowerCAmelCase_ : Dict=7_6_8 , lowerCAmelCase_ : Dict=1_2 , lowerCAmelCase_ : Optional[int]=1_2 , lowerCAmelCase_ : str=3_0_7_2 , lowerCAmelCase_ : Dict="gelu" , lowerCAmelCase_ : Dict=0.0 , lowerCAmelCase_ : int=0.0 , lowerCAmelCase_ : Any=0.02 , lowerCAmelCase_ : List[str]=1E-6 , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : Optional[int]="divided_space_time" , lowerCAmelCase_ : Optional[Any]=0 , **lowerCAmelCase_ : Dict , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase_)
lowercase_ = image_size
lowercase_ = patch_size
lowercase_ = num_channels
lowercase_ = num_frames
lowercase_ = hidden_size
lowercase_ = num_hidden_layers
lowercase_ = num_attention_heads
lowercase_ = intermediate_size
lowercase_ = hidden_act
lowercase_ = hidden_dropout_prob
lowercase_ = attention_probs_dropout_prob
lowercase_ = initializer_range
lowercase_ = layer_norm_eps
lowercase_ = qkv_bias
lowercase_ = attention_type
lowercase_ = drop_path_rate
| 567 | 0 |
import requests
__lowerCAmelCase : Dict = "YOUR API KEY"
def lowerCAmelCase ( UpperCamelCase__ : str , UpperCamelCase__ : str = giphy_api_key ):
"""simple docstring"""
__UpperCAmelCase = '''+'''.join(query.split() )
__UpperCAmelCase = f"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
__UpperCAmelCase = requests.get(UpperCamelCase__ ).json()['''data''']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("\n".join(get_gifs("space ship")))
| 701 | '''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowerCAmelCase : Any = ""
__lowerCAmelCase : int = ""
__lowerCAmelCase : Union[str, Any] = ""
__lowerCAmelCase : Any = 1 # (0 is vertical, 1 is horizontal)
def lowerCAmelCase ( ):
"""simple docstring"""
__UpperCAmelCase , __UpperCAmelCase = get_dataset(UpperCamelCase__ , UpperCamelCase__ )
print('''Processing...''' )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = update_image_and_anno(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for index, image in enumerate(UpperCamelCase__ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
__UpperCAmelCase = random_chars(3_2 )
__UpperCAmelCase = paths[index].split(os.sep )[-1].rsplit('''.''' , 1 )[0]
__UpperCAmelCase = f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
cva.imwrite(f"""/{file_root}.jpg""" , UpperCamelCase__ , [cva.IMWRITE_JPEG_QUALITY, 8_5] )
print(f"""Success {index+1}/{len(UpperCamelCase__ )} with {file_name}""" )
__UpperCAmelCase = []
for anno in new_annos[index]:
__UpperCAmelCase = f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
annos_list.append(UpperCamelCase__ )
with open(f"""/{file_root}.txt""" , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def lowerCAmelCase ( UpperCamelCase__ : str , UpperCamelCase__ : str ):
"""simple docstring"""
__UpperCAmelCase = []
__UpperCAmelCase = []
for label_file in glob.glob(os.path.join(UpperCamelCase__ , '''*.txt''' ) ):
__UpperCAmelCase = label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(UpperCamelCase__ ) as in_file:
__UpperCAmelCase = in_file.readlines()
__UpperCAmelCase = os.path.join(UpperCamelCase__ , f"""{label_name}.jpg""" )
__UpperCAmelCase = []
for obj_list in obj_lists:
__UpperCAmelCase = obj_list.rstrip('''\n''' ).split(''' ''' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(UpperCamelCase__ )
labels.append(UpperCamelCase__ )
return img_paths, labels
def lowerCAmelCase ( UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : int = 1 ):
"""simple docstring"""
__UpperCAmelCase = []
__UpperCAmelCase = []
__UpperCAmelCase = []
for idx in range(len(UpperCamelCase__ ) ):
__UpperCAmelCase = []
__UpperCAmelCase = img_list[idx]
path_list.append(UpperCamelCase__ )
__UpperCAmelCase = anno_list[idx]
__UpperCAmelCase = cva.imread(UpperCamelCase__ )
if flip_type == 1:
__UpperCAmelCase = cva.flip(UpperCamelCase__ , UpperCamelCase__ )
for bbox in img_annos:
__UpperCAmelCase = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
__UpperCAmelCase = cva.flip(UpperCamelCase__ , UpperCamelCase__ )
for bbox in img_annos:
__UpperCAmelCase = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(UpperCamelCase__ )
new_imgs_list.append(UpperCamelCase__ )
return new_imgs_list, new_annos_lists, path_list
def lowerCAmelCase ( UpperCamelCase__ : int = 3_2 ):
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
__UpperCAmelCase = ascii_lowercase + digits
return "".join(random.choice(UpperCamelCase__ ) for _ in range(UpperCamelCase__ ) )
if __name__ == "__main__":
main()
print("DONE ✅")
| 654 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
super().__init__()
self.register_modules(vqvae=_a , unet=_a , scheduler=_a )
@torch.no_grad()
def __call__( self , lowerCAmelCase_ = 1 , lowerCAmelCase_ = None , lowerCAmelCase_ = 0.0 , lowerCAmelCase_ = 50 , lowerCAmelCase_ = "pil" , lowerCAmelCase_ = True , **lowerCAmelCase_ , ) -> Optional[Any]:
_A = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=_a , )
_A = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_A = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(_a )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
_A = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_A = {}
if accepts_eta:
_A = eta
for t in self.progress_bar(self.scheduler.timesteps ):
_A = self.scheduler.scale_model_input(_a , _a )
# predict the noise residual
_A = self.unet(_a , _a ).sample
# compute the previous noisy sample x_t -> x_t-1
_A = self.scheduler.step(_a , _a , _a , **_a ).prev_sample
# decode the image latents with the VAE
_A = self.vqvae.decode(_a ).sample
_A = (image / 2 + 0.5).clamp(0 , 1 )
_A = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_A = self.numpy_to_pil(_a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_a )
| 401 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowerCAmelCase : List[str] = object()
# For specifying empty leaf dict `{}`
lowerCAmelCase : Any = object()
def a__ ( snake_case__ , snake_case__ ) -> List[Any]:
lowerCamelCase = tuple((re.compile(x + """$""" ) for x in qs) )
for i in range(len(snake_case__ ) - len(snake_case__ ) + 1 ):
lowerCamelCase = [x.match(snake_case__ ) for x, y in zip(snake_case__ , ks[i:] )]
if matches and all(snake_case__ ):
return True
return False
def a__ ( snake_case__ ) -> str:
def replace(snake_case__ , snake_case__ ):
for rule, replacement in rules:
if _match(snake_case__ , snake_case__ ):
return replacement
return val
return replace
def a__ ( ) -> Union[str, Any]:
return [
# embeddings
(("transformer", "wpe", "embedding"), P("""mp""" , snake_case__ )),
(("transformer", "wte", "embedding"), P("""mp""" , snake_case__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(snake_case__ , """mp""" )),
(("attention", "out_proj", "kernel"), P("""mp""" , snake_case__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(snake_case__ , """mp""" )),
(("mlp", "c_fc", "bias"), P("""mp""" )),
(("mlp", "c_proj", "kernel"), P("""mp""" , snake_case__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def a__ ( snake_case__ ) -> Optional[Any]:
lowerCamelCase = _get_partition_rules()
lowerCamelCase = _replacement_rules(snake_case__ )
lowerCamelCase = {k: _unmatched for k in flatten_dict(snake_case__ )}
lowerCamelCase = {k: replace(snake_case__ , snake_case__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(snake_case__ ) )
| 543 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def UpperCamelCase_ ( a_ ) ->Union[str, Any]:
if not is_accelerate_available():
return method
A =version.parse(accelerate.__version__ ).base_version
if version.parse(a_ ) < version.parse("0.17.0" ):
return method
def wrapper(self , *a_ , **a_ ):
if hasattr(self , "_hf_hook" ) and hasattr(self._hf_hook , "pre_forward" ):
self._hf_hook.pre_forward(self )
return method(self , *a_ , **a_ )
return wrapper
| 689 |
def UpperCamelCase_ ( a_ = 6008_5147_5143 ) ->int:
try:
A =int(a_ )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
A =2
A =0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
A =i
while n % i == 0:
A =n // i
i += 1
return int(a_ )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 689 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowercase__ = None
lowercase__ = logging.get_logger(__name__)
lowercase__ = """▁"""
lowercase__ = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
lowercase__ = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
lowercase__ = {
"""google/pegasus-xsum""": 512,
}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = PegasusTokenizer
lowerCamelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self , lowercase=None , lowercase=None , lowercase="<pad>" , lowercase="</s>" , lowercase="<unk>" , lowercase="<mask_2>" , lowercase="<mask_1>" , lowercase=None , lowercase=103 , **lowercase , ):
_lowerCamelCase : List[Any] = offset
if additional_special_tokens is not None:
if not isinstance(lowercase , lowercase ):
raise TypeError(
F'''additional_special_tokens should be of type {type(lowercase )}, but is'''
F''' {type(lowercase )}''' )
_lowerCamelCase : Tuple = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F'''<unk_{i}>''' for i in range(len(lowercase ) , self.offset - 1 )
]
if len(set(lowercase ) ) != len(lowercase ):
raise ValueError(
'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
_lowerCamelCase : List[Any] = additional_special_tokens_extended
else:
_lowerCamelCase : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F'''<unk_{i}>''' for i in range(2 , self.offset )]
super().__init__(
lowercase , tokenizer_file=lowercase , pad_token=lowercase , eos_token=lowercase , unk_token=lowercase , mask_token=lowercase , mask_token_sent=lowercase , offset=lowercase , additional_special_tokens=lowercase , **lowercase , )
_lowerCamelCase : str = vocab_file
_lowerCamelCase : Optional[Any] = False if not self.vocab_file else True
def A_ ( self , lowercase ):
_lowerCamelCase : Dict = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def A_ ( self , lowercase , lowercase = None , lowercase = False ):
if already_has_special_tokens:
return self._special_token_mask(lowercase )
elif token_ids_a is None:
return self._special_token_mask(lowercase ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def A_ ( self , lowercase , lowercase=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def A_ ( self , lowercase , lowercase = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(lowercase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_lowerCamelCase : Optional[int] = os.path.join(
lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ):
copyfile(self.vocab_file , lowercase )
return (out_vocab_file,) | 630 |
"""simple docstring"""
import operator as op
def _snake_case ( lowercase__ ):
_lowerCamelCase : Dict = []
_lowerCamelCase : List[str] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
_lowerCamelCase : Optional[int] = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' )
print('-' * (30 + len(lowercase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
else:
_lowerCamelCase : int = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
_lowerCamelCase : Tuple = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
stack.append(
str(opr[x](int(lowercase__ ) , int(lowercase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
lowercase__ = input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
print("""\n\tResult = """, solve(Postfix)) | 630 | 1 |
'''simple docstring'''
import cmath
import math
def lowerCAmelCase ( UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Tuple ):
"""simple docstring"""
__UpperCAmelCase = math.radians(SCREAMING_SNAKE_CASE_ )
__UpperCAmelCase = math.radians(SCREAMING_SNAKE_CASE_ )
# Convert voltage and current to rectangular form
__UpperCAmelCase = cmath.rect(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__UpperCAmelCase = cmath.rect(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703 | '''simple docstring'''
def lowerCAmelCase ( UpperCamelCase__ : int ):
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
__UpperCAmelCase = f"""Input value of [number={number}] must be an integer"""
raise TypeError(UpperCamelCase__ )
if number < 1:
__UpperCAmelCase = f"""Input value of [number={number}] must be > 0"""
raise ValueError(UpperCamelCase__ )
__UpperCAmelCase = 1
for i in range(1 , UpperCamelCase__ ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 654 | 0 |
def UpperCAmelCase__ ( __magic_name__ : Dict , __magic_name__ : Tuple ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = 0
lowerCAmelCase : Any = len(__magic_name__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
lowerCAmelCase : Union[str, Any] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__magic_name__ ):
return None
lowerCAmelCase : Union[str, Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
lowerCAmelCase : int = left
lowerCAmelCase : Any = point
elif point > right:
lowerCAmelCase : Optional[int] = right
lowerCAmelCase : List[Any] = point
else:
if item < current_item:
lowerCAmelCase : List[Any] = point - 1
else:
lowerCAmelCase : int = point + 1
return None
def UpperCAmelCase__ ( __magic_name__ : Dict , __magic_name__ : Optional[Any] , __magic_name__ : List[Any] , __magic_name__ : Optional[int] ):
'''simple docstring'''
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
lowerCAmelCase : Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__magic_name__ ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
elif point > right:
return interpolation_search_by_recursion(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
__magic_name__ , __magic_name__ , __magic_name__ , point - 1 )
else:
return interpolation_search_by_recursion(
__magic_name__ , __magic_name__ , point + 1 , __magic_name__ )
def UpperCAmelCase__ ( __magic_name__ : Dict ):
'''simple docstring'''
if collection != sorted(__magic_name__ ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = 0
if debug == 1:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
__SCREAMING_SNAKE_CASE : Any = 67
__SCREAMING_SNAKE_CASE : List[Any] = interpolation_search(collection, target)
if result is not None:
print(f"""{target} found at positions: {result}""")
else:
print('Not found')
| 348 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : str = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[Any] = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Any = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : int = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 348 | 1 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__snake_case = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
year={2021}
}
'''
__snake_case = '''\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.
'''
__snake_case = r'''
Calculates accuracy after canonicalizing inputs.
Args:
predictions: list of predictions to score. Each prediction
is a string that contains natural language and LaTex.
references: list of reference for each prediction. Each
reference is a string that contains natural language
and LaTex.
Returns:
accuracy: accuracy after canonicalizing inputs
(e.g., converting "1/2" to "\\frac{1}{2}")
Examples:
>>> metric = datasets.load_metric("competition_math")
>>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])
>>> print(results)
{\'accuracy\': 1.0}
'''
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCamelCase ( datasets.Metric ):
def _UpperCAmelCase ( self ) -> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ),
'''references''': datasets.Value('''string''' ),
} ) , homepage='''https://github.com/hendrycks/math''' , codebase_urls=['''https://github.com/hendrycks/math'''] , )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> int:
_a = 0.0
for i, j in zip(__UpperCAmelCase , __UpperCAmelCase ):
n_correct += 1.0 if math_equivalence.is_equiv(__UpperCAmelCase , __UpperCAmelCase ) else 0.0
_a = n_correct / len(__UpperCAmelCase )
return {
"accuracy": accuracy,
} | 710 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : Any = ['pixel_values']
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BILINEAR , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> None:
super().__init__(**__UpperCAmelCase )
_a = size if size is not None else {'''shortest_edge''': 384}
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
_a = do_resize
_a = size
# Default value set here for backwards compatibility where the value in config is None
_a = crop_pct if crop_pct is not None else 224 / 256
_a = resample
_a = do_rescale
_a = rescale_factor
_a = do_normalize
_a = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_a = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
_a = size['''shortest_edge''']
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
_a = int(shortest_edge / crop_pct )
_a = get_resize_output_image_size(__UpperCAmelCase , size=__UpperCAmelCase , default_to_square=__UpperCAmelCase )
_a = resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=__UpperCAmelCase , size=(shortest_edge, shortest_edge) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
__UpperCAmelCase , size=(shortest_edge, shortest_edge) , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> Optional[int]:
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ) -> PIL.Image.Image:
_a = do_resize if do_resize is not None else self.do_resize
_a = crop_pct if crop_pct is not None else self.crop_pct
_a = resample if resample is not None else self.resample
_a = do_rescale if do_rescale is not None else self.do_rescale
_a = rescale_factor if rescale_factor is not None else self.rescale_factor
_a = do_normalize if do_normalize is not None else self.do_normalize
_a = image_mean if image_mean is not None else self.image_mean
_a = image_std if image_std is not None else self.image_std
_a = size if size is not None else self.size
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
_a = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError('''crop_pct must be specified if size < 384.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_a = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
_a = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , crop_pct=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_rescale:
_a = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
_a = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
_a = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
_a = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase ) | 285 | 0 |
import math
import os
import sys
def UpperCamelCase ( __magic_name__ : str ) -> str:
"""simple docstring"""
lowercase__ = """"""
try:
with open(__magic_name__ , """rb""" ) as binary_file:
lowercase__ = binary_file.read()
for dat in data:
lowercase__ = f'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print("""File not accessible""" )
sys.exit()
def UpperCamelCase ( __magic_name__ : dict[str, str] , __magic_name__ : str , __magic_name__ : int , __magic_name__ : str ) -> None:
"""simple docstring"""
lexicon.pop(__magic_name__ )
lowercase__ = last_match_id
if math.loga(__magic_name__ ).is_integer():
for curr_key in lexicon:
lowercase__ = """0""" + lexicon[curr_key]
lowercase__ = bin(__magic_name__ )[2:]
def UpperCamelCase ( __magic_name__ : str ) -> str:
"""simple docstring"""
lowercase__ = {"""0""": """0""", """1""": """1"""}
lowercase__ , lowercase__ = """""", """"""
lowercase__ = len(__magic_name__ )
for i in range(len(__magic_name__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
lowercase__ = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
index += 1
lowercase__ = """"""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
lowercase__ = lexicon[curr_string]
result += last_match_id
return result
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : str ) -> str:
"""simple docstring"""
lowercase__ = os.path.getsize(__magic_name__ )
lowercase__ = bin(__magic_name__ )[2:]
lowercase__ = len(__magic_name__ )
return "0" * (length_length - 1) + file_length_binary + compressed
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : str ) -> None:
"""simple docstring"""
lowercase__ = 8
try:
with open(__magic_name__ , """wb""" ) as opened_file:
lowercase__ = [
to_write[i : i + byte_length]
for i in range(0 , len(__magic_name__ ) , __magic_name__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("""10000000""" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(__magic_name__ , 2 ).to_bytes(1 , byteorder="""big""" ) )
except OSError:
print("""File not accessible""" )
sys.exit()
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : str ) -> None:
"""simple docstring"""
lowercase__ = read_file_binary(__magic_name__ )
lowercase__ = compress_data(__magic_name__ )
lowercase__ = add_file_length(__magic_name__ , __magic_name__ )
write_file_binary(__magic_name__ , __magic_name__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 15 |
def SCREAMING_SNAKE_CASE ( snake_case , snake_case = False ) -> str:
if not isinstance(snake_case , snake_case ):
__lowercase = F"Expected string as input, found {type(snake_case )}"
raise ValueError(snake_case )
if not isinstance(snake_case , snake_case ):
__lowercase = F"Expected boolean as use_pascal parameter, found {type(snake_case )}"
raise ValueError(snake_case )
__lowercase = input_str.split('_' )
__lowercase = 0 if use_pascal else 1
__lowercase = words[start_index:]
__lowercase = [word[0].upper() + word[1:] for word in words_to_capitalize]
__lowercase = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 375 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase__ : Tuple = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
'''
lowercase__ : Optional[Any] = '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''
lowercase__ : str = '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for \'record\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'prediction_text\': the predicted answer text
- for \'multirc\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question-answer pair as specified by the dataset
- \'prediction\': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for \'record\': list of question-answers dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'answers\': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for \'record\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1\': F1 score
- for \'multirc\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1_m\': Per-question macro-F1 score
- \'f1_a\': Average F1 score over all answers
- for \'axb\':
\'matthews_correlation\': Matthew Correlation
- for \'cb\':
- \'accuracy\': Accuracy
- \'f1\': F1 score
- for all others:
- \'accuracy\': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
>>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
>>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
>>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def __lowercase ( _a , _a ):
return float((preds == labels).mean() )
def __lowercase ( _a , _a , _a="binary" ):
snake_case_ : List[Any] = simple_accuracy(_a , _a )
snake_case_ : List[str] = float(fa_score(y_true=_a , y_pred=_a , average=_a ) )
return {
"accuracy": acc,
"f1": fa,
}
def __lowercase ( _a , _a ):
snake_case_ : Any = {}
for id_pred, label in zip(_a , _a ):
snake_case_ : Union[str, Any] = f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
snake_case_ : int = id_pred['''prediction''']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
snake_case_ : List[str] = [(pred, label)]
snake_case_, snake_case_ : str = [], []
for question, preds_labels in question_map.items():
snake_case_, snake_case_ : List[Any] = zip(*_a )
snake_case_ : List[Any] = fa_score(y_true=_a , y_pred=_a , average='''macro''' )
fas.append(_a )
snake_case_ : int = int(sum(pred == label for pred, label in preds_labels ) == len(_a ) )
ems.append(_a )
snake_case_ : Union[str, Any] = float(sum(_a ) / len(_a ) )
snake_case_ : str = sum(_a ) / len(_a )
snake_case_ : Union[str, Any] = float(fa_score(y_true=_a , y_pred=[id_pred['''prediction'''] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _UpperCAmelCase ( datasets.Metric):
def _snake_case ( self : List[Any] ):
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]''' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None , )
def _snake_case ( self : Optional[Any] ):
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('''int64''' ),
"query": datasets.Value('''int64''' ),
},
"prediction_text": datasets.Value('''string''' ),
},
"references": {
"idx": {
"passage": datasets.Value('''int64''' ),
"query": datasets.Value('''int64''' ),
},
"answers": datasets.Sequence(datasets.Value('''string''' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('''int64''' ),
"paragraph": datasets.Value('''int64''' ),
"question": datasets.Value('''int64''' ),
},
"prediction": datasets.Value('''int64''' ),
},
"references": datasets.Value('''int64''' ),
}
else:
return {
"predictions": datasets.Value('''int64''' ),
"references": datasets.Value('''int64''' ),
}
def _snake_case ( self : Optional[int] , lowercase_ : Dict , lowercase_ : Any ):
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowercase_ , lowercase_ )}
elif self.config_name == "cb":
return acc_and_fa(lowercase_ , lowercase_ , fa_avg='''macro''' )
elif self.config_name == "record":
snake_case_ : Tuple = [
{
'''qas''': [
{'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
for ref in references
]
}
]
snake_case_ : Union[str, Any] = {pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
return evaluate_record(lowercase_ , lowercase_ )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowercase_ , lowercase_ )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowercase_ , lowercase_ )}
else:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]''' )
| 485 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int ):
snake_case_ : Any = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase_ , '''embed_dim''' ) )
self.parent.assertTrue(hasattr(lowercase_ , '''num_heads''' ) )
class _UpperCAmelCase :
def __init__( self : List[str] , lowercase_ : Optional[Any] , lowercase_ : int=13 , lowercase_ : Optional[int]=64 , lowercase_ : Any=3 , lowercase_ : Any=[16, 48, 96] , lowercase_ : List[Any]=[1, 3, 6] , lowercase_ : Union[str, Any]=[1, 2, 10] , lowercase_ : Optional[Any]=[7, 3, 3] , lowercase_ : Union[str, Any]=[4, 2, 2] , lowercase_ : Tuple=[2, 1, 1] , lowercase_ : List[str]=[2, 2, 2] , lowercase_ : Union[str, Any]=[False, False, True] , lowercase_ : Optional[int]=[0.0, 0.0, 0.0] , lowercase_ : str=0.02 , lowercase_ : Optional[Any]=1E-12 , lowercase_ : Optional[int]=True , lowercase_ : Optional[int]=True , lowercase_ : Optional[Any]=2 , ):
snake_case_ : List[Any] = parent
snake_case_ : int = batch_size
snake_case_ : Union[str, Any] = image_size
snake_case_ : Tuple = patch_sizes
snake_case_ : List[Any] = patch_stride
snake_case_ : Dict = patch_padding
snake_case_ : Any = is_training
snake_case_ : Any = use_labels
snake_case_ : str = num_labels
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = embed_dim
snake_case_ : int = num_heads
snake_case_ : List[str] = stride_kv
snake_case_ : Any = depth
snake_case_ : Dict = cls_token
snake_case_ : Dict = attention_drop_rate
snake_case_ : int = initializer_range
snake_case_ : Tuple = layer_norm_eps
def _snake_case ( self : Dict ):
snake_case_ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : str = None
if self.use_labels:
# create a random int32 tensor of given shape
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ : Dict = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : int ):
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def _snake_case ( self : int , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
snake_case_ : Tuple = TFCvtModel(config=lowercase_ )
snake_case_ : Tuple = model(lowercase_ , training=lowercase_ )
snake_case_ : int = (self.image_size, self.image_size)
snake_case_, snake_case_ : List[str] = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
snake_case_ : str = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
snake_case_ : str = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def _snake_case ( self : Dict , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : List[str] ):
snake_case_ : int = self.num_labels
snake_case_ : Any = TFCvtForImageClassification(lowercase_ )
snake_case_ : List[Any] = model(lowercase_ , labels=lowercase_ , training=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : Any ):
snake_case_ : Tuple = self.prepare_config_and_inputs()
snake_case_, snake_case_, snake_case_ : List[str] = config_and_inputs
snake_case_ : Any = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : Optional[int] = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
_lowerCAmelCase : str = (
{"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
if is_tf_available()
else {}
)
_lowerCAmelCase : str = False
_lowerCAmelCase : int = False
_lowerCAmelCase : Tuple = False
_lowerCAmelCase : int = False
_lowerCAmelCase : int = False
def _snake_case ( self : int ):
snake_case_ : Optional[int] = TFCvtModelTester(self )
snake_case_ : str = TFCvtConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37 )
def _snake_case ( self : int ):
self.config_tester.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()
@unittest.skip(reason='''Cvt does not output attentions''' )
def _snake_case ( self : Any ):
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def _snake_case ( self : str ):
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def _snake_case ( self : Union[str, Any] ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
def _snake_case ( self : Tuple ):
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def _snake_case ( self : Tuple ):
super().test_keras_fit()
@unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8''' )
def _snake_case ( self : List[str] ):
snake_case_ : Optional[Any] = tf.keras.mixed_precision.Policy('''mixed_float16''' )
tf.keras.mixed_precision.set_global_policy(lowercase_ )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy('''float32''' )
def _snake_case ( self : int ):
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_ : Dict = model_class(lowercase_ )
snake_case_ : str = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : List[str] = [*signature.parameters.keys()]
snake_case_ : int = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase_ )
def _snake_case ( self : List[str] ):
def check_hidden_states_output(lowercase_ : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : str ):
snake_case_ : Any = model_class(lowercase_ )
snake_case_ : Optional[Any] = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
snake_case_ : Tuple = outputs.hidden_states
snake_case_ : str = len(self.model_tester.depth )
self.assertEqual(len(lowercase_ ) , lowercase_ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
snake_case_, snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Dict = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ : Tuple = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def _snake_case ( self : str ):
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def _snake_case ( self : List[Any] ):
snake_case_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def _snake_case ( self : Optional[Any] ):
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : int = TFCvtModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def __lowercase ( ):
snake_case_ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _UpperCAmelCase ( unittest.TestCase):
@cached_property
def _snake_case ( self : List[Any] ):
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _snake_case ( self : Tuple ):
snake_case_ : int = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
snake_case_ : Any = self.default_image_processor
snake_case_ : Union[str, Any] = prepare_img()
snake_case_ : int = image_processor(images=lowercase_ , return_tensors='''tf''' )
# forward pass
snake_case_ : Tuple = model(**lowercase_ )
# verify the logits
snake_case_ : Optional[Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , lowercase_ )
snake_case_ : Tuple = tf.constant([0.92_85, 0.90_15, -0.31_50] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowercase_ , atol=1E-4 ) )
| 485 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a_ = abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __UpperCAmelCase ( __UpperCamelCase ):
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCamelCase )
def __UpperCAmelCase ( __UpperCamelCase ):
from diffusers.utils.testing_utils import pytest_terminal_summary_main
__lowercase : str = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__UpperCamelCase , id=__UpperCamelCase )
| 76 |
"""simple docstring"""
from collections import deque
class _a :
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
lowerCamelCase__ = process_name # process name
lowerCamelCase__ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
lowerCamelCase__ = arrival_time
lowerCamelCase__ = burst_time # remaining burst time
lowerCamelCase__ = 0 # total time of the process wait in ready queue
lowerCamelCase__ = 0 # time from arrival time to completion time
class _a :
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : deque[Process] , SCREAMING_SNAKE_CASE__ : int , ):
# total number of mlfq's queues
lowerCamelCase__ = number_of_queues
# time slice of queues that round robin algorithm applied
lowerCamelCase__ = time_slices
# unfinished process is in this ready_queue
lowerCamelCase__ = queue
# current time
lowerCamelCase__ = current_time
# finished process is in this sequence queue
lowerCamelCase__ = deque()
def _UpperCamelCase ( self : List[str] ):
lowerCamelCase__ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def _UpperCamelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : list[Process] ):
lowerCamelCase__ = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def _UpperCamelCase ( self : Dict , SCREAMING_SNAKE_CASE__ : list[Process] ):
lowerCamelCase__ = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def _UpperCamelCase ( self : Any , SCREAMING_SNAKE_CASE__ : list[Process] ):
lowerCamelCase__ = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def _UpperCamelCase ( self : Any , SCREAMING_SNAKE_CASE__ : deque[Process] ):
return [q.burst_time for q in queue]
def _UpperCamelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Process ):
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def _UpperCamelCase ( self : Dict , SCREAMING_SNAKE_CASE__ : deque[Process] ):
lowerCamelCase__ = deque() # sequence deque of finished process
while len(SCREAMING_SNAKE_CASE__ ) != 0:
lowerCamelCase__ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(SCREAMING_SNAKE_CASE__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
lowerCamelCase__ = 0
# set the process's turnaround time because it is finished
lowerCamelCase__ = self.current_time - cp.arrival_time
# set the completion time
lowerCamelCase__ = self.current_time
# add the process to queue that has finished queue
finished.append(SCREAMING_SNAKE_CASE__ )
self.finish_queue.extend(SCREAMING_SNAKE_CASE__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def _UpperCamelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : deque[Process] , SCREAMING_SNAKE_CASE__ : int ):
lowerCamelCase__ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
lowerCamelCase__ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(SCREAMING_SNAKE_CASE__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
lowerCamelCase__ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(SCREAMING_SNAKE_CASE__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
lowerCamelCase__ = 0
# set the finish time
lowerCamelCase__ = self.current_time
# update the process' turnaround time because it is finished
lowerCamelCase__ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(SCREAMING_SNAKE_CASE__ )
self.finish_queue.extend(SCREAMING_SNAKE_CASE__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def _UpperCamelCase ( self : Dict ):
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
lowerCamelCase__ , lowerCamelCase__ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
_snake_case = Process("P1", 0, 53)
_snake_case = Process("P2", 0, 17)
_snake_case = Process("P3", 0, 68)
_snake_case = Process("P4", 0, 24)
_snake_case = 3
_snake_case = [17, 25]
_snake_case = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
_snake_case = Process("P1", 0, 53)
_snake_case = Process("P2", 0, 17)
_snake_case = Process("P3", 0, 68)
_snake_case = Process("P4", 0, 24)
_snake_case = 3
_snake_case = [17, 25]
_snake_case = deque([Pa, Pa, Pa, Pa])
_snake_case = MLFQ(number_of_queues, time_slices, queue, 0)
_snake_case = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"""waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"""
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"""completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"""
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"""turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"""
)
# print sequence of finished processes
print(
f"""sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"""
)
| 510 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
lowercase_ = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Optional[int] = 'ernie_m'
lowercase : Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __A = 25_0002 , __A = 768 , __A = 12 , __A = 12 , __A = 3072 , __A = "gelu" , __A = 0.1 , __A = 0.1 , __A = 514 , __A = 0.02 , __A = 1 , __A = 1E-05 , __A=None , __A=False , __A=0.0 , **__A , ) -> Optional[Any]:
super().__init__(pad_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_act
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =classifier_dropout
_lowerCAmelCase =is_decoder
_lowerCAmelCase =act_dropout
| 708 | '''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')
# TF training parameters
lowercase_ = False
lowercase_ = False
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return TrainCommand(a__ )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@staticmethod
def UpperCamelCase__ ( __A ) -> Tuple:
_lowerCAmelCase =parser.add_parser('train' , help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' , type=__A , required=__A , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , )
train_parser.add_argument(
'--column_label' , type=__A , default=0 , help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' , type=__A , default=1 , help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' , type=__A , default=2 , help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' , action='store_true' , help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' , type=__A , default='' , help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' , type=__A , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , )
train_parser.add_argument('--output' , type=__A , default='./' , help='path to saved the trained model.' )
train_parser.add_argument(
'--task' , type=__A , default='text_classification' , help='Task to train the model on.' )
train_parser.add_argument(
'--model' , type=__A , default='bert-base-uncased' , help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' , type=__A , default=32 , help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' , type=__A , default=64 , help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' , type=__A , default=3E-5 , help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' , type=__A , default=1E-08 , help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=__A )
def __init__( self , __A ) -> List[str]:
_lowerCAmelCase =logging.get_logger('transformers-cli/training' )
_lowerCAmelCase ='tf' if is_tf_available() else 'torch'
os.makedirs(args.output , exist_ok=__A )
_lowerCAmelCase =args.output
_lowerCAmelCase =args.column_label
_lowerCAmelCase =args.column_text
_lowerCAmelCase =args.column_id
self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
if args.task == "text_classification":
_lowerCAmelCase =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'''Loading dataset from {args.train_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =None
if args.validation_data:
self.logger.info(F'''Loading validation dataset from {args.validation_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =args.validation_split
_lowerCAmelCase =args.train_batch_size
_lowerCAmelCase =args.valid_batch_size
_lowerCAmelCase =args.learning_rate
_lowerCAmelCase =args.adam_epsilon
def UpperCamelCase__ ( self ) -> List[str]:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
raise NotImplementedError
def UpperCamelCase__ ( self ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 58 | 0 |
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> list:
if len(snake_case__ ) < 2:
return collection
def circle_sort_util(snake_case__ , snake_case__ , snake_case__ ) -> bool:
lowerCAmelCase = False
if low == high:
return swapped
lowerCAmelCase = low
lowerCAmelCase = high
while left < right:
if collection[left] > collection[right]:
lowerCAmelCase , lowerCAmelCase = (
collection[right],
collection[left],
)
lowerCAmelCase = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
lowerCAmelCase , lowerCAmelCase = (
collection[right + 1],
collection[left],
)
lowerCAmelCase = True
lowerCAmelCase = low + int((high - low) / 2 )
lowerCAmelCase = circle_sort_util(snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase = circle_sort_util(snake_case__ , mid + 1 , snake_case__ )
return swapped or left_swap or right_swap
lowerCAmelCase = True
while is_not_sorted is True:
lowerCAmelCase = circle_sort_util(snake_case__ , 0 , len(snake_case__ ) - 1 )
return collection
if __name__ == "__main__":
lowercase__ : List[str] = input('''Enter numbers separated by a comma:\n''').strip()
lowercase__ : List[str] = [int(item) for item in user_input.split(''',''')]
print(circle_sort(unsorted))
| 312 | import os
lowercase__ : List[str] = {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> int:
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(snake_case__ ) - 1:
lowerCAmelCase = SYMBOLS[numerals[index]]
lowerCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> str:
lowerCAmelCase = ''''''
lowerCAmelCase = num // 1_0_0_0
numerals += m_count * "M"
num %= 1_0_0_0
lowerCAmelCase = num // 1_0_0
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_0_0
lowerCAmelCase = num // 1_0
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 1_0
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def SCREAMING_SNAKE_CASE_ ( snake_case__ = "/p089_roman.txt" ) -> int:
lowerCAmelCase = 0
with open(os.path.dirname(snake_case__ ) + roman_numerals_filename ) as filea:
lowerCAmelCase = filea.readlines()
for line in lines:
lowerCAmelCase = line.strip()
lowerCAmelCase = parse_roman_numerals(snake_case__ )
lowerCAmelCase = generate_roman_numerals(snake_case__ )
savings += len(snake_case__ ) - len(snake_case__ )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 312 | 1 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
lowercase = logging.getLogger(__name__)
lowercase = '''Hello world! cécé herlolip'''
lowercase = namedtuple(
'''BertAbsConfig''',
[
'''temp_dir''',
'''large''',
'''use_bert_emb''',
'''finetune_bert''',
'''encoder''',
'''share_emb''',
'''max_pos''',
'''enc_layers''',
'''enc_hidden_size''',
'''enc_heads''',
'''enc_ff_size''',
'''enc_dropout''',
'''dec_layers''',
'''dec_hidden_size''',
'''dec_heads''',
'''dec_ff_size''',
'''dec_dropout''',
],
)
def UpperCAmelCase_ ( lowercase__ , lowercase__ ):
'''simple docstring'''
a_ =BertAbsConfig(
temp_dir="." , finetune_bert=lowercase__ , large=lowercase__ , share_emb=lowercase__ , use_bert_emb=lowercase__ , encoder="bert" , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
a_ =torch.load(lowercase__ , lambda lowercase__ , lowercase__ : storage )
a_ =AbsSummarizer(lowercase__ , torch.device("cpu" ) , lowercase__ )
original.eval()
a_ =BertAbsSummarizer(lowercase__ , torch.device("cpu" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical" )
a_ =BertTokenizer.from_pretrained("bert-base-uncased" )
# prepare the model inputs
a_ =tokenizer.encode("This is sample éàalj'-." )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(lowercase__ )) )
a_ =torch.tensor(lowercase__ ).unsqueeze(0 )
a_ =tokenizer.encode("This is sample 3 éàalj'-." )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(lowercase__ )) )
a_ =torch.tensor(lowercase__ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
a_ =encoder_input_ids
a_ =decoder_input_ids
a_ =a_ =None
a_ =None
a_ =a_ =None
a_ =a_ =None
a_ =None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
a_ =original(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )[0]
a_ =original.generator(lowercase__ )
a_ =new_model(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )[0]
a_ =new_model.generator(lowercase__ )
a_ =torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("Maximum absolute difference beween weights: {:.2f}".format(lowercase__ ) )
a_ =torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("Maximum absolute difference beween weights: {:.2f}".format(lowercase__ ) )
a_ =torch.allclose(lowercase__ , lowercase__ , atol=1E-3 )
if are_identical:
logging.info("all weights are equal up to 1e-3" )
else:
raise ValueError("the weights are different. The new model is likely different from the original one." )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary" )
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin" )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument(
'''--bertabs_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch dump.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
lowercase = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 41 |
'''simple docstring'''
from collections.abc import Generator
def UpperCAmelCase_ ( ):
'''simple docstring'''
a_ , a_ =0, 1
while True:
a_ , a_ =b, a + b
yield b
def UpperCAmelCase_ ( lowercase__ = 1_0_0_0 ):
'''simple docstring'''
a_ =1
a_ =fibonacci_generator()
while len(str(next(lowercase__ ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 41 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__ = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
},
}
UpperCamelCase__ = {
'''facebook/bart-base''': 1_0_2_4,
'''facebook/bart-large''': 1_0_2_4,
'''facebook/bart-large-mnli''': 1_0_2_4,
'''facebook/bart-large-cnn''': 1_0_2_4,
'''facebook/bart-large-xsum''': 1_0_2_4,
'''yjernite/bart_eli5''': 1_0_2_4,
}
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ['input_ids', 'attention_mask']
lowerCAmelCase__ = BartTokenizer
def __init__( self : Tuple , _A : List[str]=None , _A : Optional[Any]=None , _A : Union[str, Any]=None , _A : Tuple="replace" , _A : Optional[Any]="<s>" , _A : int="</s>" , _A : Optional[Any]="</s>" , _A : List[str]="<s>" , _A : Optional[int]="<unk>" , _A : Optional[int]="<pad>" , _A : str="<mask>" , _A : Dict=False , _A : int=True , **_A : Optional[Any] , ):
'''simple docstring'''
super().__init__(
_A , _A , tokenizer_file=_A , errors=_A , bos_token=_A , eos_token=_A , sep_token=_A , cls_token=_A , unk_token=_A , pad_token=_A , mask_token=_A , add_prefix_space=_A , trim_offsets=_A , **_A , )
UpperCAmelCase__ : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , _A ) != add_prefix_space:
UpperCAmelCase__ : str = getattr(_A , pre_tok_state.pop('''type''' ) )
UpperCAmelCase__ : Any = add_prefix_space
UpperCAmelCase__ : str = pre_tok_class(**_A )
UpperCAmelCase__ : Dict = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
UpperCAmelCase__ : Optional[Any] = '''post_processor'''
UpperCAmelCase__ : List[Any] = getattr(self.backend_tokenizer , _A , _A )
if tokenizer_component_instance:
UpperCAmelCase__ : Tuple = 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:
UpperCAmelCase__ : Union[str, Any] = tuple(state['''sep'''] )
if "cls" in state:
UpperCAmelCase__ : Union[str, Any] = tuple(state['''cls'''] )
UpperCAmelCase__ : Dict = False
if state.get('''add_prefix_space''' , _A ) != add_prefix_space:
UpperCAmelCase__ : Union[str, Any] = add_prefix_space
UpperCAmelCase__ : Dict = True
if state.get('''trim_offsets''' , _A ) != trim_offsets:
UpperCAmelCase__ : List[Any] = trim_offsets
UpperCAmelCase__ : List[Any] = True
if changes_to_apply:
UpperCAmelCase__ : Dict = getattr(_A , state.pop('''type''' ) )
UpperCAmelCase__ : Union[str, Any] = component_class(**_A )
setattr(self.backend_tokenizer , _A , _A )
@property
def lowercase_ ( self : Dict ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def lowercase_ ( self : Dict , _A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else value
UpperCAmelCase__ : str = value
def lowercase_ ( self : Optional[int] , *_A : List[str] , **_A : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Any = kwargs.get('''is_split_into_words''' , _A )
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(*_A , **_A )
def lowercase_ ( self : Optional[Any] , *_A : Union[str, Any] , **_A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = kwargs.get('''is_split_into_words''' , _A )
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(*_A , **_A )
def lowercase_ ( self : Optional[int] , _A : str , _A : Optional[str] = None ):
'''simple docstring'''
UpperCAmelCase__ : str = self._tokenizer.model.save(_A , name=_A )
return tuple(_A )
def lowercase_ ( self : Tuple , _A : Union[str, Any] , _A : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase__ : Any = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase_ ( self : int , _A : List[int] , _A : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = [self.sep_token_id]
UpperCAmelCase__ : 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]
| 75 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__A = Mapping[str, np.ndarray]
__A = Mapping[str, Any] # Is a nested dict.
__A = 0.01
@dataclasses.dataclass(frozen=snake_case )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = 42 # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
A_ = 42 # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
A_ = 42 # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
A_ = 42 # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
A_ = 42 # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
A_ = None
# Optional remark about the protein. Included as a comment in output PDB
# files
A_ = None
# Templates used to generate this protein (prediction-only)
A_ = None
# Chain corresponding to each parent
A_ = None
def __A ( _lowercase ):
'''simple docstring'''
_A = R'''(\[[A-Z]+\]\n)'''
_A = [tag.strip() for tag in re.split(_lowercase , _lowercase ) if len(_lowercase ) > 0]
_A = zip(tags[0::2] , [l.split('''\n''' ) for l in tags[1::2]] )
_A = ["N", "CA", "C"]
_A = None
_A = None
_A = None
for g in groups:
if "[PRIMARY]" == g[0]:
_A = g[1][0].strip()
for i in range(len(_lowercase ) ):
if seq[i] not in residue_constants.restypes:
_A = '''X''' # FIXME: strings are immutable
_A = np.array(
[residue_constants.restype_order.get(_lowercase , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
_A = []
for axis in range(3 ):
tertiary.append(list(map(_lowercase , g[1][axis].split() ) ) )
_A = np.array(_lowercase )
_A = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_lowercase ):
_A = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
_A = np.array(list(map({'''-''': 0, '''+''': 1}.get , g[1][0].strip() ) ) )
_A = np.zeros(
(
len(_lowercase ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_lowercase ):
_A = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_lowercase , atom_mask=_lowercase , aatype=_lowercase , residue_index=np.arange(len(_lowercase ) ) , b_factors=_lowercase , )
def __A ( _lowercase , _lowercase = 0 ):
'''simple docstring'''
_A = []
_A = prot.remark
if remark is not None:
pdb_headers.append(f"""REMARK {remark}""" )
_A = prot.parents
_A = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
_A = [p for i, p in zip(_lowercase , _lowercase ) if i == chain_id]
if parents is None or len(_lowercase ) == 0:
_A = ['''N/A''']
pdb_headers.append(f"""PARENT {" ".join(_lowercase )}""" )
return pdb_headers
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = []
_A = pdb_str.split('''\n''' )
_A = prot.remark
if remark is not None:
out_pdb_lines.append(f"""REMARK {remark}""" )
_A = 42
if prot.parents is not None and len(prot.parents ) > 0:
_A = []
if prot.parents_chain_index is not None:
_A = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_lowercase ) , [] )
parent_dict[str(_lowercase )].append(_lowercase )
_A = max([int(_lowercase ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
_A = parent_dict.get(str(_lowercase ) , ['''N/A'''] )
parents_per_chain.append(_lowercase )
else:
parents_per_chain.append(list(prot.parents ) )
else:
_A = [['''N/A''']]
def make_parent_line(_lowercase ) -> str:
return f"""PARENT {" ".join(_lowercase )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
_A = 0
for i, l in enumerate(_lowercase ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_lowercase )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_lowercase ):
_A = parents_per_chain[chain_counter]
else:
_A = ['''N/A''']
out_pdb_lines.append(make_parent_line(_lowercase ) )
return "\n".join(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
_A = residue_constants.restypes + ['''X''']
def res_atoa(_lowercase ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , '''UNK''' )
_A = residue_constants.atom_types
_A = []
_A = prot.atom_mask
_A = prot.aatype
_A = prot.atom_positions
_A = prot.residue_index.astype(np.intaa )
_A = prot.b_factors
_A = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError('''Invalid aatypes.''' )
_A = get_pdb_headers(_lowercase )
if len(_lowercase ) > 0:
pdb_lines.extend(_lowercase )
_A = aatype.shape[0]
_A = 1
_A = 0
_A = string.ascii_uppercase
_A = None
# Add all atom sites.
for i in range(_lowercase ):
_A = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_lowercase , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
_A = '''ATOM'''
_A = atom_name if len(_lowercase ) == 4 else f""" {atom_name}"""
_A = ''''''
_A = ''''''
_A = 1.00
_A = atom_name[0] # Protein supports only C, N, O, S, this works.
_A = ''''''
_A = '''A'''
if chain_index is not None:
_A = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
_A = (
f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
f"""{res_name_a:>3} {chain_tag:>1}"""
f"""{residue_index[i]:>4}{insertion_code:>1} """
f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
f"""{occupancy:>6.2f}{b_factor:>6.2f} """
f"""{element:>2}{charge:>2}"""
)
pdb_lines.append(_lowercase )
atom_index += 1
_A = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
_A = True
_A = chain_index[i + 1]
if should_terminate:
# Close the chain.
_A = '''TER'''
_A = (
f"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(_lowercase )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_lowercase , _lowercase ) )
pdb_lines.append('''END''' )
pdb_lines.append('''''' )
return "\n".join(_lowercase )
def __A ( _lowercase ):
'''simple docstring'''
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def __A ( _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , ):
'''simple docstring'''
return Protein(
aatype=features['''aatype'''] , atom_positions=result['''final_atom_positions'''] , atom_mask=result['''final_atom_mask'''] , residue_index=features['''residue_index'''] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result['''final_atom_mask'''] ) , chain_index=_lowercase , remark=_lowercase , parents=_lowercase , parents_chain_index=_lowercase , )
| 484 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str , __A : int , __A : Tuple=13 , __A : Union[str, Any]=30 , __A : Tuple=2 , __A : Dict=3 , __A : Optional[Any]=True , __A : Union[str, Any]=True , __A : Dict=32 , __A : Union[str, Any]=5 , __A : List[str]=4 , __A : Dict=37 , __A : List[Any]="gelu" , __A : Optional[int]=0.1 , __A : Optional[int]=0.1 , __A : Dict=10 , __A : Optional[Any]=0.0_2 , ) -> Dict:
'''simple docstring'''
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCAmelCase__ = (image_size // patch_size) ** 2
lowerCAmelCase__ = num_patches + 1
def lowercase__ ( self : str ) -> int:
'''simple docstring'''
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__A , initializer_range=self.initializer_range , )
return config, pixel_values
def lowercase__ ( self : Tuple , __A : Any , __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = FlaxViTModel(config=__A )
lowerCAmelCase__ = model(__A )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
lowerCAmelCase__ = (self.image_size, self.image_size)
lowerCAmelCase__ = (self.patch_size, self.patch_size)
lowerCAmelCase__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def lowercase__ ( self : str , __A : Optional[int] , __A : List[str] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = self.type_sequence_label_size
lowerCAmelCase__ = FlaxViTForImageClassification(config=__A )
lowerCAmelCase__ = model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = FlaxViTForImageClassification(__A )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(__A )
def lowercase__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = self.prepare_config_and_inputs()
(
(
lowerCAmelCase__
) ,(
lowerCAmelCase__
) ,
) = config_and_inputs
lowerCAmelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class lowerCamelCase__ ( _A, unittest.TestCase ):
'''simple docstring'''
A__ = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def lowercase__ ( self : Dict ) -> None:
'''simple docstring'''
lowerCAmelCase__ = FlaxViTModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=__A , has_text_modality=__A , hidden_size=37 )
def lowercase__ ( self : Optional[int] ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase__ ( self : int ) -> Tuple:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowercase__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
def lowercase__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ ,lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(__A )
lowerCAmelCase__ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __A )
def lowercase__ ( self : int ) -> Tuple:
'''simple docstring'''
lowerCAmelCase__ ,lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCAmelCase__ = self._prepare_for_class(__A , __A )
lowerCAmelCase__ = model_class(__A )
@jax.jit
def model_jitted(__A : Tuple , **__A : List[Any] ):
return model(pixel_values=__A , **__A )
with self.subTest("""JIT Enabled""" ):
lowerCAmelCase__ = model_jitted(**__A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
lowerCAmelCase__ = model_jitted(**__A ).to_tuple()
self.assertEqual(len(__A ) , len(__A ) )
for jitted_output, output in zip(__A , __A ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def lowercase__ ( self : Any ) -> Tuple:
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowerCAmelCase__ = model_class_name.from_pretrained("""google/vit-base-patch16-224""" )
lowerCAmelCase__ = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(__A )
| 709 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[str] , __A : Tuple , __A : Union[str, Any]=7 , __A : Any=3 , __A : Dict=18 , __A : Dict=30 , __A : Dict=400 , __A : Dict=True , __A : Union[str, Any]=32 , __A : List[Any]=True , ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = image_size
lowerCAmelCase__ = min_resolution
lowerCAmelCase__ = max_resolution
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size_divisor
lowerCAmelCase__ = do_rescale
def lowercase__ ( self : int ) -> List[Any]:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class lowerCamelCase__ ( _A, unittest.TestCase ):
'''simple docstring'''
A__ = GLPNImageProcessor if is_vision_available() else None
def lowercase__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = GLPNImageProcessingTester(self )
@property
def lowercase__ ( self : int ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase__ ( self : List[str] ) -> int:
'''simple docstring'''
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , """do_resize""" ) )
self.assertTrue(hasattr(__A , """size_divisor""" ) )
self.assertTrue(hasattr(__A , """resample""" ) )
self.assertTrue(hasattr(__A , """do_rescale""" ) )
def lowercase__ ( self : Dict ) -> List[str]:
'''simple docstring'''
pass
def lowercase__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def lowercase__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , numpify=__A )
for image in image_inputs:
self.assertIsInstance(__A , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def lowercase__ ( self : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , torchify=__A )
for image in image_inputs:
self.assertIsInstance(__A , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 211 | 0 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( a_ :Tuple , a_ :List[Any] , a_ :Optional[int]) -> Optional[int]:
return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def __A ( a_ :int , a_ :List[Any] , a_ :Tuple , a_ :Any="attention") -> List[str]:
__a : Union[str, Any] = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :])
__a : Union[str, Any] = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2])
__a : str = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :])
__a : Optional[int] = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2])
__a : int = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :])
__a : List[str] = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2])
__a : Dict = np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :])
__a : List[str] = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2])
return k, o, q, v
def __A ( a_ :int , a_ :List[str] , a_ :int , a_ :Union[str, Any]=False) -> List[str]:
if split_mlp_wi:
__a : List[str] = params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
__a : Tuple = params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
__a : Any = (wi_a, wi_a)
else:
__a : Any = params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]
__a : int = params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
return wi, wo
def __A ( a_ :List[Any] , a_ :Optional[int] , a_ :Optional[int] , a_ :List[str]) -> Any:
return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def __A ( a_ :List[Any] , *, a_ :Optional[int] , a_ :Any , a_ :List[Any] = False) -> List[Any]:
__a : List[Any] = traverse_util.flatten_dict(variables['''target'''])
__a : Union[str, Any] = {'/'.join(UpperCAmelCase__): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
__a : Union[str, Any] = 'encoder/encoder/mlp/wi_0/kernel' in old
print('''Split MLP:''' , UpperCAmelCase__)
__a : Optional[Any] = collections.OrderedDict()
# Shared embeddings.
__a : Dict = old['token_embedder/embedding']
# Encoder.
for i in range(UpperCAmelCase__):
# Block i, layer 0 (Self Attention).
__a : Optional[Any] = tax_layer_norm_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''encoder''' , '''pre_attention_layer_norm''')
__a : Dict = tax_attention_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''encoder''' , '''attention''')
__a : Any = layer_norm
__a : Tuple = k.T
__a : int = o.T
__a : Optional[int] = q.T
__a : str = v.T
# Block i, layer 1 (MLP).
__a : int = tax_layer_norm_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''encoder''' , '''pre_mlp_layer_norm''')
__a : Dict = tax_mlp_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''encoder''' , UpperCAmelCase__)
__a : int = layer_norm
if split_mlp_wi:
__a : Optional[int] = wi[0].T
__a : Dict = wi[1].T
else:
__a : Optional[Any] = wi.T
__a : int = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
__a : int = tax_relpos_bias_lookup(
UpperCAmelCase__ , UpperCAmelCase__ , '''encoder''').T
__a : int = old['encoder/encoder_norm/scale']
if not scalable_attention:
__a : int = tax_relpos_bias_lookup(
UpperCAmelCase__ , 0 , '''encoder''').T
__a : List[str] = tax_relpos_bias_lookup(
UpperCAmelCase__ , 0 , '''decoder''').T
if not is_encoder_only:
# Decoder.
for i in range(UpperCAmelCase__):
# Block i, layer 0 (Self Attention).
__a : Dict = tax_layer_norm_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''decoder''' , '''pre_self_attention_layer_norm''')
__a : Any = tax_attention_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''decoder''' , '''self_attention''')
__a : str = layer_norm
__a : Tuple = k.T
__a : List[Any] = o.T
__a : Optional[Any] = q.T
__a : Optional[Any] = v.T
# Block i, layer 1 (Cross Attention).
__a : Tuple = tax_layer_norm_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''decoder''' , '''pre_cross_attention_layer_norm''')
__a : Optional[Any] = tax_attention_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''decoder''' , '''encoder_decoder_attention''')
__a : Optional[Any] = layer_norm
__a : Union[str, Any] = k.T
__a : Dict = o.T
__a : Dict = q.T
__a : Any = v.T
# Block i, layer 2 (MLP).
__a : Union[str, Any] = tax_layer_norm_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''decoder''' , '''pre_mlp_layer_norm''')
__a : Optional[Any] = tax_mlp_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''decoder''' , UpperCAmelCase__)
__a : Dict = layer_norm
if split_mlp_wi:
__a : Tuple = wi[0].T
__a : List[str] = wi[1].T
else:
__a : Any = wi.T
__a : Tuple = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
__a : int = tax_relpos_bias_lookup(UpperCAmelCase__ , UpperCAmelCase__ , '''decoder''').T
__a : Any = old['decoder/decoder_norm/scale']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
__a : Union[str, Any] = old['decoder/logits_dense/kernel'].T
return new
def __A ( a_ :List[Any] , a_ :List[Any]) -> int:
__a : Union[str, Any] = collections.OrderedDict([(k, torch.from_numpy(v.copy())) for (k, v) in converted_params.items()])
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
__a : str = state_dict['shared.weight']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
__a : List[Any] = state_dict['shared.weight']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''')
__a : str = state_dict['shared.weight']
return state_dict
def __A ( a_ :List[Any] , a_ :Tuple , a_ :Union[str, Any] , a_ :List[str] , a_ :Optional[Any]) -> Optional[Any]:
__a : Any = checkpoints.load_tax_checkpoint(UpperCAmelCase__)
__a : int = convert_tax_to_pytorch(
UpperCAmelCase__ , num_layers=config.num_layers , is_encoder_only=UpperCAmelCase__ , scalable_attention=UpperCAmelCase__)
__a : Tuple = make_state_dict(UpperCAmelCase__ , UpperCAmelCase__)
model.load_state_dict(UpperCAmelCase__ , strict=UpperCAmelCase__)
def __A ( a_ :Union[str, Any] , a_ :Any , a_ :str , a_ :Tuple = False , a_ :Any = False , ) -> Any:
__a : Any = MTaConfig.from_json_file(UpperCAmelCase__)
print(F"""Building PyTorch model from configuration: {config}""")
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
__a : Optional[Any] = UMTaEncoderModel(UpperCAmelCase__)
else:
__a : Dict = UMTaForConditionalGeneration(UpperCAmelCase__)
# Load weights from tf checkpoint
load_tax_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""")
model.save_pretrained(UpperCAmelCase__)
# Verify that we can load the checkpoint.
model.from_pretrained(UpperCAmelCase__)
print('''Done''')
if __name__ == "__main__":
A = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
parser.add_argument(
'''--scalable_attention''',
action='''store_true''',
help='''Whether the model uses scaled attention (umt5 model)''',
default=False,
)
A = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
) | 52 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : int = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : int = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 57 | 0 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = ''''''
lowerCamelCase__ = '''hf-legacy''' # "hf://"" is reserved for hffs
def __init__( self , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , **__SCREAMING_SNAKE_CASE , ):
super().__init__(self , **__SCREAMING_SNAKE_CASE )
snake_case__ : Union[str, Any] = repo_info
snake_case__ : Dict = token
snake_case__ : Optional[int] = None
def __UpperCamelCase ( self ):
if self.dir_cache is None:
snake_case__ : str = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
snake_case__ : Union[str, Any] = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(__SCREAMING_SNAKE_CASE ): {"""name""": str(__SCREAMING_SNAKE_CASE ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = "rb" , **__SCREAMING_SNAKE_CASE , ):
if not isinstance(self.repo_info , __SCREAMING_SNAKE_CASE ):
raise NotImplementedError(f"Open is only implemented for dataset repositories, but got {self.repo_info}" )
snake_case__ : Dict = hf_hub_url(self.repo_info.id , __SCREAMING_SNAKE_CASE , revision=self.repo_info.sha )
return fsspec.open(
__SCREAMING_SNAKE_CASE , mode=__SCREAMING_SNAKE_CASE , headers=get_authentication_headers_for_url(__SCREAMING_SNAKE_CASE , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open()
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
self._get_dirs()
snake_case__ : str = self._strip_protocol(__SCREAMING_SNAKE_CASE )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False , **__SCREAMING_SNAKE_CASE ):
self._get_dirs()
snake_case__ : str = PurePosixPath(path.strip("""/""" ) )
snake_case__ : int = {}
for p, f in self.dir_cache.items():
snake_case__ : Optional[int] = PurePosixPath(p.strip("""/""" ) )
snake_case__ : int = p.parent
if root == path:
snake_case__ : List[str] = f
snake_case__ : str = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 419 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : str ) -> str:
'''simple docstring'''
snake_case__ : int = len(__magic_name__ )
snake_case__ : int = len(__magic_name__ )
snake_case__ : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
snake_case__ : list = []
for char_count in range(__magic_name__ ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(__magic_name__ )
if __name__ == "__main__":
print(alternative_string_arrange("AB", "XYZ"), end=" ")
| 419 | 1 |
'''simple docstring'''
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
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"""vocab_file""": """spm_char.model"""}
UpperCamelCase_ = {
"""vocab_file""": {
"""microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
"""microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
"""microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
}
}
UpperCamelCase_ = {
"""microsoft/speecht5_asr""": 10_24,
"""microsoft/speecht5_tts""": 10_24,
"""microsoft/speecht5_vc""": 10_24,
}
class a_ (_a ):
__lowerCAmelCase : str = VOCAB_FILES_NAMES
__lowerCAmelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : Optional[Any] = ['input_ids', 'attention_mask']
def __init__( self , snake_case_ , snake_case_="<s>" , snake_case_="</s>" , snake_case_="<unk>" , snake_case_="<pad>" , snake_case_ = None , **snake_case_ , ):
_lowerCAmelCase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
_lowerCAmelCase : Optional[int] = vocab_file
_lowerCAmelCase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
@property
def __UpperCamelCase ( self ):
return self.sp_model.get_piece_size()
def __UpperCamelCase ( self ):
_lowerCAmelCase : List[Any] = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
_lowerCAmelCase : Any = self.__dict__.copy()
_lowerCAmelCase : str = None
return state
def __setstate__( self , snake_case_ ):
_lowerCAmelCase : Any = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_lowerCAmelCase : Dict = {}
_lowerCAmelCase : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __UpperCamelCase ( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def __UpperCamelCase ( self , snake_case_ ):
return self.sp_model.piece_to_id(snake_case_ )
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : List[Any] = self.sp_model.IdToPiece(snake_case_ )
return token
def __UpperCamelCase ( self , snake_case_ ):
_lowerCAmelCase : Union[str, Any] = []
_lowerCAmelCase : Tuple = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_ ) + token
_lowerCAmelCase : Union[str, Any] = []
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def __UpperCamelCase ( self , snake_case_ , snake_case_=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def __UpperCamelCase ( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
_lowerCAmelCase : Optional[Any] = [1]
if token_ids_a is None:
return ([0] * len(snake_case_ )) + suffix_ones
return ([0] * len(snake_case_ )) + ([0] * len(snake_case_ )) + suffix_ones
def __UpperCamelCase ( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCAmelCase : List[Any] = os.path.join(
snake_case_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , """wb""" ) as fi:
_lowerCAmelCase : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
| 384 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
lowercase_ = {
"""vocab_file""": {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"""
),
},
}
lowercase_ = {
"""yjernite/retribert-base-uncased""": 512,
}
lowercase_ = {
"""yjernite/retribert-base-uncased""": {"""do_lower_case""": True},
}
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Tuple = VOCAB_FILES_NAMES
_UpperCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : Tuple = PRETRAINED_INIT_CONFIGURATION
_UpperCamelCase : Optional[Any] = RetriBertTokenizer
_UpperCamelCase : Dict = ['input_ids', 'attention_mask']
def __init__( self : str , a : Any=None , a : Optional[Any]=None , a : Dict=True , a : Union[str, Any]="[UNK]" , a : int="[SEP]" , a : Union[str, Any]="[PAD]" , a : str="[CLS]" , a : List[Any]="[MASK]" , a : Dict=True , a : Optional[Any]=None , **a : Any , )-> Optional[int]:
"""simple docstring"""
super().__init__(
a , tokenizer_file=a , do_lower_case=a , unk_token=a , sep_token=a , pad_token=a , cls_token=a , mask_token=a , tokenize_chinese_chars=a , strip_accents=a , **a , )
lowercase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , a ) != do_lower_case
or normalizer_state.get('strip_accents' , a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , a ) != tokenize_chinese_chars
):
lowercase__ = getattr(a , normalizer_state.pop('type' ) )
lowercase__ = do_lower_case
lowercase__ = strip_accents
lowercase__ = tokenize_chinese_chars
lowercase__ = normalizer_class(**a )
lowercase__ = do_lower_case
def SCREAMING_SNAKE_CASE_ ( self : Dict , a : List[Any] , a : int=None )-> Optional[Any]:
"""simple docstring"""
lowercase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def SCREAMING_SNAKE_CASE_ ( self : List[str] , a : List[int] , a : Optional[List[int]] = None )-> List[int]:
"""simple docstring"""
lowercase__ = [self.sep_token_id]
lowercase__ = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE_ ( self : Any , a : str , a : Optional[str] = None )-> Tuple[str]:
"""simple docstring"""
lowercase__ = self._tokenizer.model.save(a , name=a )
return tuple(a )
| 235 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowercase ( unittest.TestCase ):
@slow
def __snake_case( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small" )
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("google/mt5-small" )
SCREAMING_SNAKE_CASE = tokenizer("Hello there" , return_tensors="np" ).input_ids
SCREAMING_SNAKE_CASE = tokenizer("Hi I am" , return_tensors="np" ).input_ids
SCREAMING_SNAKE_CASE = shift_tokens_right(_UpperCamelCase , model.config.pad_token_id , model.config.decoder_start_token_id )
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , decoder_input_ids=_UpperCamelCase ).logits
SCREAMING_SNAKE_CASE = optax.softmax_cross_entropy(_UpperCamelCase , onehot(_UpperCamelCase , logits.shape[-1] ) ).mean()
SCREAMING_SNAKE_CASE = -(labels.shape[-1] * loss.item())
SCREAMING_SNAKE_CASE = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 714 | from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] ):
return [
int(1_0_0_0 * (box[0] / width) ),
int(1_0_0_0 * (box[1] / height) ),
int(1_0_0_0 * (box[2] / width) ),
int(1_0_0_0 * (box[3] / height) ),
]
def __lowerCamelCase (UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : Optional[str] , UpperCAmelCase__ : Optional[str] = None ):
SCREAMING_SNAKE_CASE = tesseract_config if tesseract_config is not None else ""
# apply OCR
SCREAMING_SNAKE_CASE = to_pil_image(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = pil_image.size
SCREAMING_SNAKE_CASE = pytesseract.image_to_data(UpperCAmelCase__ , lang=UpperCAmelCase__ , output_type="dict" , config=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = data["text"], data["left"], data["top"], data["width"], data["height"]
# filter empty words and corresponding coordinates
SCREAMING_SNAKE_CASE = [idx for idx, word in enumerate(UpperCAmelCase__ ) if not word.strip()]
SCREAMING_SNAKE_CASE = [word for idx, word in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE = [coord for idx, coord in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE = [coord for idx, coord in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE = [coord for idx, coord in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE = [coord for idx, coord in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
SCREAMING_SNAKE_CASE = []
for x, y, w, h in zip(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE = [x, y, x + w, y + h]
actual_boxes.append(UpperCAmelCase__ )
# finally, normalize the bounding boxes
SCREAMING_SNAKE_CASE = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) )
assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class lowercase ( a ):
lowercase__ : Optional[int] = ["""pixel_values"""]
def __init__( self : int , _UpperCamelCase : bool = True , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCamelCase : bool = True , _UpperCamelCase : Optional[str] = None , _UpperCamelCase : Optional[str] = "" , **_UpperCamelCase : Optional[int] , ) -> None:
'''simple docstring'''
super().__init__(**_UpperCamelCase )
SCREAMING_SNAKE_CASE = size if size is not None else {"height": 224, "width": 224}
SCREAMING_SNAKE_CASE = get_size_dict(_UpperCamelCase )
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = resample
SCREAMING_SNAKE_CASE = apply_ocr
SCREAMING_SNAKE_CASE = ocr_lang
SCREAMING_SNAKE_CASE = tesseract_config
def __snake_case( self : List[Any] , _UpperCamelCase : np.ndarray , _UpperCamelCase : Dict[str, int] , _UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCamelCase : Any , ) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_size_dict(_UpperCamelCase )
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()}" )
SCREAMING_SNAKE_CASE = (size["height"], size["width"])
return resize(_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def __snake_case( self : Tuple , _UpperCamelCase : ImageInput , _UpperCamelCase : bool = None , _UpperCamelCase : Dict[str, int] = None , _UpperCamelCase : PILImageResampling = None , _UpperCamelCase : bool = None , _UpperCamelCase : Optional[str] = None , _UpperCamelCase : Optional[str] = None , _UpperCamelCase : Optional[Union[str, TensorType]] = None , _UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCamelCase : str , ) -> PIL.Image.Image:
'''simple docstring'''
SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE = size if size is not None else self.size
SCREAMING_SNAKE_CASE = get_size_dict(_UpperCamelCase )
SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE = apply_ocr if apply_ocr is not None else self.apply_ocr
SCREAMING_SNAKE_CASE = ocr_lang if ocr_lang is not None else self.ocr_lang
SCREAMING_SNAKE_CASE = tesseract_config if tesseract_config is not None else self.tesseract_config
SCREAMING_SNAKE_CASE = make_list_of_images(_UpperCamelCase )
if not valid_images(_UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE = [to_numpy_array(_UpperCamelCase ) for image in images]
if apply_ocr:
requires_backends(self , "pytesseract" )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for image in images:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = apply_tesseract(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
words_batch.append(_UpperCamelCase )
boxes_batch.append(_UpperCamelCase )
if do_resize:
SCREAMING_SNAKE_CASE = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
SCREAMING_SNAKE_CASE = [flip_channel_order(_UpperCamelCase ) for image in images]
SCREAMING_SNAKE_CASE = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
SCREAMING_SNAKE_CASE = BatchFeature(data={"pixel_values": images} , tensor_type=_UpperCamelCase )
if apply_ocr:
SCREAMING_SNAKE_CASE = words_batch
SCREAMING_SNAKE_CASE = boxes_batch
return data
| 647 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
SCREAMING_SNAKE_CASE = get_tests_dir('fixtures')
SCREAMING_SNAKE_CASE = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
SCREAMING_SNAKE_CASE = get_tests_dir('fixtures/dummy-config.json')
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self ):
__a = 0
def snake_case_ ( self ):
__a = AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(__A , __A )
def snake_case_ ( self ):
__a = AutoFeatureExtractor.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def snake_case_ ( self ):
with tempfile.TemporaryDirectory() as tmpdirname:
__a = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
__a = AutoFeatureExtractor.from_pretrained(__A ).to_dict()
config_dict.pop("""feature_extractor_type""" )
__a = WavaVecaFeatureExtractor(**__A )
# save in new folder
model_config.save_pretrained(__A )
config.save_pretrained(__A )
__a = AutoFeatureExtractor.from_pretrained(__A )
# make sure private variable is not incorrectly saved
__a = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(__A , __A )
def snake_case_ ( self ):
__a = AutoFeatureExtractor.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def snake_case_ ( self ):
with self.assertRaisesRegex(
__A , """bert-base is not a local folder and is not a valid model identifier""" ):
__a = AutoFeatureExtractor.from_pretrained("""bert-base""" )
def snake_case_ ( self ):
with self.assertRaisesRegex(
__A , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
__a = AutoFeatureExtractor.from_pretrained(__A , revision="""aaaaaa""" )
def snake_case_ ( self ):
with self.assertRaisesRegex(
__A , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
__a = AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model""" )
def snake_case_ ( self ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__A ):
__a = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__A ):
__a = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
__a = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(__A )
__a = AutoFeatureExtractor.from_pretrained(__A , trust_remote_code=__A )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
def snake_case_ ( self ):
try:
AutoConfig.register("""custom""" , __A )
AutoFeatureExtractor.register(__A , __A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__A ):
AutoFeatureExtractor.register(__A , __A )
# Now that the config is registered, it can be used as any other config with the auto-API
__a = CustomFeatureExtractor.from_pretrained(__A )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(__A )
__a = AutoFeatureExtractor.from_pretrained(__A )
self.assertIsInstance(__A , __A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def snake_case_ ( self ):
class __UpperCAmelCase ( __A ):
"""simple docstring"""
_lowerCamelCase = True
try:
AutoConfig.register("""custom""" , __A )
AutoFeatureExtractor.register(__A , __A )
# If remote code is not set, the default is to use local
__a = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
__a = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
__a = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(not hasattr(__A , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 99 |
'''simple docstring'''
def lowerCamelCase__ ( __lowerCamelCase : str = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =set()
# Replace all the whitespace in our sentence
_UpperCAmelCase : Dict =input_str.replace(' ' , '' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(__lowerCamelCase ) == 2_6
def lowerCamelCase__ ( __lowerCamelCase : str = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
_UpperCAmelCase : Tuple =[False] * 2_6
for char in input_str:
if char.islower():
_UpperCAmelCase : Dict =True
elif char.isupper():
_UpperCAmelCase : List[str] =True
return all(__lowerCamelCase )
def lowerCamelCase__ ( __lowerCamelCase : str = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
return len({char for char in input_str.lower() if char.isalpha()} ) == 2_6
def lowerCamelCase__ ( ):
'''simple docstring'''
from timeit import timeit
_UpperCAmelCase : List[Any] ='from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
print(timeit('is_pangram()' , setup=__lowerCamelCase ) )
print(timeit('is_pangram_faster()' , setup=__lowerCamelCase ) )
print(timeit('is_pangram_fastest()' , setup=__lowerCamelCase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 446 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( snake_case_ : list[int] , snake_case_ : list[int] , snake_case_ : list[int] , snake_case_ : list[list[str]] , snake_case_ : int , ) -> None:
SCREAMING_SNAKE_CASE : int = len(SCREAMING_SNAKE_CASE_ )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(SCREAMING_SNAKE_CASE_ ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> None:
SCREAMING_SNAKE_CASE : list[list[str]] = []
depth_first_search([] , [] , [] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Print all the boards
for board in boards:
for column in board:
print(SCREAMING_SNAKE_CASE_ )
print('' )
print(len(SCREAMING_SNAKE_CASE_ ) , 'solutions were found.' )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 702 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def SCREAMING_SNAKE_CASE_ ( snake_case_ : np.ndarray ) -> np.ndarray:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def SCREAMING_SNAKE_CASE_ ( snake_case_ : np.ndarray ) -> np.ndarray:
return (gray > 127) & (gray <= 255)
def SCREAMING_SNAKE_CASE_ ( snake_case_ : np.ndarray , snake_case_ : np.ndarray ) -> np.ndarray:
SCREAMING_SNAKE_CASE : List[Any] = np.zeros_like(snake_case_ )
SCREAMING_SNAKE_CASE : Tuple = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
SCREAMING_SNAKE_CASE : Optional[int] = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
SCREAMING_SNAKE_CASE : Optional[int] = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
SCREAMING_SNAKE_CASE : int = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
__UpperCAmelCase = Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
__UpperCAmelCase = np.array(Image.open(lena_path))
# kernel to be applied
__UpperCAmelCase = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
__UpperCAmelCase = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
__UpperCAmelCase = Image.fromarray(output).convert('RGB')
pil_img.save('result_dilation.png')
| 220 | 0 |
import math
import qiskit
def a_ ( lowerCAmelCase_ : int = 1, lowerCAmelCase_ : int = 1, lowerCAmelCase_ : int = 1 ):
if (
isinstance(lowerCAmelCase_, lowerCAmelCase_ )
or isinstance(lowerCAmelCase_, lowerCAmelCase_ )
or isinstance(lowerCAmelCase_, lowerCAmelCase_ )
):
raise TypeError('inputs must be integers.' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('inputs must be positive.' )
if (
(math.floor(lowerCAmelCase_ ) != input_a)
or (math.floor(lowerCAmelCase_ ) != input_a)
or (math.floor(lowerCAmelCase_ ) != carry_in)
):
raise ValueError('inputs must be exact integers.' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('inputs must be less or equal to 2.' )
# build registers
__lowerCAmelCase = qiskit.QuantumRegister(4, 'qr' )
__lowerCAmelCase = qiskit.ClassicalRegister(2, 'cr' )
# list the entries
__lowerCAmelCase = [input_a, input_a, carry_in]
__lowerCAmelCase = qiskit.QuantumCircuit(lowerCAmelCase_, lowerCAmelCase_ )
for i in range(0, 3 ):
if entry[i] == 2:
quantum_circuit.h(lowerCAmelCase_ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(lowerCAmelCase_ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(lowerCAmelCase_ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0, 1, 3 ) # ccx = toffoli gate
quantum_circuit.cx(0, 1 )
quantum_circuit.ccx(1, 2, 3 )
quantum_circuit.cx(1, 2 )
quantum_circuit.cx(0, 1 )
quantum_circuit.measure([2, 3], lowerCAmelCase_ ) # measure the last two qbits
__lowerCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
__lowerCAmelCase = qiskit.execute(lowerCAmelCase_, lowerCAmelCase_, shots=1000 )
return job.result().get_counts(lowerCAmelCase_ )
if __name__ == "__main__":
print(F"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")
| 53 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=UpperCamelCase__ , default="""biencoder-nq-dev.json""" , help="""Path to raw DPR training data""" , )
parser.add_argument(
"""--evaluation_set""" , type=UpperCamelCase__ , help="""where to store parsed evaluation_set file""" , )
parser.add_argument(
"""--gold_data_path""" , type=UpperCamelCase__ , help="""where to store parsed gold_data_path file""" , )
_a : Optional[int] = parser.parse_args()
with open(args.src_path , """r""" ) as src_file, open(args.evaluation_set , """w""" ) as eval_file, open(
args.gold_data_path , """w""" ) as gold_file:
_a : Dict = json.load(UpperCamelCase__ )
for dpr_record in tqdm(UpperCamelCase__ ):
_a : int = dpr_record["""question"""]
_a : Dict = [context["""title"""] for context in dpr_record["""positive_ctxs"""]]
eval_file.write(question + """\n""" )
gold_file.write("""\t""".join(UpperCamelCase__ ) + """\n""" )
if __name__ == "__main__":
main()
| 389 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = "deta"
a = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : str , __lowerCamelCase : int=None , __lowerCamelCase : List[str]=900 , __lowerCamelCase : int=2048 , __lowerCamelCase : Optional[int]=6 , __lowerCamelCase : Optional[int]=2048 , __lowerCamelCase : Optional[int]=8 , __lowerCamelCase : str=6 , __lowerCamelCase : int=1024 , __lowerCamelCase : Any=8 , __lowerCamelCase : Tuple=0.0 , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Any="relu" , __lowerCamelCase : int=256 , __lowerCamelCase : List[Any]=0.1 , __lowerCamelCase : List[str]=0.0 , __lowerCamelCase : List[str]=0.0 , __lowerCamelCase : int=0.02 , __lowerCamelCase : Union[str, Any]=1.0 , __lowerCamelCase : Dict=True , __lowerCamelCase : List[Any]=False , __lowerCamelCase : List[Any]="sine" , __lowerCamelCase : Union[str, Any]=5 , __lowerCamelCase : Optional[Any]=4 , __lowerCamelCase : int=4 , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Optional[Any]=300 , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Dict=True , __lowerCamelCase : List[str]=1 , __lowerCamelCase : Optional[Any]=5 , __lowerCamelCase : str=2 , __lowerCamelCase : Tuple=1 , __lowerCamelCase : List[str]=1 , __lowerCamelCase : Dict=5 , __lowerCamelCase : int=2 , __lowerCamelCase : List[str]=0.1 , __lowerCamelCase : List[str]=0.25 , **__lowerCamelCase : Any , ) -> List[Any]:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
SCREAMING_SNAKE_CASE__ = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(__lowerCamelCase , __lowerCamelCase ):
SCREAMING_SNAKE_CASE__ = backbone_config.pop('''model_type''' )
SCREAMING_SNAKE_CASE__ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE__ = config_class.from_dict(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = backbone_config
SCREAMING_SNAKE_CASE__ = num_queries
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = d_model
SCREAMING_SNAKE_CASE__ = encoder_ffn_dim
SCREAMING_SNAKE_CASE__ = encoder_layers
SCREAMING_SNAKE_CASE__ = encoder_attention_heads
SCREAMING_SNAKE_CASE__ = decoder_ffn_dim
SCREAMING_SNAKE_CASE__ = decoder_layers
SCREAMING_SNAKE_CASE__ = decoder_attention_heads
SCREAMING_SNAKE_CASE__ = dropout
SCREAMING_SNAKE_CASE__ = attention_dropout
SCREAMING_SNAKE_CASE__ = activation_dropout
SCREAMING_SNAKE_CASE__ = activation_function
SCREAMING_SNAKE_CASE__ = init_std
SCREAMING_SNAKE_CASE__ = init_xavier_std
SCREAMING_SNAKE_CASE__ = encoder_layerdrop
SCREAMING_SNAKE_CASE__ = auxiliary_loss
SCREAMING_SNAKE_CASE__ = position_embedding_type
# deformable attributes
SCREAMING_SNAKE_CASE__ = num_feature_levels
SCREAMING_SNAKE_CASE__ = encoder_n_points
SCREAMING_SNAKE_CASE__ = decoder_n_points
SCREAMING_SNAKE_CASE__ = two_stage
SCREAMING_SNAKE_CASE__ = two_stage_num_proposals
SCREAMING_SNAKE_CASE__ = with_box_refine
SCREAMING_SNAKE_CASE__ = 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
SCREAMING_SNAKE_CASE__ = class_cost
SCREAMING_SNAKE_CASE__ = bbox_cost
SCREAMING_SNAKE_CASE__ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE__ = mask_loss_coefficient
SCREAMING_SNAKE_CASE__ = dice_loss_coefficient
SCREAMING_SNAKE_CASE__ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE__ = giou_loss_coefficient
SCREAMING_SNAKE_CASE__ = eos_coefficient
SCREAMING_SNAKE_CASE__ = focal_alpha
super().__init__(is_encoder_decoder=__lowerCamelCase , **__lowerCamelCase )
@property
def lowercase_ ( self : Dict ) -> int:
return self.encoder_attention_heads
@property
def lowercase_ ( self : Optional[int] ) -> int:
return self.d_model
def lowercase_ ( self : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE__ = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE__ = self.__class__.model_type
return output
| 472 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCAmelCase__ ( A__ , unittest.TestCase ):
"""simple docstring"""
a = KandinskyImgaImgPipeline
a = ["prompt", "image_embeds", "negative_image_embeds", "image"]
a = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
]
a = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
a = False
@property
def lowercase_ ( self : str ) -> List[str]:
return 32
@property
def lowercase_ ( self : Optional[int] ) -> int:
return 32
@property
def lowercase_ ( self : Union[str, Any] ) -> int:
return self.time_input_dim
@property
def lowercase_ ( self : List[str] ) -> int:
return self.time_input_dim * 4
@property
def lowercase_ ( self : Union[str, Any] ) -> Any:
return 100
@property
def lowercase_ ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE__ = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def lowercase_ ( self : List[Any] ) -> List[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
SCREAMING_SNAKE_CASE__ = MultilingualCLIP(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = text_encoder.eval()
return text_encoder
@property
def lowercase_ ( self : str ) -> Dict:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE__ = UNetaDConditionModel(**__lowerCamelCase )
return model
@property
def lowercase_ ( self : Dict ) -> Optional[Any]:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowercase_ ( self : Tuple ) -> Optional[int]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = VQModel(**self.dummy_movq_kwargs )
return model
def lowercase_ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = self.dummy_text_encoder
SCREAMING_SNAKE_CASE__ = self.dummy_tokenizer
SCREAMING_SNAKE_CASE__ = self.dummy_unet
SCREAMING_SNAKE_CASE__ = self.dummy_movq
SCREAMING_SNAKE_CASE__ = {
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
SCREAMING_SNAKE_CASE__ = DDIMScheduler(**__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def lowercase_ ( self : List[Any] , __lowerCamelCase : List[str] , __lowerCamelCase : Union[str, Any]=0 ) -> str:
SCREAMING_SNAKE_CASE__ = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__lowerCamelCase )
# create init_image
SCREAMING_SNAKE_CASE__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ = Image.fromarray(np.uinta(__lowerCamelCase ) ).convert('''RGB''' ).resize((256, 256) )
if str(__lowerCamelCase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ = torch.manual_seed(__lowerCamelCase )
else:
SCREAMING_SNAKE_CASE__ = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = {
'''prompt''': '''horse''',
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def lowercase_ ( self : int ) -> Any:
SCREAMING_SNAKE_CASE__ = '''cpu'''
SCREAMING_SNAKE_CASE__ = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ = self.pipeline_class(**__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
SCREAMING_SNAKE_CASE__ = output.images
SCREAMING_SNAKE_CASE__ = pipe(
**self.get_dummy_inputs(__lowerCamelCase ) , return_dict=__lowerCamelCase , )[0]
SCREAMING_SNAKE_CASE__ = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ = np.array(
[0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self : Tuple ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : List[str] ) -> List[str]:
SCREAMING_SNAKE_CASE__ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_img2img_frog.npy''' )
SCREAMING_SNAKE_CASE__ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE__ = '''A red cartoon frog, 4k'''
SCREAMING_SNAKE_CASE__ = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = KandinskyImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ = pipeline.to(__lowerCamelCase )
pipeline.set_progress_bar_config(disable=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = pipe_prior(
__lowerCamelCase , generator=__lowerCamelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE__ = pipeline(
__lowerCamelCase , image=__lowerCamelCase , image_embeds=__lowerCamelCase , negative_image_embeds=__lowerCamelCase , generator=__lowerCamelCase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE__ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowerCamelCase , __lowerCamelCase )
| 472 | 1 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
__snake_case = number_of_bytes // partitions
__snake_case = []
for i in range(_lowerCamelCase ):
__snake_case = i * bytes_per_partition + 1
__snake_case = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 408 | 0 |
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 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=4 , ):
a_ = parent
a_ = batch_size
a_ = seq_length
a_ = is_training
a_ = use_attention_mask
a_ = use_token_type_ids
a_ = use_labels
a_ = vocab_size
a_ = hidden_size
a_ = num_hidden_layers
a_ = num_attention_heads
a_ = intermediate_size
a_ = hidden_act
a_ = hidden_dropout_prob
a_ = attention_probs_dropout_prob
a_ = max_position_embeddings
a_ = type_vocab_size
a_ = type_sequence_label_size
a_ = initializer_range
a_ = num_choices
def __magic_name__ ( self ):
a_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a_ = None
if self.use_attention_mask:
a_ = random_attention_mask([self.batch_size, self.seq_length] )
a_ = None
if self.use_token_type_ids:
a_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a_ = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
a_ = config_and_inputs
a_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
a_ = config_and_inputs
a_ = True
a_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
a_ = 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_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
_lowerCamelCase : Optional[int] = True
_lowerCamelCase : Optional[int] = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self ):
a_ = FlaxBertModelTester(self )
@slow
def __magic_name__ ( self ):
a_ = FlaxBertModel.from_pretrained("""bert-base-cased""" )
a_ = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCamelCase_ ) | 704 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : Tuple , UpperCamelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
a_ = flax_key_tuple[:-1] + ("""weight""",)
a_ = torch.permute(UpperCamelCase , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase ):
# linear layer
a_ = flax_key_tuple[:-1] + ("""weight""",)
a_ = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
a_ = flax_key_tuple[:-1] + ("""weight""",)
return flax_key_tuple, flax_tensor
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : List[Any] , UpperCamelCase : Dict , UpperCamelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
if "metadata" in layer:
a_ = layer.split("""metadata""" )
a_ = """""".join(split_layer[0] )[:-1]
a_ = [tuple(("""metadata""" + split_layer[1]).split("""/""" ) )]
elif "kvstore" in layer:
a_ = layer.split("""kvstore""" )
a_ = """""".join(split_layer[0] )[:-1]
a_ = [tuple(("""kvstore""" + split_layer[1]).split("""/""" ) )]
else:
a_ = layer.split("""/""" )
a_ = """/""".join(split_layer[:-1] )
a_ = (split_layer[-1],)
if "kvstore/path" in layer:
a_ = F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
elif "kvstore/driver" in layer:
a_ = """file"""
else:
a_ = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : str , UpperCamelCase : Any ) -> List[str]:
"""simple docstring"""
a_ = rename_keys(UpperCamelCase )
a_ = {}
for k, v in current_block.items():
a_ = v
a_ = new_current_block
torch.save(UpperCamelCase , UpperCamelCase )
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : Optional[int] , UpperCamelCase : Dict , UpperCamelCase : List[Any] , UpperCamelCase : List[Any] , UpperCamelCase : str = WEIGHTS_NAME ) -> Any:
"""simple docstring"""
a_ = convert_file_size_to_int(UpperCamelCase )
a_ = []
a_ = {}
a_ = 0
a_ = 0
os.makedirs(UpperCamelCase , exist_ok=UpperCamelCase )
with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""" ) as fp:
a_ = serialization.msgpack_restore(fp.read() )["""optimizer"""]["""target"""]
a_ = flatten_dict(UpperCamelCase , sep="""/""" )
a_ = {}
for layer in checkpoint_info.keys():
a_ , a_ , a_ = get_key_and_tensorstore_dict(
UpperCamelCase , UpperCamelCase , UpperCamelCase )
if curr_real_layer_name in all_layers:
a_ = content
else:
a_ = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
a_ = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
a_ = torch.tensor(UpperCamelCase )
a_ = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
a_ , a_ = rename_base_flax_keys(tuple(key.split("""/""" ) ) , UpperCamelCase )
a_ = """/""".join(UpperCamelCase )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
a_ = os.path.join(
UpperCamelCase , weights_name.replace(""".bin""" , F"""-{len(UpperCamelCase )+1:05d}-of-???.bin""" ) )
rename_and_save_block(UpperCamelCase , UpperCamelCase )
sharded_state_dicts.append(current_block.keys() )
del current_block
a_ = {}
a_ = 0
a_ = raw_weights.to(getattr(UpperCamelCase , UpperCamelCase ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
a_ = os.path.join(UpperCamelCase , weights_name.replace(""".bin""" , F"""-{len(UpperCamelCase )+1:05d}-of-???.bin""" ) )
rename_and_save_block(UpperCamelCase , UpperCamelCase )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(UpperCamelCase ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
a_ = {}
a_ = {}
for idx, shard in enumerate(UpperCamelCase ):
a_ = weights_name.replace(
""".bin""" , F"""-{idx+1:05d}-of-{len(UpperCamelCase ):05d}.bin""" ) # len(sharded_state_dicts):05d}
a_ = os.path.join(UpperCamelCase , weights_name.replace(""".bin""" , F"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(UpperCamelCase , os.path.join(UpperCamelCase , UpperCamelCase ) )
a_ = shard
for key in shard:
a_ = shard_file
# Add the metadata
a_ = {"""total_size""": total_size}
a_ = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(UpperCamelCase , UpperCamelCase ) , """w""" , encoding="""utf-8""" ) as f:
a_ = json.dumps(UpperCamelCase , indent=2 , sort_keys=UpperCamelCase ) + """\n"""
f.write(UpperCamelCase )
return metadata, index
if __name__ == "__main__":
_A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--switch_t5x_checkpoint_path',
default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
type=str,
required=False,
help='Path to the output pytorch model.',
)
_A = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def __SCREAMING_SNAKE_CASE ( ) -> List[Any]:
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
a_ = SwitchTransformersConfig.from_pretrained("""google/switch-base-8""" )
config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""" )
a_ = SwitchTransformersForConditionalGeneration.from_pretrained(
"""/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""" )
a_ = TaTokenizer.from_pretrained("""t5-small""" )
a_ = """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""
a_ = tokenizer(UpperCamelCase , return_tensors="""pt""" ).input_ids
a_ = model.generate(UpperCamelCase , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) ) | 403 | 0 |
import os
import sys
import unittest
__UpperCamelCase: int = 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
__UpperCamelCase: int = os.path.join(git_repo_path, """src""", """diffusers""")
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self: Dict ):
lowercase__ : List[Any] = find_backend(' if not is_torch_available():' )
self.assertEqual(lowerCamelCase_, 'torch' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
lowercase__ : List[Any] = find_backend(' if not (is_torch_available() and is_transformers_available()):' )
self.assertEqual(lowerCamelCase_, 'torch_and_transformers' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
lowercase__ : Dict = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):' )
self.assertEqual(lowerCamelCase_, 'torch_and_transformers_and_onnx' )
def snake_case__( self: Tuple ):
lowercase__ : Optional[Any] = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch', lowerCamelCase_ )
self.assertIn('torch_and_transformers', lowerCamelCase_ )
self.assertIn('flax_and_transformers', lowerCamelCase_ )
self.assertIn('torch_and_transformers_and_onnx', lowerCamelCase_ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel', objects['torch'] )
self.assertIn('FlaxUNet2DConditionModel', objects['flax'] )
self.assertIn('StableDiffusionPipeline', objects['torch_and_transformers'] )
self.assertIn('FlaxStableDiffusionPipeline', objects['flax_and_transformers'] )
self.assertIn('LMSDiscreteScheduler', objects['torch_and_scipy'] )
self.assertIn('OnnxStableDiffusionPipeline', objects['torch_and_transformers_and_onnx'] )
def snake_case__( self: Optional[Any] ):
lowercase__ : Union[str, Any] = create_dummy_object('CONSTANT', '\'torch\'' )
self.assertEqual(lowerCamelCase_, '\nCONSTANT = None\n' )
lowercase__ : List[str] = create_dummy_object('function', '\'torch\'' )
self.assertEqual(
lowerCamelCase_, '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
lowercase__ : int = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
lowercase__ : Optional[Any] = create_dummy_object('FakeClass', '\'torch\'' )
self.assertEqual(lowerCamelCase_, lowerCamelCase_ )
def snake_case__( self: Any ):
lowercase__ : Union[str, Any] = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
lowercase__ : Dict = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'], lowerCamelCase_ )
| 266 |
import argparse
import os
import re
__UpperCamelCase: Any = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__UpperCamelCase: Dict = re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__UpperCamelCase: List[str] = re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")
def SCREAMING_SNAKE_CASE__ ( _lowercase : int , _lowercase : bool = False ) -> List[str]:
'''simple docstring'''
with open(_lowercase , 'r' , encoding='utf-8' ) as f:
lowercase__ : Union[str, Any] = f.read()
lowercase__ : Optional[Any] = content.split('\n' )
lowercase__ : Optional[int] = []
lowercase__ : int = 0
while line_idx < len(_lowercase ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowercase__ : Tuple = len(re.search(r'^(\s*)\S' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(' ' * indent + '(' ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowercase__ : Tuple = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowercase__ : Any = line_idx
while not lines[line_idx].startswith(' ' * indent + ')' ):
line_idx += 1
blocks.append('\n'.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowercase__ : List[str] = sorted(_lowercase , key=lambda _lowercase : _re_identifier.search(_lowercase ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(_lowercase , 'w' , encoding='utf-8' ) as f:
f.write('\n'.join(_lowercase ) )
elif "\n".join(_lowercase ) != content:
return True
def SCREAMING_SNAKE_CASE__ ( _lowercase : bool = False ) -> List[Any]:
'''simple docstring'''
lowercase__ : List[Any] = [os.path.join(_lowercase , _lowercase ) for f in os.listdir(_lowercase ) if f.endswith('.py' )]
lowercase__ : str = [sort_auto_mapping(_lowercase , overwrite=_lowercase ) for fname in fnames]
if not overwrite and any(_lowercase ):
lowercase__ : List[Any] = [f for f, d in zip(_lowercase , _lowercase ) if d]
raise ValueError(
f"""The following files have auto mappings that need sorting: {", ".join(_lowercase )}. Run `make style` to fix"""
' this.' )
if __name__ == "__main__":
__UpperCamelCase: Tuple = argparse.ArgumentParser()
parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
__UpperCamelCase: List[Any] = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 266 | 1 |
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def lowerCAmelCase_ ( ) -> None:
"""simple docstring"""
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
print(F"""| 0 | 1 | {nor_gate(0 , 1 )} |""" )
print(F"""| 1 | 0 | {nor_gate(1 , 0 )} |""" )
print(F"""| 1 | 1 | {nor_gate(1 , 1 )} |""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 328 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase = TypeVar('KEY')
__lowerCamelCase = TypeVar('VAL')
@dataclass(frozen=SCREAMING_SNAKE_CASE , slots=SCREAMING_SNAKE_CASE )
class _UpperCamelCase( Generic[KEY, VAL] ):
__A: KEY
__A: VAL
class _UpperCamelCase( _Item ):
def __init__( self : Optional[Any] ):
super().__init__(_lowerCamelCase , _lowerCamelCase )
def __bool__( self : Optional[int] ):
return False
__lowerCamelCase = _DeletedItem()
class _UpperCamelCase( MutableMapping[KEY, VAL] ):
def __init__( self : Optional[Any] , _lowerCamelCase : int = 8 , _lowerCamelCase : float = 0.75 ):
_UpperCAmelCase : List[str] = initial_block_size
_UpperCAmelCase : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
_UpperCAmelCase : Optional[Any] = capacity_factor
_UpperCAmelCase : List[Any] = 0
def a__ ( self : List[Any] , _lowerCamelCase : KEY ):
return hash(_lowerCamelCase ) % len(self._buckets )
def a__ ( self : List[str] , _lowerCamelCase : int ):
return (ind + 1) % len(self._buckets )
def a__ ( self : Any , _lowerCamelCase : int , _lowerCamelCase : KEY , _lowerCamelCase : VAL ):
_UpperCAmelCase : Optional[Any] = self._buckets[ind]
if not stored:
_UpperCAmelCase : Union[str, Any] = _Item(_lowerCamelCase , _lowerCamelCase )
self._len += 1
return True
elif stored.key == key:
_UpperCAmelCase : List[Any] = _Item(_lowerCamelCase , _lowerCamelCase )
return True
else:
return False
def a__ ( self : Any ):
_UpperCAmelCase : Dict = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_lowerCamelCase )
def a__ ( self : Any ):
if len(self._buckets ) <= self._initial_block_size:
return False
_UpperCAmelCase : Tuple = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def a__ ( self : Union[str, Any] , _lowerCamelCase : int ):
_UpperCAmelCase : Dict = self._buckets
_UpperCAmelCase : Union[str, Any] = [None] * new_size
_UpperCAmelCase : Union[str, Any] = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def a__ ( self : Any ):
self._resize(len(self._buckets ) * 2 )
def a__ ( self : int ):
self._resize(len(self._buckets ) // 2 )
def a__ ( self : Any , _lowerCamelCase : KEY ):
_UpperCAmelCase : Any = self._get_bucket_index(_lowerCamelCase )
for _ in range(len(self._buckets ) ):
yield ind
_UpperCAmelCase : Union[str, Any] = self._get_next_ind(_lowerCamelCase )
def a__ ( self : Union[str, Any] , _lowerCamelCase : KEY , _lowerCamelCase : VAL ):
for ind in self._iterate_buckets(_lowerCamelCase ):
if self._try_set(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
break
def __setitem__( self : Optional[int] , _lowerCamelCase : KEY , _lowerCamelCase : VAL ):
if self._is_full():
self._size_up()
self._add_item(_lowerCamelCase , _lowerCamelCase )
def __delitem__( self : int , _lowerCamelCase : KEY ):
for ind in self._iterate_buckets(_lowerCamelCase ):
_UpperCAmelCase : List[str] = self._buckets[ind]
if item is None:
raise KeyError(_lowerCamelCase )
if item is _deleted:
continue
if item.key == key:
_UpperCAmelCase : Union[str, Any] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : str , _lowerCamelCase : KEY ):
for ind in self._iterate_buckets(_lowerCamelCase ):
_UpperCAmelCase : Any = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_lowerCamelCase )
def __len__( self : List[str] ):
return self._len
def __iter__( self : List[str] ):
yield from (item.key for item in self._buckets if item)
def __repr__( self : Tuple ):
_UpperCAmelCase : Optional[int] = " ,".join(
f"""{item.key}: {item.val}""" for item in self._buckets if item )
return f"""HashMap({val_string})"""
| 328 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class UpperCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase_ = "nat"
lowerCamelCase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self :List[Any] , __A :Union[str, Any]=4 , __A :Dict=3 , __A :str=64 , __A :Optional[int]=[3, 4, 6, 5] , __A :Tuple=[2, 4, 8, 16] , __A :List[str]=7 , __A :Optional[Any]=3.0 , __A :Tuple=True , __A :Tuple=0.0 , __A :Dict=0.0 , __A :Tuple=0.1 , __A :str="gelu" , __A :Tuple=0.0_2 , __A :str=1E-5 , __A :Tuple=0.0 , __A :List[str]=None , __A :Optional[Any]=None , **__A :Optional[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(**__A )
SCREAMING_SNAKE_CASE__ = patch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = embed_dim
SCREAMING_SNAKE_CASE__ = depths
SCREAMING_SNAKE_CASE__ = len(__A )
SCREAMING_SNAKE_CASE__ = num_heads
SCREAMING_SNAKE_CASE__ = kernel_size
SCREAMING_SNAKE_CASE__ = mlp_ratio
SCREAMING_SNAKE_CASE__ = qkv_bias
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = drop_path_rate
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE__ = int(embed_dim * 2 ** (len(__A ) - 1) )
SCREAMING_SNAKE_CASE__ = layer_scale_init_value
SCREAMING_SNAKE_CASE__ = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(__A ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_aligned_output_features_output_indices(
out_features=__A , out_indices=__A , stage_names=self.stage_names ) | 6 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( lowerCAmelCase ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = CTRLTokenizer
_UpperCamelCase = False
_UpperCamelCase = False
def __snake_case ( self : str) -> List[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A_ = ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
A_ = dict(zip(_lowercase , range(len(_lowercase))))
A_ = ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
A_ = {'unk_token': '<unk>'}
A_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
A_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'])
with open(self.vocab_file , 'w' , encoding='utf-8') as fp:
fp.write(json.dumps(_lowercase) + '\n')
with open(self.merges_file , 'w' , encoding='utf-8') as fp:
fp.write('\n'.join(_lowercase))
def __snake_case ( self : Optional[int] , **_lowercase : Optional[int]) -> Union[str, Any]:
kwargs.update(self.special_tokens_map)
return CTRLTokenizer.from_pretrained(self.tmpdirname , **_lowercase)
def __snake_case ( self : List[Any] , _lowercase : Union[str, Any]) -> Dict:
A_ = 'adapt react readapt apt'
A_ = 'adapt react readapt apt'
return input_text, output_text
def __snake_case ( self : Any) -> Any:
A_ = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map)
A_ = 'adapt react readapt apt'
A_ = 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
A_ = tokenizer.tokenize(_lowercase)
self.assertListEqual(_lowercase , _lowercase)
A_ = tokens + [tokenizer.unk_token]
A_ = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase) , _lowercase)
| 366 | 0 |
from __future__ import annotations
import time
_lowercase = list[tuple[int, int]]
_lowercase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_lowercase = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class lowerCamelCase__ :
def __init__( self : List[Any] , __a : int , __a : int , __a : int , __a : int , __a : Node | None ):
'''simple docstring'''
lowerCamelCase__: List[Any] = pos_x
lowerCamelCase__: Optional[Any] = pos_y
lowerCamelCase__: str = (pos_y, pos_x)
lowerCamelCase__: Union[str, Any] = goal_x
lowerCamelCase__: str = goal_y
lowerCamelCase__: Union[str, Any] = parent
class lowerCamelCase__ :
def __init__( self : Optional[Any] , __a : tuple[int, int] , __a : tuple[int, int] ):
'''simple docstring'''
lowerCamelCase__: Optional[int] = Node(start[1] , start[0] , goal[1] , goal[0] , __a )
lowerCamelCase__: List[Any] = Node(goal[1] , goal[0] , goal[1] , goal[0] , __a )
lowerCamelCase__: Any = [self.start]
lowerCamelCase__: Optional[int] = False
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
while self.node_queue:
lowerCamelCase__: Dict = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
lowerCamelCase__: int = True
return self.retrace_path(__a )
lowerCamelCase__: Union[str, Any] = self.get_successors(__a )
for node in successors:
self.node_queue.append(__a )
if not self.reached:
return [self.start.pos]
return None
def lowerCamelCase_ ( self : List[str] , __a : Node ):
'''simple docstring'''
lowerCamelCase__: int = []
for action in delta:
lowerCamelCase__: str = parent.pos_x + action[1]
lowerCamelCase__: List[str] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(__a , __a , self.target.pos_y , self.target.pos_x , __a ) )
return successors
def lowerCamelCase_ ( self : Tuple , __a : Node | None ):
'''simple docstring'''
lowerCamelCase__: Union[str, Any] = node
lowerCamelCase__: str = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
lowerCamelCase__: str = current_node.parent
path.reverse()
return path
class lowerCamelCase__ :
def __init__( self : List[Any] , __a : str , __a : Dict ):
'''simple docstring'''
lowerCamelCase__: int = BreadthFirstSearch(__a , __a )
lowerCamelCase__: Dict = BreadthFirstSearch(__a , __a )
lowerCamelCase__: str = False
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
lowerCamelCase__: Dict = self.fwd_bfs.node_queue.pop(0 )
lowerCamelCase__: Optional[int] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
lowerCamelCase__: str = True
return self.retrace_bidirectional_path(
__a , __a )
lowerCamelCase__: int = current_bwd_node
lowerCamelCase__: List[Any] = current_fwd_node
lowerCamelCase__: List[str] = {
self.fwd_bfs: self.fwd_bfs.get_successors(__a ),
self.bwd_bfs: self.bwd_bfs.get_successors(__a ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(__a )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def lowerCamelCase_ ( self : Dict , __a : Node , __a : Node ):
'''simple docstring'''
lowerCamelCase__: str = self.fwd_bfs.retrace_path(__a )
lowerCamelCase__: Optional[int] = self.bwd_bfs.retrace_path(__a )
bwd_path.pop()
bwd_path.reverse()
lowerCamelCase__: Tuple = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_lowercase = (0, 0)
_lowercase = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_lowercase = time.time()
_lowercase = BreadthFirstSearch(init, goal)
_lowercase = bfs.search()
_lowercase = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
_lowercase = time.time()
_lowercase = BidirectionalBreadthFirstSearch(init, goal)
_lowercase = bd_bfs.search()
_lowercase = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time)
| 242 |
def __lowerCAmelCase ( _UpperCamelCase , _UpperCamelCase ) -> int:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_UpperCamelCase , int(b / 2 ) ) * actual_power(_UpperCamelCase , int(b / 2 ) )
else:
return a * actual_power(_UpperCamelCase , int(b / 2 ) ) * actual_power(_UpperCamelCase , int(b / 2 ) )
def __lowerCAmelCase ( _UpperCamelCase , _UpperCamelCase ) -> float:
'''simple docstring'''
if b < 0:
return 1 / actual_power(_UpperCamelCase , _UpperCamelCase )
return actual_power(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
print(power(-2, -3))
| 242 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
__magic_name__ : int = False
@skip_mps
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = StableDiffusionAttendAndExcitePipeline
UpperCAmelCase__ : Any = False
UpperCAmelCase__ : Optional[int] = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase__ : Dict = TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''} )
UpperCAmelCase__ : List[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase__ : Any = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def UpperCamelCase( cls ):
super().setUpClass()
torch.use_deterministic_algorithms(lowerCamelCase )
@classmethod
def UpperCamelCase( cls ):
super().tearDownClass()
torch.use_deterministic_algorithms(lowerCamelCase )
def UpperCamelCase( self ):
torch.manual_seed(0 )
_snake_case = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=lowerCamelCase , )
_snake_case = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=lowerCamelCase , set_alpha_to_one=lowerCamelCase , )
torch.manual_seed(0 )
_snake_case = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_snake_case = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act="gelu" , projection_dim=512 , )
_snake_case = CLIPTextModel(lowerCamelCase )
_snake_case = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_snake_case = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCamelCase( self , lowerCamelCase , lowerCamelCase=0 ):
if str(lowerCamelCase ).startswith("mps" ):
_snake_case = torch.manual_seed(lowerCamelCase )
else:
_snake_case = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
_snake_case = _snake_case = {
"prompt": "a cat and a frog",
"token_indices": [2, 5],
"generator": generator,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
"max_iter_to_alter": 2,
"thresholds": {0: 0.7},
}
return inputs
def UpperCamelCase( self ):
_snake_case = "cpu"
_snake_case = self.get_dummy_components()
_snake_case = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_snake_case = self.get_dummy_inputs(lowerCamelCase )
_snake_case = pipe(**lowerCamelCase ).images
_snake_case = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 64, 64, 3) )
_snake_case = np.array(
[0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496] )
_snake_case = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1e-3 )
def UpperCamelCase( self ):
super().test_cpu_offload_forward_pass(expected_max_diff=5e-4 )
def UpperCamelCase( self ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase( self ):
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7e-4 )
def UpperCamelCase( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def UpperCamelCase( self ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4 )
def UpperCamelCase( self ):
super().test_save_load_local(expected_max_difference=5e-4 )
def UpperCamelCase( self ):
super().test_save_load_optional_components(expected_max_difference=4e-4 )
@require_torch_gpu
@slow
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def UpperCamelCase( cls ):
super().setUpClass()
torch.use_deterministic_algorithms(lowerCamelCase )
@classmethod
def UpperCamelCase( cls ):
super().tearDownClass()
torch.use_deterministic_algorithms(lowerCamelCase )
def UpperCamelCase( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase( self ):
_snake_case = torch.manual_seed(51 )
_snake_case = StableDiffusionAttendAndExcitePipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , safety_checker=lowerCamelCase , torch_dtype=torch.floataa )
pipe.to("cuda" )
_snake_case = "a painting of an elephant with glasses"
_snake_case = [5, 7]
_snake_case = pipe(
prompt=lowerCamelCase , token_indices=lowerCamelCase , guidance_scale=7.5 , generator=lowerCamelCase , num_inference_steps=5 , max_iter_to_alter=5 , output_type="numpy" , ).images[0]
_snake_case = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy" )
assert np.abs((expected_image - image).max() ) < 5e-1
| 672 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , np.ndarray ):
return list(tensor.shape )
_snake_case = tf.shape(SCREAMING_SNAKE_CASE__ )
if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE__ ):
return dynamic
_snake_case = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE__ )]
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None ):
'''simple docstring'''
return tf.nn.softmax(logits=logits + 1E-9 , axis=SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=-1 ):
'''simple docstring'''
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )
# Get mean and variance on the axis to be normalized
_snake_case , _snake_case = tf.nn.moments(SCREAMING_SNAKE_CASE__ , axes=[axis] , keepdims=SCREAMING_SNAKE_CASE__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
_snake_case = [1] * inputs.shape.rank
_snake_case = shape_list(SCREAMING_SNAKE_CASE__ )[axis]
_snake_case = tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
_snake_case = tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Compute layer normalization using the batch_normalization
# function.
_snake_case = tf.nn.batch_normalization(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , offset=SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ , variance_epsilon=SCREAMING_SNAKE_CASE__ , )
return outputs
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=-1 ):
'''simple docstring'''
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
_snake_case = tf.shape(SCREAMING_SNAKE_CASE__ )
_snake_case = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
_snake_case = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , tf.Tensor ):
_snake_case = tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
_snake_case = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
_snake_case = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
_snake_case = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "input_ids" ):
'''simple docstring'''
tf.debugging.assert_less(
SCREAMING_SNAKE_CASE__ , tf.cast(SCREAMING_SNAKE_CASE__ , dtype=tensor.dtype ) , message=(
f'''The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE__ )}) must be smaller than the embedding '''
f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = 6_45_12
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
_snake_case = [x for x in data if len(SCREAMING_SNAKE_CASE__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"The following attributes cannot be saved to HDF5 file because "
f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
f'''bytes: {bad_attributes}''' )
_snake_case = np.asarray(SCREAMING_SNAKE_CASE__ )
_snake_case = 1
_snake_case = np.array_split(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
_snake_case = np.array_split(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__ ):
_snake_case = chunk_data
else:
_snake_case = data
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if name in group.attrs:
_snake_case = [n.decode("utf8" ) if hasattr(SCREAMING_SNAKE_CASE__ , "decode" ) else n for n in group.attrs[name]]
else:
_snake_case = []
_snake_case = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("utf8" ) if hasattr(SCREAMING_SNAKE_CASE__ , "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
chunk_id += 1
return data
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE__ ):
if isinstance(SCREAMING_SNAKE_CASE__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(SCREAMING_SNAKE_CASE__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , SCREAMING_SNAKE_CASE__ )
| 672 | 1 |
'''simple docstring'''
from __future__ import annotations
__snake_case: Optional[int] = "Muhammad Umer Farooq"
__snake_case: Optional[int] = "MIT"
__snake_case: Tuple = "1.0.0"
__snake_case: Optional[Any] = "Muhammad Umer Farooq"
__snake_case: Any = "contact@muhammadumerfarooq.me"
__snake_case: int = "Alpha"
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class _UpperCAmelCase ( __a ):
"""simple docstring"""
def __init__( self , lowerCAmelCase_ ):
'''simple docstring'''
super().__init__()
a_ : Any = []
a_ : List[str] = domain
def _lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
a_ : int = parse.urljoin(self.domain , lowerCAmelCase_ )
self.urls.append(lowerCAmelCase_ )
def _snake_case ( A_ : Dict ):
"""simple docstring"""
return ".".join(get_sub_domain_name(lowercase__ ).split(""".""" )[-2:] )
def _snake_case ( A_ : List[str] ):
"""simple docstring"""
return parse.urlparse(lowercase__ ).netloc
def _snake_case ( A_ : Dict = "https://github.com" ):
"""simple docstring"""
a_ : Optional[int] = get_domain_name(lowercase__ )
# Initialize the parser
a_ : List[str] = Parser(lowercase__ )
try:
# Open URL
a_ : Any = requests.get(lowercase__ )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
a_ : Tuple = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
a_ : Dict = requests.get(lowercase__ )
# Get the valid email.
a_ : int = re.findall("""[a-zA-Z0-9]+@""" + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(lowercase__ )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(lowercase__ )
if __name__ == "__main__":
__snake_case: List[Any] = emails_from_url("https://github.com")
print(F"""{len(emails)} emails found:""")
print("\n".join(sorted(emails)))
| 719 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__snake_case: Tuple = logging.get_logger(__name__)
__snake_case: Tuple = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class _UpperCAmelCase ( lowerCAmelCase__ ):
"""simple docstring"""
a_ = "t5"
a_ = ["past_key_values"]
a_ = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self , lowerCAmelCase_=3_21_28 , lowerCAmelCase_=5_12 , lowerCAmelCase_=64 , lowerCAmelCase_=20_48 , lowerCAmelCase_=6 , lowerCAmelCase_=None , lowerCAmelCase_=8 , lowerCAmelCase_=32 , lowerCAmelCase_=1_28 , lowerCAmelCase_=0.1 , lowerCAmelCase_=1E-6 , lowerCAmelCase_=1.0 , lowerCAmelCase_="relu" , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=0 , lowerCAmelCase_=1 , **lowerCAmelCase_ , ):
'''simple docstring'''
a_ : List[Any] = vocab_size
a_ : List[str] = d_model
a_ : Union[str, Any] = d_kv
a_ : List[Any] = d_ff
a_ : int = num_layers
a_ : Any = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
a_ : Union[str, Any] = num_heads
a_ : int = relative_attention_num_buckets
a_ : Dict = relative_attention_max_distance
a_ : Union[str, Any] = dropout_rate
a_ : Union[str, Any] = layer_norm_epsilon
a_ : List[Any] = initializer_factor
a_ : Any = feed_forward_proj
a_ : List[Any] = use_cache
a_ : Dict = self.feed_forward_proj.split("""-""" )
a_ : Union[str, Any] = act_info[-1]
a_ : str = act_info[0] == """gated"""
if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
a_ : List[str] = """gelu_new"""
super().__init__(
pad_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ , )
class _UpperCAmelCase ( lowerCAmelCase__ ):
"""simple docstring"""
@property
def _lowerCAmelCase ( self ):
'''simple docstring'''
a_ : List[Any] = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
a_ : Tuple = """past_encoder_sequence + sequence"""
a_ : str = {0: """batch"""}
a_ : Any = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
a_ : List[Any] = {0: """batch""", 1: """decoder_sequence"""}
a_ : Optional[int] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase_ , direction="""inputs""" )
return common_inputs
@property
def _lowerCAmelCase ( self ):
'''simple docstring'''
return 13
| 460 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class lowercase_ :
"""simple docstring"""
def __init__( self : str , __lowerCamelCase : int , __lowerCamelCase : Dict=9_9 , __lowerCamelCase : Any=1_3 , __lowerCamelCase : Tuple=7 , __lowerCamelCase : int=9 , __lowerCamelCase : List[str]=True , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : int=False , __lowerCamelCase : str=3_2 , __lowerCamelCase : Dict=5 , __lowerCamelCase : Union[str, Any]=4 , __lowerCamelCase : int=3_7 , __lowerCamelCase : Optional[int]=8 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : Tuple=0.0_0_2 , __lowerCamelCase : Optional[int]=1 , __lowerCamelCase : int=0 , __lowerCamelCase : str=0 , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Optional[Any]=None , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = encoder_seq_length
_SCREAMING_SNAKE_CASE = decoder_seq_length
# For common tests
_SCREAMING_SNAKE_CASE = self.decoder_seq_length
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_attention_mask
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = d_ff
_SCREAMING_SNAKE_CASE = relative_attention_num_buckets
_SCREAMING_SNAKE_CASE = dropout_rate
_SCREAMING_SNAKE_CASE = initializer_factor
_SCREAMING_SNAKE_CASE = eos_token_id
_SCREAMING_SNAKE_CASE = pad_token_id
_SCREAMING_SNAKE_CASE = decoder_start_token_id
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = decoder_layers
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
return TaConfig.from_pretrained("google/umt5-base" )
def lowerCAmelCase_ ( self : Optional[int] , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any] , __lowerCamelCase : List[str] , __lowerCamelCase : List[Any]=None , __lowerCamelCase : Tuple=None , __lowerCamelCase : Optional[Any]=None , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Optional[Any]=None , ):
"""simple docstring"""
if attention_mask is None:
_SCREAMING_SNAKE_CASE = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_SCREAMING_SNAKE_CASE = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_SCREAMING_SNAKE_CASE = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=snake_case__ )
if decoder_head_mask is None:
_SCREAMING_SNAKE_CASE = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=snake_case__ )
if cross_attn_head_mask is None:
_SCREAMING_SNAKE_CASE = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=snake_case__ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_SCREAMING_SNAKE_CASE = input_ids.clamp(self.pad_token_id + 1 )
_SCREAMING_SNAKE_CASE = decoder_input_ids.clamp(self.pad_token_id + 1 )
_SCREAMING_SNAKE_CASE = self.get_config()
_SCREAMING_SNAKE_CASE = config.num_attention_heads
_SCREAMING_SNAKE_CASE = self.prepare_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
return config, input_dict
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
return TaConfig(
vocab_size=1_6_6 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def lowerCAmelCase_ ( self : List[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Dict , __lowerCamelCase : str , __lowerCamelCase : Any , __lowerCamelCase : List[Any] , __lowerCamelCase : str , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = UMTaModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(
input_ids=snake_case__ , decoder_input_ids=snake_case__ , attention_mask=snake_case__ , decoder_attention_mask=snake_case__ , )
_SCREAMING_SNAKE_CASE = model(input_ids=snake_case__ , decoder_input_ids=snake_case__ )
_SCREAMING_SNAKE_CASE = result.last_hidden_state
_SCREAMING_SNAKE_CASE = result.past_key_values
_SCREAMING_SNAKE_CASE = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(snake_case__ ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def lowerCAmelCase_ ( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict , __lowerCamelCase : Any , __lowerCamelCase : List[str] , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = UMTaModel(config=snake_case__ ).get_decoder().to(snake_case__ ).eval()
# first forward pass
_SCREAMING_SNAKE_CASE = model(snake_case__ , use_cache=snake_case__ )
_SCREAMING_SNAKE_CASE = model(snake_case__ )
_SCREAMING_SNAKE_CASE = model(snake_case__ , use_cache=snake_case__ )
self.parent.assertTrue(len(snake_case__ ) == len(snake_case__ ) )
self.parent.assertTrue(len(snake_case__ ) == len(snake_case__ ) + 1 )
_SCREAMING_SNAKE_CASE = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_SCREAMING_SNAKE_CASE = torch.cat([input_ids, next_tokens] , dim=-1 )
_SCREAMING_SNAKE_CASE = model(snake_case__ )['last_hidden_state']
_SCREAMING_SNAKE_CASE = model(snake_case__ , past_key_values=snake_case__ )['last_hidden_state']
# select random slice
_SCREAMING_SNAKE_CASE = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_SCREAMING_SNAKE_CASE = output_from_no_past[:, -1, random_slice_idx].detach()
_SCREAMING_SNAKE_CASE = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1e-3 ) )
def lowerCAmelCase_ ( self : Optional[int] , __lowerCamelCase : List[str] , __lowerCamelCase : List[Any] , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = UMTaModel(config=snake_case__ ).to(snake_case__ ).half().eval()
_SCREAMING_SNAKE_CASE = model(**snake_case__ )['last_hidden_state']
self.parent.assertFalse(torch.isnan(snake_case__ ).any().item() )
@require_torch
class lowercase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase_ = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
lowerCamelCase_ = (UMTaForConditionalGeneration,) if is_torch_available() else ()
lowerCamelCase_ = (
{
'''conversational''': UMTaForConditionalGeneration,
'''feature-extraction''': UMTaModel,
'''summarization''': UMTaForConditionalGeneration,
'''text2text-generation''': UMTaForConditionalGeneration,
'''translation''': UMTaForConditionalGeneration,
'''question-answering''': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = True
lowerCamelCase_ = True
# The small UMT5 model needs higher percentages for CPU/MP tests
lowerCamelCase_ = [0.8, 0.9]
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = UMTaModelTester(self )
@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE = UMTaModel(config_and_inputs[0] ).to(snake_case__ )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
snake_case__ , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F"""{tmpdirname}/t5_test.onnx""" , export_params=snake_case__ , opset_version=9 , input_names=["input_ids", "decoder_input_ids"] , )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*snake_case__ )
def lowerCAmelCase_ ( self : int ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE = config_and_inputs[0]
_SCREAMING_SNAKE_CASE = UMTaForConditionalGeneration(snake_case__ ).eval()
model.to(snake_case__ )
_SCREAMING_SNAKE_CASE = {
'head_mask': torch.zeros(config.num_layers , config.num_heads , device=snake_case__ ),
'decoder_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=snake_case__ ),
'cross_attn_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=snake_case__ ),
}
for attn_name, (name, mask) in zip(snake_case__ , head_masking.items() ):
_SCREAMING_SNAKE_CASE = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_SCREAMING_SNAKE_CASE = torch.ones(
config.num_decoder_layers , config.num_heads , device=snake_case__ )
_SCREAMING_SNAKE_CASE = model.generate(
config_and_inputs[1]["input_ids"] , num_beams=1 , max_length=3 , output_attentions=snake_case__ , return_dict_in_generate=snake_case__ , **snake_case__ , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_SCREAMING_SNAKE_CASE = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases." )
def lowerCAmelCase_ ( self : int ):
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
@slow
@unittest.skip(
"Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged" )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = UMTaForConditionalGeneration.from_pretrained("google/umt5-small" , return_dict=snake_case__ ).to(snake_case__ )
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("google/umt5-small" , use_fast=snake_case__ , legacy=snake_case__ )
_SCREAMING_SNAKE_CASE = [
'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
'No se como puedo <extra_id_0>.',
'This is the reason why we <extra_id_0> them.',
'The <extra_id_0> walks in <extra_id_1>, seats',
'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
]
_SCREAMING_SNAKE_CASE = tokenizer(snake_case__ , return_tensors="pt" , padding=snake_case__ ).input_ids
# fmt: off
_SCREAMING_SNAKE_CASE = torch.tensor(
[
[ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
] )
# fmt: on
torch.testing.assert_allclose(snake_case__ , snake_case__ )
_SCREAMING_SNAKE_CASE = model.generate(input_ids.to(snake_case__ ) )
_SCREAMING_SNAKE_CASE = [
'<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
'<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
]
_SCREAMING_SNAKE_CASE = tokenizer.batch_decode(snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
| 418 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
__magic_name__ = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__magic_name__ = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__magic_name__ = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__magic_name__ = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__magic_name__ = field(default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Whether tp freeze the encoder."} )
__magic_name__ = field(default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Whether to freeze the embeddings."} )
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
__magic_name__ = field(
metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."} )
__magic_name__ = field(
default="summarization" , metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"} , )
__magic_name__ = field(
default=1_0_2_4 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__magic_name__ = field(
default=1_2_8 , metadata={
"help": (
"The maximum total sequence length for target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__magic_name__ = field(
default=1_4_2 , metadata={
"help": (
"The maximum total sequence length for validation target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded. "
"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
"during ``evaluate`` and ``predict``."
)
} , )
__magic_name__ = field(
default=1_4_2 , metadata={
"help": (
"The maximum total sequence length for test target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__magic_name__ = field(default=-1 , metadata={"help": "# training examples. -1 means use all."} )
__magic_name__ = field(default=-1 , metadata={"help": "# validation examples. -1 means use all."} )
__magic_name__ = field(default=-1 , metadata={"help": "# test examples. -1 means use all."} )
__magic_name__ = field(default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Source language id for translation."} )
__magic_name__ = field(default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Target language id for translation."} )
__magic_name__ = field(default=SCREAMING_SNAKE_CASE_ , metadata={"help": "# num_beams to use for evaluation."} )
__magic_name__ = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."} , )
def lowercase (_A , _A , _A ):
"""simple docstring"""
logger.info(f'***** {split} metrics *****' )
for key in sorted(metrics.keys() ):
logger.info(f' {key} = {metrics[key]}' )
save_json(_A , os.path.join(_A , f'{split}_results.json' ) )
def lowercase ():
"""simple docstring"""
_lowerCAmelCase : int = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[int] = parser.parse_args_into_dataclasses()
check_output_dir(_A )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('Training/evaluation parameters %s' , _A )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCAmelCase : Optional[int] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowerCAmelCase : Tuple = ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
for p in extra_model_params:
if getattr(_A , _A , _A ):
assert hasattr(_A , _A ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
setattr(_A , _A , getattr(_A , _A ) )
_lowerCAmelCase : 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 , )
_lowerCAmelCase : str = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf='.ckpt' in model_args.model_name_or_path , config=_A , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(_A , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCAmelCase : Union[str, Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(_A , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(_A , _A ):
_lowerCAmelCase : Union[str, Any] = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCAmelCase : Union[str, Any] = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(_A )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCAmelCase : Any = SeqaSeqDataset
# Get datasets
_lowerCAmelCase : List[Any] = (
dataset_class(
_A , type_path='train' , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '' , )
if training_args.do_train
else None
)
_lowerCAmelCase : Optional[Any] = (
dataset_class(
_A , type_path='val' , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '' , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCAmelCase : Union[str, Any] = (
dataset_class(
_A , type_path='test' , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '' , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCAmelCase : Optional[Any] = (
build_compute_metrics_fn(data_args.task , _A ) if training_args.predict_with_generate else None
)
_lowerCAmelCase : int = SeqaSeqTrainer(
model=_A , args=_A , data_args=_A , train_dataset=_A , eval_dataset=_A , data_collator=SeqaSeqDataCollator(
_A , _A , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=_A , tokenizer=_A , )
_lowerCAmelCase : str = {}
# Training
if training_args.do_train:
logger.info('*** Train ***' )
_lowerCAmelCase : Dict = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCAmelCase : Any = train_result.metrics
_lowerCAmelCase : str = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics('train' , _A , training_args.output_dir )
all_metrics.update(_A )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , 'trainer_state.json' ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_lowerCAmelCase : List[str] = trainer.evaluate(metric_key_prefix='val' )
_lowerCAmelCase : int = data_args.n_val
_lowerCAmelCase : List[str] = round(metrics['val_loss'] , 4 )
if trainer.is_world_process_zero():
handle_metrics('val' , _A , training_args.output_dir )
all_metrics.update(_A )
if training_args.do_predict:
logger.info('*** Predict ***' )
_lowerCAmelCase : Any = trainer.predict(test_dataset=_A , metric_key_prefix='test' )
_lowerCAmelCase : Optional[int] = test_output.metrics
_lowerCAmelCase : Optional[int] = data_args.n_test
if trainer.is_world_process_zero():
_lowerCAmelCase : Dict = round(metrics['test_loss'] , 4 )
handle_metrics('test' , _A , training_args.output_dir )
all_metrics.update(_A )
if training_args.predict_with_generate:
_lowerCAmelCase : Union[str, Any] = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=_A , clean_up_tokenization_spaces=_A )
_lowerCAmelCase : List[Any] = lmap(str.strip , _A )
write_txt_file(_A , os.path.join(training_args.output_dir , 'test_generations.txt' ) )
if trainer.is_world_process_zero():
save_json(_A , os.path.join(training_args.output_dir , 'all_results.json' ) )
return all_metrics
def lowercase (_A ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 444 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class UpperCAmelCase__ ( lowercase__ , lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : Any = 1
@register_to_config
def __init__( self : Tuple ,_a : Dict=2000 ,_a : Union[str, Any]=0.1 ,_a : Dict=20 ,_a : List[Any]=1E-3 ):
'''simple docstring'''
_a : Dict = None
_a : int = None
_a : Union[str, Any] = None
def __lowercase ( self : Any ,_a : Dict ,_a : Union[str, torch.device] = None ):
'''simple docstring'''
_a : List[Any] = torch.linspace(1 ,self.config.sampling_eps ,_a ,device=_a )
def __lowercase ( self : Union[str, Any] ,_a : str ,_a : Optional[int] ,_a : Any ,_a : Union[str, Any]=None ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
'`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
_a : int = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
_a : Union[str, Any] = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
_a : Dict = std.flatten()
while len(std.shape ) < len(score.shape ):
_a : Any = std.unsqueeze(-1 )
_a : int = -score / std
# compute
_a : List[Any] = -1.0 / len(self.timesteps )
_a : str = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
_a : Union[str, Any] = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
_a : List[str] = beta_t.unsqueeze(-1 )
_a : Any = -0.5 * beta_t * x
_a : Any = torch.sqrt(_a )
_a : Optional[Any] = drift - diffusion**2 * score
_a : List[Any] = x + drift * dt
# add noise
_a : int = randn_tensor(x.shape ,layout=x.layout ,generator=_a ,device=x.device ,dtype=x.dtype )
_a : Optional[Any] = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self : Optional[int] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 319 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__lowerCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : int = '''AutoTokenizer'''
__UpperCAmelCase : Optional[Any] = ['''tokenizer''']
__UpperCAmelCase : str = {
'''semantic_prompt''': 1,
'''coarse_prompt''': 2,
'''fine_prompt''': 2,
}
def __init__( self : Union[str, Any] ,_a : Union[str, Any] ,_a : Dict=None ):
'''simple docstring'''
super().__init__(_a )
_a : List[str] = speaker_embeddings
@classmethod
def __lowercase ( cls : Any ,_a : Optional[int] ,_a : Union[str, Any]="speaker_embeddings_path.json" ,**_a : Union[str, Any] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
_a : Tuple = get_file_from_repo(
_a ,_a ,subfolder=kwargs.pop('subfolder' ,_a ) ,cache_dir=kwargs.pop('cache_dir' ,_a ) ,force_download=kwargs.pop('force_download' ,_a ) ,proxies=kwargs.pop('proxies' ,_a ) ,resume_download=kwargs.pop('resume_download' ,_a ) ,local_files_only=kwargs.pop('local_files_only' ,_a ) ,use_auth_token=kwargs.pop('use_auth_token' ,_a ) ,revision=kwargs.pop('revision' ,_a ) ,)
if speaker_embeddings_path is None:
logger.warning(
F"""`{os.path.join(_a ,_a )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.""" )
_a : List[Any] = None
else:
with open(_a ) as speaker_embeddings_json:
_a : List[str] = json.load(_a )
else:
_a : str = None
_a : Any = AutoTokenizer.from_pretrained(_a ,**_a )
return cls(tokenizer=_a ,speaker_embeddings=_a )
def __lowercase ( self : List[str] ,_a : Tuple ,_a : Any="speaker_embeddings_path.json" ,_a : Optional[int]="speaker_embeddings" ,_a : bool = False ,**_a : Optional[int] ,):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_a ,_a ,'v2' ) ,exist_ok=_a )
_a : Optional[Any] = {}
_a : List[str] = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
_a : Any = self._load_voice_preset(_a )
_a : Tuple = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict['repo_or_path'] ,_a ,F"""{prompt_key}_{key}""" ) ,voice_preset[key] ,allow_pickle=_a ,)
_a : Dict = os.path.join(_a ,F"""{prompt_key}_{key}.npy""" )
_a : Any = tmp_dict
with open(os.path.join(_a ,_a ) ,'w' ) as fp:
json.dump(_a ,_a )
super().save_pretrained(_a ,_a ,**_a )
def __lowercase ( self : Tuple ,_a : str = None ,**_a : List[Any] ):
'''simple docstring'''
_a : Optional[Any] = self.speaker_embeddings[voice_preset]
_a : Optional[Any] = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].""" )
_a : List[Any] = get_file_from_repo(
self.speaker_embeddings.get('repo_or_path' ,'/' ) ,voice_preset_paths[key] ,subfolder=kwargs.pop('subfolder' ,_a ) ,cache_dir=kwargs.pop('cache_dir' ,_a ) ,force_download=kwargs.pop('force_download' ,_a ) ,proxies=kwargs.pop('proxies' ,_a ) ,resume_download=kwargs.pop('resume_download' ,_a ) ,local_files_only=kwargs.pop('local_files_only' ,_a ) ,use_auth_token=kwargs.pop('use_auth_token' ,_a ) ,revision=kwargs.pop('revision' ,_a ) ,)
if path is None:
raise ValueError(
F"""`{os.path.join(self.speaker_embeddings.get('repo_or_path' ,'/' ) ,voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.""" )
_a : Tuple = np.load(_a )
return voice_preset_dict
def __lowercase ( self : List[Any] ,_a : Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(F"""Voice preset unrecognized, missing {key} as a key.""" )
if not isinstance(voice_preset[key] ,np.ndarray ):
raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.""" )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.""" )
def __call__( self : Any ,_a : List[str]=None ,_a : Tuple=None ,_a : Tuple="pt" ,_a : Any=256 ,_a : Optional[Any]=False ,_a : List[str]=True ,_a : Optional[Any]=False ,**_a : Dict ,):
'''simple docstring'''
if voice_preset is not None and not isinstance(_a ,_a ):
if (
isinstance(_a ,_a )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
_a : Union[str, Any] = self._load_voice_preset(_a )
else:
if isinstance(_a ,_a ) and not voice_preset.endswith('.npz' ):
_a : str = voice_preset + '.npz'
_a : Optional[int] = np.load(_a )
if voice_preset is not None:
self._validate_voice_preset_dict(_a ,**_a )
_a : List[str] = BatchFeature(data=_a ,tensor_type=_a )
_a : List[Any] = self.tokenizer(
_a ,return_tensors=_a ,padding='max_length' ,max_length=_a ,return_attention_mask=_a ,return_token_type_ids=_a ,add_special_tokens=_a ,**_a ,)
if voice_preset is not None:
_a : Dict = voice_preset
return encoded_text
| 319 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def wrapper(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
A_ = timeit.default_timer()
A_ = func(*_UpperCamelCase , **_UpperCamelCase )
A_ = timeit.default_timer() - starttime
return delta
A_ = func.__name__
return wrapper
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=100 , SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
A_ = []
A_ = seq_shapes or {}
for i in range(_UpperCamelCase ):
A_ = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(_UpperCamelCase , _ArrayXD ):
A_ = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(_UpperCamelCase , datasets.Value ):
if v.dtype == "string":
A_ = '''The small grey turtle was surprisingly fast when challenged.'''
else:
A_ = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(_UpperCamelCase , datasets.Sequence ):
while isinstance(_UpperCamelCase , datasets.Sequence ):
A_ = v.feature
A_ = seq_shapes[k]
A_ = np.random.rand(*_UpperCamelCase ).astype(v.dtype )
A_ = data
dummy_data.append((i, example) )
return dummy_data
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=100 , SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
A_ = generate_examples(_UpperCamelCase , num_examples=_UpperCamelCase , seq_shapes=_UpperCamelCase )
with ArrowWriter(features=_UpperCamelCase , path=_UpperCamelCase ) as writer:
for key, record in dummy_data:
A_ = features.encode_example(_UpperCamelCase )
writer.write(_UpperCamelCase )
A_ ,A_ = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
A_ = datasets.Dataset.from_file(filename=_UpperCamelCase , info=datasets.DatasetInfo(features=_UpperCamelCase ) )
return dataset
| 203 |
from __future__ import annotations
from math import gcd
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int = 2 , _UpperCamelCase : int = 1 , _UpperCamelCase : int = 3 , ) -> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input value cannot be less than 2' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(_UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int ) -> int:
return (pow(_UpperCamelCase , 2 ) + step) % modulus
for _ in range(_UpperCamelCase ):
# These track the position within the cycle detection logic.
SCREAMING_SNAKE_CASE = seed
SCREAMING_SNAKE_CASE = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
SCREAMING_SNAKE_CASE = rand_fn(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
SCREAMING_SNAKE_CASE = rand_fn(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
SCREAMING_SNAKE_CASE = rand_fn(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
SCREAMING_SNAKE_CASE = gcd(hare - tortoise , _UpperCamelCase )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
SCREAMING_SNAKE_CASE = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
a_ : int = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
a_ : Tuple = parser.parse_args()
a_ : int = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"""{args.num} is probably prime""")
else:
a_ : Union[str, Any] = args.num // divisor
print(F"""{args.num} = {divisor} * {quotient}""")
| 439 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase ( __UpperCamelCase ):
def __init__( self : List[Any] , UpperCAmelCase : pyspark.sql.DataFrame , UpperCAmelCase : Optional[NamedSplit] = None , UpperCAmelCase : Optional[Features] = None , UpperCAmelCase : bool = True , UpperCAmelCase : str = None , UpperCAmelCase : bool = False , UpperCAmelCase : str = None , UpperCAmelCase : bool = True , UpperCAmelCase : str = "arrow" , **UpperCAmelCase : Tuple , ) -> int:
super().__init__(
split=UpperCAmelCase , features=UpperCAmelCase , cache_dir=UpperCAmelCase , keep_in_memory=UpperCAmelCase , streaming=UpperCAmelCase , **UpperCAmelCase , )
lowerCamelCase__ : Union[str, Any] = load_from_cache_file
lowerCamelCase__ : List[Any] = file_format
lowerCamelCase__ : List[Any] = Spark(
df=UpperCAmelCase , features=UpperCAmelCase , cache_dir=UpperCAmelCase , working_dir=UpperCAmelCase , **UpperCAmelCase , )
def A_ ( self : str ) -> Union[str, Any]:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowerCamelCase__ : List[str] = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=UpperCAmelCase , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 715 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class lowerCAmelCase ( unittest.TestCase ):
def A_ ( self : str ) -> Union[str, Any]:
lowerCamelCase__ : Optional[int] = 'laion/clap-htsat-unfused'
lowerCamelCase__ : List[Any] = tempfile.mkdtemp()
def A_ ( self : Optional[int] , **UpperCAmelCase : int ) -> Optional[Any]:
return RobertaTokenizer.from_pretrained(self.checkpoint , **UpperCAmelCase )
def A_ ( self : Union[str, Any] , **UpperCAmelCase : Any ) -> List[str]:
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **UpperCAmelCase )
def A_ ( self : List[str] ) -> Dict:
shutil.rmtree(self.tmpdirname )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
lowerCamelCase__ : Tuple = self.get_tokenizer()
lowerCamelCase__ : Dict = self.get_feature_extractor()
lowerCamelCase__ : Dict = ClapProcessor(tokenizer=UpperCAmelCase , feature_extractor=UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ : List[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCAmelCase )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , UpperCAmelCase )
def A_ ( self : str ) -> int:
lowerCamelCase__ : Optional[Any] = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ : Dict = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowerCamelCase__ : Dict = self.get_feature_extractor(do_normalize=UpperCAmelCase , padding_value=1.0 )
lowerCamelCase__ : str = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCAmelCase )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , UpperCAmelCase )
def A_ ( self : List[Any] ) -> int:
lowerCamelCase__ : Any = self.get_feature_extractor()
lowerCamelCase__ : str = self.get_tokenizer()
lowerCamelCase__ : Optional[Any] = ClapProcessor(tokenizer=UpperCAmelCase , feature_extractor=UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = floats_list((3, 1000) )
lowerCamelCase__ : Tuple = feature_extractor(UpperCAmelCase , return_tensors='np' )
lowerCamelCase__ : Tuple = processor(audios=UpperCAmelCase , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A_ ( self : List[str] ) -> Tuple:
lowerCamelCase__ : Dict = self.get_feature_extractor()
lowerCamelCase__ : List[Any] = self.get_tokenizer()
lowerCamelCase__ : Any = ClapProcessor(tokenizer=UpperCAmelCase , feature_extractor=UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = 'This is a test string'
lowerCamelCase__ : Union[str, Any] = processor(text=UpperCAmelCase )
lowerCamelCase__ : Any = tokenizer(UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : Optional[int] ) -> Tuple:
lowerCamelCase__ : Tuple = self.get_feature_extractor()
lowerCamelCase__ : List[str] = self.get_tokenizer()
lowerCamelCase__ : Optional[int] = ClapProcessor(tokenizer=UpperCAmelCase , feature_extractor=UpperCAmelCase )
lowerCamelCase__ : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCamelCase__ : List[Any] = processor.batch_decode(UpperCAmelCase )
lowerCamelCase__ : Tuple = tokenizer.batch_decode(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def A_ ( self : Optional[Any] ) -> int:
lowerCamelCase__ : str = self.get_feature_extractor()
lowerCamelCase__ : str = self.get_tokenizer()
lowerCamelCase__ : Dict = ClapProcessor(tokenizer=UpperCAmelCase , feature_extractor=UpperCAmelCase )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
| 188 | 0 |
def UpperCamelCase_ ( __a ) -> bool:
a__ : List[Any] = 0
for ch in input_str:
a__ : str = ord(__a )
a__ : Any = pow(2 , __a )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 | def lowerCamelCase ( UpperCamelCase : int ) -> bool:
_lowerCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowerCamelCase ( UpperCamelCase : int = 50_00 ) -> int:
_lowerCamelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , UpperCamelCase )]
for i, pentagonal_i in enumerate(UpperCamelCase ):
for j in range(UpperCamelCase , len(UpperCamelCase ) ):
_lowerCamelCase = pentagonal_nums[j]
_lowerCamelCase = pentagonal_i + pentagonal_j
_lowerCamelCase = pentagonal_j - pentagonal_i
if is_pentagonal(UpperCamelCase ) and is_pentagonal(UpperCamelCase ):
return b
return -1
if __name__ == "__main__":
print(F'''{solution() = }''') | 544 | 0 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
)
| 260 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( self :List[str] , lowerCAmelCase__ :TransformeraDModel , lowerCAmelCase__ :AutoencoderKL , lowerCAmelCase__ :KarrasDiffusionSchedulers , lowerCAmelCase__ :Optional[Dict[int, str]] = None , ) -> List[Any]:
super().__init__()
self.register_modules(transformer=lowerCAmelCase__ , vae=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
# create a imagenet -> id dictionary for easier use
__SCREAMING_SNAKE_CASE : Dict = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''',''' ):
__SCREAMING_SNAKE_CASE : int = int(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = dict(sorted(self.labels.items() ) )
def __magic_name__( self :List[Any] , lowerCAmelCase__ :Union[str, List[str]] ) -> List[int]:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = list(lowerCAmelCase__ )
for l in label:
if l not in self.labels:
raise ValueError(
f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self :Tuple , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :float = 4.0 , lowerCAmelCase__ :Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCAmelCase__ :int = 50 , lowerCAmelCase__ :Optional[str] = "pil" , lowerCAmelCase__ :bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
__SCREAMING_SNAKE_CASE : Any = len(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.transformer.config.sample_size
__SCREAMING_SNAKE_CASE : Optional[Any] = self.transformer.config.in_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowerCAmelCase__ , device=self.device , dtype=self.transformer.dtype , )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
__SCREAMING_SNAKE_CASE : str = torch.tensor(lowerCAmelCase__ , device=self.device ).reshape(-1 )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_000] * batch_size , device=self.device )
__SCREAMING_SNAKE_CASE : Tuple = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowerCAmelCase__ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
__SCREAMING_SNAKE_CASE : Optional[int] = latent_model_input[: len(lowerCAmelCase__ ) // 2]
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([half, half] , dim=0 )
__SCREAMING_SNAKE_CASE : Dict = self.scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = t
if not torch.is_tensor(lowerCAmelCase__ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
__SCREAMING_SNAKE_CASE : Optional[int] = latent_model_input.device.type == '''mps'''
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Tuple = torch.floataa if is_mps else torch.floataa
else:
__SCREAMING_SNAKE_CASE : List[Any] = torch.intaa if is_mps else torch.intaa
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([timesteps] , dtype=lowerCAmelCase__ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
__SCREAMING_SNAKE_CASE : str = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__SCREAMING_SNAKE_CASE : str = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
__SCREAMING_SNAKE_CASE : Optional[Any] = self.transformer(
lowerCAmelCase__ , timestep=lowerCAmelCase__ , class_labels=lowerCAmelCase__ ).sample
# perform guidance
if guidance_scale > 1:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = torch.split(lowerCAmelCase__ , len(lowerCAmelCase__ ) // 2 , dim=0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
__SCREAMING_SNAKE_CASE : Any = torch.cat([half_eps, half_eps] , dim=0 )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = torch.split(lowerCAmelCase__ , lowerCAmelCase__ , dim=1 )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred
# compute previous image: x_t -> x_t-1
__SCREAMING_SNAKE_CASE : Tuple = self.scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
if guidance_scale > 1:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = latent_model_input.chunk(2 , dim=0 )
else:
__SCREAMING_SNAKE_CASE : List[str] = latent_model_input
__SCREAMING_SNAKE_CASE : Dict = 1 / self.vae.config.scaling_factor * latents
__SCREAMING_SNAKE_CASE : Tuple = self.vae.decode(lowerCAmelCase__ ).sample
__SCREAMING_SNAKE_CASE : int = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__SCREAMING_SNAKE_CASE : Dict = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__SCREAMING_SNAKE_CASE : str = self.numpy_to_pil(lowerCAmelCase__ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowerCAmelCase__ )
| 260 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : Union[str, Any] , a_ : int = 101 ):
"""simple docstring"""
__snake_case = length
def __len__( self : Optional[Any] ):
"""simple docstring"""
return self.length
def __getitem__( self : Optional[Any] , a_ : Any ):
"""simple docstring"""
return i
class SCREAMING_SNAKE_CASE__ :
def __call__( self : List[Any] , a_ : Any ):
"""simple docstring"""
return {"input_ids": torch.tensor(a_ ), "labels": torch.tensor(a_ )}
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Union[str, Any] ):
"""simple docstring"""
super().__init__()
# Add some (unused) params otherwise DDP will complain.
__snake_case = nn.Linear(120 , 80 )
def A ( self : int , a_ : Optional[int] , a_ : List[Any]=None ):
"""simple docstring"""
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
@require_torch_neuroncore
def A ( self : List[Any] ):
"""simple docstring"""
__snake_case = f'''--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
__snake_case = self.get_auto_remove_tmp_dir()
__snake_case = f'''--output_dir {output_dir}'''.split()
__snake_case = ["torchrun"] + distributed_args + args
execute_subprocess_async(a_ , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
@require_torch_multi_gpu
def A ( self : Any ):
"""simple docstring"""
__snake_case = f'''--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
__snake_case = self.get_auto_remove_tmp_dir()
__snake_case = f'''--output_dir {output_dir}'''.split()
__snake_case = ["torchrun"] + distributed_args + args
execute_subprocess_async(a_ , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
a : Optional[Any] = HfArgumentParser((TrainingArguments,))
a : List[str] = parser.parse_args_into_dataclasses()[0]
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
a : Union[str, Any] = DummyDataset(dataset_length)
def __UpperCAmelCase ( _UpperCAmelCase : EvalPrediction ) -> Dict:
__snake_case = list(range(len(_UpperCAmelCase ) ) )
__snake_case = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
F'''{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}''' )
return {"success": success}
a : List[Any] = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
a : Optional[Any] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
a : Tuple = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
a : Any = 2
a : Union[str, Any] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
a : int = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
a : str = None
| 69 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , a_ : Optional[int]=None , a_ : int=None ):
"""simple docstring"""
__snake_case = list(poly_a or [0] )[:]
__snake_case = list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
__snake_case = len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
__snake_case = len(self.polyB )
# Add 0 to make lengths equal a power of 2
__snake_case = int(
2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )
while len(self.polyA ) < self.c_max_length:
self.polyA.append(0 )
while len(self.polyB ) < self.c_max_length:
self.polyB.append(0 )
# A complex root used for the fourier transform
__snake_case = complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
__snake_case = self.__multiply()
def A ( self : Any , a_ : Optional[Any] ):
"""simple docstring"""
__snake_case = [[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
# Corner case
if len(a_ ) <= 1:
return dft[0]
#
__snake_case = self.c_max_length // 2
while next_ncol > 0:
__snake_case = [[] for i in range(a_ )]
__snake_case = self.root**next_ncol
# First half of next step
__snake_case = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(a_ ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
__snake_case = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(a_ ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
__snake_case = new_dft
__snake_case = next_ncol // 2
return dft[0]
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.__dft("A" )
__snake_case = self.__dft("B" )
__snake_case = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
del dft_a
del dft_b
# Corner Case
if len(inverce_c[0] ) <= 1:
return inverce_c[0]
# Inverse DFT
__snake_case = 2
while next_ncol <= self.c_max_length:
__snake_case = [[] for i in range(a_ )]
__snake_case = self.root ** (next_ncol // 2)
__snake_case = 1
# First half of next step
for j in range(self.c_max_length // next_ncol ):
for i in range(next_ncol // 2 ):
# Even positions
new_inverse_c[i].append(
(
inverce_c[i][j]
+ inverce_c[i][j + self.c_max_length // next_ncol]
)
/ 2 )
# Odd positions
new_inverse_c[i + next_ncol // 2].append(
(
inverce_c[i][j]
- inverce_c[i][j + self.c_max_length // next_ncol]
)
/ (2 * current_root) )
current_root *= root
# Update
__snake_case = new_inverse_c
next_ncol *= 2
# Unpack
__snake_case = [round(x[0].real , 8 ) + round(x[0].imag , 8 ) * 1j for x in inverce_c]
# Remove leading 0's
while inverce_c[-1] == 0:
inverce_c.pop()
return inverce_c
def __str__( self : Optional[int] ):
"""simple docstring"""
__snake_case = "A = " + " + ".join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
__snake_case = "B = " + " + ".join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
__snake_case = "A*B = " + " + ".join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.product ) )
return f'''{a}\n{b}\n{c}'''
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | 1 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _lowerCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
lowerCAmelCase = 'EncodecFeatureExtractor'
lowerCAmelCase = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
"""simple docstring"""
super().__init__(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowerCAmelCase = self.feature_extractor
lowerCAmelCase = False
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE : str=None , SCREAMING_SNAKE_CASE : Union[str, Any]=None , SCREAMING_SNAKE_CASE : Union[str, Any]=True ) -> str:
"""simple docstring"""
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE , language=SCREAMING_SNAKE_CASE , no_timestamps=SCREAMING_SNAKE_CASE )
def __call__( self : Optional[Any] , *SCREAMING_SNAKE_CASE : Tuple , **SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
lowerCAmelCase = kwargs.pop("audio" , SCREAMING_SNAKE_CASE )
lowerCAmelCase = kwargs.pop("sampling_rate" , SCREAMING_SNAKE_CASE )
lowerCAmelCase = kwargs.pop("text" , SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase = args[0]
lowerCAmelCase = 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 text is not None:
lowerCAmelCase = self.tokenizer(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
if audio is not None:
lowerCAmelCase = self.feature_extractor(SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE , sampling_rate=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
lowerCAmelCase = audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
lowerCAmelCase = audio_inputs["padding_mask"]
return inputs
def __A ( self : Dict , *SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
lowerCAmelCase = kwargs.pop("audio" , SCREAMING_SNAKE_CASE )
lowerCAmelCase = kwargs.pop("padding_mask" , SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase = args[0]
lowerCAmelCase = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE , padding_mask=SCREAMING_SNAKE_CASE )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __A ( self : Dict , *SCREAMING_SNAKE_CASE : Optional[int] , **SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def __A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional = None ) -> List[np.ndarray]:
"""simple docstring"""
lowerCAmelCase = to_numpy(SCREAMING_SNAKE_CASE )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE )
lowerCAmelCase = to_numpy(SCREAMING_SNAKE_CASE )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
lowerCAmelCase = seq_len - padding_mask.shape[-1]
lowerCAmelCase = 1 - self.feature_extractor.padding_value
lowerCAmelCase = np.pad(SCREAMING_SNAKE_CASE , ((0, 0), (0, difference)) , "constant" , constant_values=SCREAMING_SNAKE_CASE )
lowerCAmelCase = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE ):
lowerCAmelCase = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
lowerCAmelCase = sliced_audio.reshape(SCREAMING_SNAKE_CASE , -1 )
return audio_values
| 716 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Tuple , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[str]=1_3 , SCREAMING_SNAKE_CASE : Optional[Any]=7 , SCREAMING_SNAKE_CASE : Any=True , SCREAMING_SNAKE_CASE : List[Any]=True , SCREAMING_SNAKE_CASE : Union[str, Any]=True , SCREAMING_SNAKE_CASE : Optional[int]=True , SCREAMING_SNAKE_CASE : Optional[int]=9_9 , SCREAMING_SNAKE_CASE : Dict=3_2 , SCREAMING_SNAKE_CASE : Dict=5 , SCREAMING_SNAKE_CASE : Optional[int]=4 , SCREAMING_SNAKE_CASE : Optional[Any]=3_7 , SCREAMING_SNAKE_CASE : int="gelu" , SCREAMING_SNAKE_CASE : Any=0.1 , SCREAMING_SNAKE_CASE : int=0.1 , SCREAMING_SNAKE_CASE : Dict=5_1_2 , SCREAMING_SNAKE_CASE : Union[str, Any]=1_6 , SCREAMING_SNAKE_CASE : Dict=2 , SCREAMING_SNAKE_CASE : List[Any]=0.0_2 , SCREAMING_SNAKE_CASE : Optional[int]=4 , ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_attention_mask
lowerCAmelCase = use_token_type_ids
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
lowerCAmelCase = num_choices
def __A ( self : List[str] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase = None
if self.use_attention_mask:
lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase = None
if self.use_token_type_ids:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase = RobertaConfig(
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=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = config_and_inputs
lowerCAmelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def __A ( self : List[Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = config_and_inputs
lowerCAmelCase = True
lowerCAmelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class _lowerCAmelCase ( UpperCamelCase_ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase = FlaxRobertaModelTester(self )
@slow
def __A ( self : str ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowerCAmelCase = model_class_name.from_pretrained("roberta-base" , from_pt=SCREAMING_SNAKE_CASE )
lowerCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
| 159 | 0 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _A( UpperCamelCase__ : int , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
with open(UpperCamelCase__ ) as metadata_file:
__lowercase = json.load(UpperCamelCase__ )
__lowercase = LukeConfig(use_entity_aware_attention=UpperCamelCase__ , **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
__lowercase = torch.load(UpperCamelCase__ , map_location='''cpu''' )
# Load the entity vocab file
__lowercase = load_entity_vocab(UpperCamelCase__ )
__lowercase = RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
__lowercase = AddedToken('''<ent>''' , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ )
__lowercase = AddedToken('''<ent2>''' , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__ , LukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) , '''w''' ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
__lowercase = LukeTokenizer.from_pretrained(UpperCamelCase__ )
# Initialize the embeddings of the special tokens
__lowercase = state_dict['''embeddings.word_embeddings.weight''']
__lowercase = word_emb[tokenizer.convert_tokens_to_ids(['''@'''] )[0]].unsqueeze(0 )
__lowercase = word_emb[tokenizer.convert_tokens_to_ids(['''#'''] )[0]].unsqueeze(0 )
__lowercase = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__lowercase = F'encoder.layer.{layer_index}.attention.self.'
__lowercase = state_dict[prefix + matrix_name]
__lowercase = state_dict[prefix + matrix_name]
__lowercase = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__lowercase = state_dict['''entity_embeddings.entity_embeddings.weight''']
__lowercase = entity_emb[entity_vocab['''[MASK]''']]
__lowercase = LukeModel(config=UpperCamelCase__ ).eval()
__lowercase , __lowercase = model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
if not (len(UpperCamelCase__ ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(F'Missing keys {", ".join(UpperCamelCase__ )}. Expected only missing embeddings.position_ids' )
if not (all(key.startswith('''entity_predictions''' ) or key.startswith('''lm_head''' ) for key in unexpected_keys )):
raise ValueError(
'''Unexpected keys'''
F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}' )
# Check outputs
__lowercase = LukeTokenizer.from_pretrained(UpperCamelCase__ , task='''entity_classification''' )
__lowercase = (
'''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
)
__lowercase = (39, 42)
__lowercase = tokenizer(UpperCamelCase__ , entity_spans=[span] , add_prefix_space=UpperCamelCase__ , return_tensors='''pt''' )
__lowercase = model(**UpperCamelCase__ )
# Verify word hidden states
if model_size == "large":
__lowercase = torch.Size((1, 42, 1024) )
__lowercase = torch.tensor(
[[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
else: # base
__lowercase = torch.Size((1, 42, 768) )
__lowercase = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCamelCase__ , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
__lowercase = torch.Size((1, 1, 1024) )
__lowercase = torch.tensor([[0.0466, -0.0106, -0.0179]] )
else: # base
__lowercase = torch.Size((1, 1, 768) )
__lowercase = torch.tensor([[0.1457, 0.1044, 0.0174]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
F' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , UpperCamelCase__ , atol=1e-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(UpperCamelCase__ ) )
model.save_pretrained(UpperCamelCase__ )
def _A( UpperCamelCase__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__lowercase = {}
with open(UpperCamelCase__ , '''r''' , encoding='''utf-8''' ) as f:
for index, line in enumerate(UpperCamelCase__ ):
__lowercase , __lowercase = line.rstrip().split('''\t''' )
__lowercase = index
return entity_vocab
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
UpperCAmelCase__ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 332 |
import unittest
from knapsack import knapsack as k
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__lowercase = 0
__lowercase = [0]
__lowercase = [0]
__lowercase = len(lowerCamelCase__ )
self.assertEqual(k.knapsack(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , 0 )
__lowercase = [60]
__lowercase = [10]
__lowercase = len(lowerCamelCase__ )
self.assertEqual(k.knapsack(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , 0 )
def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = 3
__lowercase = [1, 2, 3]
__lowercase = [3, 2, 1]
__lowercase = len(lowerCamelCase__ )
self.assertEqual(k.knapsack(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , 5 )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
"""simple docstring"""
__lowercase = 50
__lowercase = [60, 100, 120]
__lowercase = [10, 20, 30]
__lowercase = len(lowerCamelCase__ )
self.assertEqual(k.knapsack(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , 220 )
if __name__ == "__main__":
unittest.main()
| 332 | 1 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
_UpperCAmelCase = """true"""
def __magic_name__ ( lowercase , lowercase=82 , lowercase=16 ):
set_seed(42 )
SCREAMING_SNAKE_CASE_: List[str] =RegressionModel()
SCREAMING_SNAKE_CASE_: Dict =deepcopy(lowercase )
SCREAMING_SNAKE_CASE_: str =RegressionDataset(length=lowercase )
SCREAMING_SNAKE_CASE_: Dict =DataLoader(lowercase , batch_size=lowercase )
model.to(accelerator.device )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] =accelerator.prepare(lowercase , lowercase )
return model, ddp_model, dataloader
def __magic_name__ ( lowercase , lowercase=False ):
SCREAMING_SNAKE_CASE_: Optional[int] =AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
SCREAMING_SNAKE_CASE_: List[str] =load_dataset("""glue""" , """mrpc""" , split="""validation""" )
def tokenize_function(lowercase ):
SCREAMING_SNAKE_CASE_: int =tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase , max_length=lowercase )
return outputs
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE_: Optional[Any] =dataset.map(
lowercase , batched=lowercase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
SCREAMING_SNAKE_CASE_: Dict =tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase ):
if use_longest:
return tokenizer.pad(lowercase , padding="""longest""" , return_tensors="""pt""" )
return tokenizer.pad(lowercase , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return DataLoader(lowercase , shuffle=lowercase , collate_fn=lowercase , batch_size=16 )
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Optional[int] =Accelerator(dispatch_batches=lowercase , split_batches=lowercase )
SCREAMING_SNAKE_CASE_: str =get_dataloader(lowercase , not dispatch_batches )
SCREAMING_SNAKE_CASE_: Optional[Any] =AutoModelForSequenceClassification.from_pretrained(
"""hf-internal-testing/mrpc-bert-base-cased""" , return_dict=lowercase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =accelerator.prepare(lowercase , lowercase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def __magic_name__ ( lowercase , lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Dict =[]
for batch in dataloader:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict =batch.values()
with torch.no_grad():
SCREAMING_SNAKE_CASE_: Union[str, Any] =model(lowercase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Any =[], []
for logit, targ in logits_and_targets:
logits.append(lowercase )
targs.append(lowercase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =torch.cat(lowercase ), torch.cat(lowercase )
return logits, targs
def __magic_name__ ( lowercase , lowercase=82 , lowercase=False , lowercase=False , lowercase=16 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =get_basic_setup(lowercase , lowercase , lowercase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: int =generate_predictions(lowercase , lowercase , lowercase )
assert (
len(lowercase ) == num_samples
), f'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(lowercase )}'''
def __magic_name__ ( lowercase = False , lowercase = False ):
SCREAMING_SNAKE_CASE_: Dict =evaluate.load("""glue""" , """mrpc""" )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] =get_mrpc_setup(lowercase , lowercase )
# First do baseline
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =setup["""no"""]
model.to(lowercase )
model.eval()
for batch in dataloader:
batch.to(lowercase )
with torch.inference_mode():
SCREAMING_SNAKE_CASE_: Optional[int] =model(**lowercase )
SCREAMING_SNAKE_CASE_: int =outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=lowercase , references=batch["""labels"""] )
SCREAMING_SNAKE_CASE_: Optional[int] =metric.compute()
# Then do distributed
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str =setup["""ddp"""]
model.eval()
for batch in dataloader:
with torch.inference_mode():
SCREAMING_SNAKE_CASE_: Optional[Any] =model(**lowercase )
SCREAMING_SNAKE_CASE_: Any =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE_: Dict =batch["""labels"""]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=lowercase , references=lowercase )
SCREAMING_SNAKE_CASE_: str =metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: Optional[Any] =Accelerator(split_batches=lowercase , dispatch_batches=lowercase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("""**Testing gather_for_metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(lowercase , lowercase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test torch metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
SCREAMING_SNAKE_CASE_: Dict =Accelerator(split_batches=lowercase , dispatch_batches=lowercase )
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(lowercase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test last batch is not dropped when perfectly divisible**""" )
SCREAMING_SNAKE_CASE_: List[str] =Accelerator()
test_torch_metrics(lowercase , 512 )
accelerator.state._reset_state()
def __magic_name__ ( lowercase ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] =input_list[i + 1], input_list[i]
# swapping if elements not in order
SCREAMING_SNAKE_CASE_: Tuple =False
for i in range(1 , len(lowercase ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: str =input_list[i + 1], input_list[i]
# swapping if elements not in order
SCREAMING_SNAKE_CASE_: str =False
return input_list
if __name__ == "__main__":
print("""Enter list to be sorted""")
_UpperCAmelCase = [int(x) for x in input().split()]
# inputing elements of the list in one line
_UpperCAmelCase = odd_even_sort(input_list)
print("""The sorted list is""")
print(sorted_list)
| 36 | 1 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
A_ : Optional[Any] = getLogger(__name__)
A_ : List[str] = 'cuda' if torch.cuda.is_available() else 'cpu'
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 8 , SCREAMING_SNAKE_CASE = DEFAULT_DEVICE , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE="summarization" , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE , ) -> Dict:
'''simple docstring'''
__UpperCAmelCase = Path(SCREAMING_SNAKE_CASE ).open('''w''' , encoding='''utf-8''' )
__UpperCAmelCase = str(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE ).to(SCREAMING_SNAKE_CASE )
if fpaa:
__UpperCAmelCase = model.half()
__UpperCAmelCase = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}''' ) # if this is wrong, check config.model_type.
__UpperCAmelCase = time.time()
# update config with task specific params
use_task_specific_params(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if prefix is None:
__UpperCAmelCase = prefix or getattr(model.config , '''prefix''' , '''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) ):
__UpperCAmelCase = [prefix + text for text in examples_chunk]
__UpperCAmelCase = tokenizer(SCREAMING_SNAKE_CASE , return_tensors='''pt''' , truncation=SCREAMING_SNAKE_CASE , padding='''longest''' ).to(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **SCREAMING_SNAKE_CASE , )
__UpperCAmelCase = tokenizer.batch_decode(SCREAMING_SNAKE_CASE , skip_special_tokens=SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
__UpperCAmelCase = int(time.time() - start_time ) # seconds
__UpperCAmelCase = len(SCREAMING_SNAKE_CASE )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def __a ( ) -> Tuple:
'''simple docstring'''
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def __a ( SCREAMING_SNAKE_CASE=True ) -> int:
'''simple docstring'''
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''model_name''' , type=SCREAMING_SNAKE_CASE , help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' , type=SCREAMING_SNAKE_CASE , help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' , type=SCREAMING_SNAKE_CASE , help='''where to save summaries''' )
parser.add_argument('''--reference_path''' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , default='''metrics.json''' , help='''where to save metrics''' )
parser.add_argument('''--device''' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' , type=SCREAMING_SNAKE_CASE , default='''summarization''' , help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' , type=SCREAMING_SNAKE_CASE , default=8 , required=SCREAMING_SNAKE_CASE , help='''batch size''' )
parser.add_argument(
'''--n_obs''' , type=SCREAMING_SNAKE_CASE , default=-1 , required=SCREAMING_SNAKE_CASE , help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' , action='''store_true''' )
parser.add_argument('''--dump-args''' , action='''store_true''' , help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' , nargs='''?''' , type=SCREAMING_SNAKE_CASE , const=datetime_now() , help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__UpperCAmelCase , __UpperCAmelCase = parser.parse_known_args()
__UpperCAmelCase = parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE )
if parsed_args and verbose:
print(f'''parsed the following generate kwargs: {parsed_args}''' )
__UpperCAmelCase = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__UpperCAmelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.''' )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
__UpperCAmelCase = generate_summaries_or_translations(
SCREAMING_SNAKE_CASE , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **SCREAMING_SNAKE_CASE , )
if args.reference_path is None:
return {}
# Compute scores
__UpperCAmelCase = calculate_bleu if '''translation''' in args.task else calculate_rouge
__UpperCAmelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
__UpperCAmelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(SCREAMING_SNAKE_CASE )]
__UpperCAmelCase = score_fn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
scores.update(SCREAMING_SNAKE_CASE )
if args.dump_args:
scores.update(SCREAMING_SNAKE_CASE )
if args.info:
__UpperCAmelCase = args.info
if verbose:
print(SCREAMING_SNAKE_CASE )
if args.score_path is not None:
json.dump(SCREAMING_SNAKE_CASE , open(args.score_path , '''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 303 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 303 | 1 |
from PIL import Image
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = image.size
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = image.load()
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(__lowerCamelCase ):
for i in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = 2_55 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
__UpperCAmelCase = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| 597 |
from PIL import Image
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = image.size
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = image.load()
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(__lowerCamelCase ):
for i in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = 2_55 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
__UpperCAmelCase = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| 597 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class __magic_name__ :
_lowerCAmelCase = 42
# setable values
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = None
@classmethod
def _A ( cls : List[str] , lowerCamelCase__ : CommonSchedulerState , lowerCamelCase__ : jnp.ndarray , lowerCamelCase__ : jnp.ndarray ):
return cls(common=lowerCamelCase__ , init_noise_sigma=lowerCamelCase__ , timesteps=lowerCamelCase__ )
@dataclass
class __magic_name__ ( snake_case ):
_lowerCAmelCase = 42
class __magic_name__ ( snake_case, snake_case ):
_lowerCAmelCase = [e.name for e in FlaxKarrasDiffusionSchedulers]
_lowerCAmelCase = 42
@property
def _A ( self : Union[str, Any] ):
return True
@register_to_config
def __init__( self : Dict , lowerCamelCase__ : int = 1_0_0_0 , lowerCamelCase__ : float = 0.0_0_0_1 , lowerCamelCase__ : float = 0.0_2 , lowerCamelCase__ : str = "linear" , lowerCamelCase__ : Optional[jnp.ndarray] = None , lowerCamelCase__ : str = "fixed_small" , lowerCamelCase__ : bool = True , lowerCamelCase__ : str = "epsilon" , lowerCamelCase__ : jnp.dtype = jnp.floataa , ):
lowerCAmelCase : Dict = dtype
def _A ( self : List[Any] , lowerCamelCase__ : Optional[CommonSchedulerState] = None ):
if common is None:
lowerCAmelCase : List[Any] = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
lowerCAmelCase : List[str] = jnp.array(1.0 , dtype=self.dtype )
lowerCAmelCase : Union[str, Any] = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=lowerCamelCase__ , init_noise_sigma=lowerCamelCase__ , timesteps=lowerCamelCase__ , )
def _A ( self : List[Any] , lowerCamelCase__ : DDPMSchedulerState , lowerCamelCase__ : jnp.ndarray , lowerCamelCase__ : Optional[int] = None ):
return sample
def _A ( self : Dict , lowerCamelCase__ : DDPMSchedulerState , lowerCamelCase__ : int , lowerCamelCase__ : Tuple = () ):
lowerCAmelCase : Tuple = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
lowerCAmelCase : Tuple = (jnp.arange(0 , lowerCamelCase__ ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=lowerCamelCase__ , timesteps=lowerCamelCase__ , )
def _A ( self : List[str] , lowerCamelCase__ : DDPMSchedulerState , lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[int]=None , lowerCamelCase__ : Union[str, Any]=None ):
lowerCAmelCase : Optional[Any] = state.common.alphas_cumprod[t]
lowerCAmelCase : List[str] = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowerCAmelCase : Optional[Any] = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
lowerCAmelCase : Dict = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
lowerCAmelCase : Tuple = jnp.clip(lowerCamelCase__ , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
lowerCAmelCase : List[str] = jnp.log(jnp.clip(lowerCamelCase__ , a_min=1E-20 ) )
elif variance_type == "fixed_large":
lowerCAmelCase : int = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
lowerCAmelCase : List[Any] = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
lowerCAmelCase : Optional[int] = variance
lowerCAmelCase : Dict = state.common.betas[t]
lowerCAmelCase : Tuple = (predicted_variance + 1) / 2
lowerCAmelCase : Optional[int] = frac * max_log + (1 - frac) * min_log
return variance
def _A ( self : List[str] , lowerCamelCase__ : DDPMSchedulerState , lowerCamelCase__ : jnp.ndarray , lowerCamelCase__ : int , lowerCamelCase__ : jnp.ndarray , lowerCamelCase__ : Optional[jax.random.KeyArray] = None , lowerCamelCase__ : bool = True , ):
lowerCAmelCase : Optional[int] = timestep
if key is None:
lowerCAmelCase : Optional[Any] = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
lowerCAmelCase , lowerCAmelCase : Dict = jnp.split(lowerCamelCase__ , sample.shape[1] , axis=1 )
else:
lowerCAmelCase : Optional[int] = None
# 1. compute alphas, betas
lowerCAmelCase : List[Any] = state.common.alphas_cumprod[t]
lowerCAmelCase : Dict = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
lowerCAmelCase : int = 1 - alpha_prod_t
lowerCAmelCase : List[Any] = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowerCAmelCase : Any = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowerCAmelCase : Tuple = model_output
elif self.config.prediction_type == "v_prediction":
lowerCAmelCase : List[str] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
''' for the FlaxDDPMScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowerCAmelCase : int = jnp.clip(lowerCamelCase__ , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowerCAmelCase : Union[str, Any] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
lowerCAmelCase : int = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowerCAmelCase : Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
lowerCAmelCase : Optional[int] = jax.random.split(lowerCamelCase__ , num=1 )
lowerCAmelCase : str = jax.random.normal(lowerCamelCase__ , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(lowerCamelCase__ , lowerCamelCase__ , predicted_variance=lowerCamelCase__ ) ** 0.5) * noise
lowerCAmelCase : int = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
lowerCAmelCase : List[Any] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=lowerCamelCase__ , state=lowerCamelCase__ )
def _A ( self : List[str] , lowerCamelCase__ : DDPMSchedulerState , lowerCamelCase__ : jnp.ndarray , lowerCamelCase__ : jnp.ndarray , lowerCamelCase__ : jnp.ndarray , ):
return add_noise_common(state.common , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def _A ( self : int , lowerCamelCase__ : DDPMSchedulerState , lowerCamelCase__ : jnp.ndarray , lowerCamelCase__ : jnp.ndarray , lowerCamelCase__ : jnp.ndarray , ):
return get_velocity_common(state.common , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def __len__( self : Any ):
return self.config.num_train_timesteps
| 348 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class __magic_name__ :
def __init__( self : Any , lowerCamelCase__ : List[str] , lowerCamelCase__ : str=None , lowerCamelCase__ : Optional[int]=None , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : List[str]="resnet50" , lowerCamelCase__ : str=3 , lowerCamelCase__ : List[Any]=3_2 , lowerCamelCase__ : Dict=3 , lowerCamelCase__ : int=True , lowerCamelCase__ : Dict=True , ):
lowerCAmelCase : Tuple = parent
lowerCAmelCase : List[Any] = out_indices if out_indices is not None else [4]
lowerCAmelCase : Optional[Any] = stage_names
lowerCAmelCase : List[Any] = out_features
lowerCAmelCase : Optional[Any] = backbone
lowerCAmelCase : str = batch_size
lowerCAmelCase : List[Any] = image_size
lowerCAmelCase : List[str] = num_channels
lowerCAmelCase : int = use_pretrained_backbone
lowerCAmelCase : Optional[Any] = is_training
def _A ( self : Any ):
lowerCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase : Union[str, Any] = self.get_config()
return config, pixel_values
def _A ( self : int ):
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def _A ( self : Optional[Any] , lowerCamelCase__ : int , lowerCamelCase__ : str ):
lowerCAmelCase : List[str] = TimmBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
lowerCAmelCase : Optional[Any] = model(lowerCamelCase__ )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 1_4, 1_4) , )
def _A ( self : int ):
lowerCAmelCase : Any = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase : str = config_and_inputs
lowerCAmelCase : Optional[int] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class __magic_name__ ( snake_case, snake_case, snake_case, unittest.TestCase ):
_lowerCAmelCase = (TimmBackbone,) if is_torch_available() else ()
_lowerCAmelCase = {"feature-extraction": TimmBackbone} if is_torch_available() else {}
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def _A ( self : Optional[Any] ):
lowerCAmelCase : Optional[int] = TimmBackboneModelTester(self )
lowerCAmelCase : Tuple = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def _A ( self : Optional[Any] ):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _A ( self : int ):
lowerCAmelCase : Any = '''resnet18'''
lowerCAmelCase : Optional[Any] = '''microsoft/resnet-18'''
lowerCAmelCase : Union[str, Any] = AutoBackbone.from_pretrained(lowerCamelCase__ , use_timm_backbone=lowerCamelCase__ )
lowerCAmelCase : Dict = AutoBackbone.from_pretrained(lowerCamelCase__ )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
lowerCAmelCase : Optional[int] = AutoBackbone.from_pretrained(lowerCamelCase__ , use_timm_backbone=lowerCamelCase__ , out_indices=[1, 2, 3] )
lowerCAmelCase : Optional[int] = AutoBackbone.from_pretrained(lowerCamelCase__ , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking''' )
def _A ( self : List[str] ):
pass
@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''' )
def _A ( self : Optional[int] ):
pass
@unittest.skip('''TimmBackbone initialization is managed on the timm side''' )
def _A ( self : int ):
pass
@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''' )
def _A ( self : str ):
pass
@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''' )
def _A ( self : Dict ):
pass
@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint''' )
def _A ( self : List[Any] ):
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def _A ( self : List[str] ):
pass
@unittest.skip('''model weights aren\'t tied in TimmBackbone.''' )
def _A ( self : Optional[int] ):
pass
@unittest.skip('''model weights aren\'t tied in TimmBackbone.''' )
def _A ( self : List[Any] ):
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def _A ( self : int ):
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def _A ( self : Tuple ):
pass
@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.''' )
def _A ( self : List[Any] ):
pass
@unittest.skip('''TimmBackbone doesn\'t support output_attentions.''' )
def _A ( self : Tuple ):
pass
@unittest.skip('''Safetensors is not supported by timm.''' )
def _A ( self : Tuple ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def _A ( self : int ):
pass
def _A ( self : Union[str, Any] ):
lowerCAmelCase , lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase : Union[str, Any] = model_class(lowerCamelCase__ )
lowerCAmelCase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase : Optional[int] = [*signature.parameters.keys()]
lowerCAmelCase : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def _A ( self : int ):
lowerCAmelCase , lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase : Tuple = True
lowerCAmelCase : Any = self.has_attentions
# no need to test all models as different heads yield the same functionality
lowerCAmelCase : Dict = self.all_model_classes[0]
lowerCAmelCase : Optional[int] = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
lowerCAmelCase : Any = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase : int = model(**lowerCamelCase__ )
lowerCAmelCase : Optional[Any] = outputs[0][-1]
# Encoder-/Decoder-only models
lowerCAmelCase : Any = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
lowerCAmelCase : Optional[Any] = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=lowerCamelCase__ )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def _A ( self : Tuple ):
lowerCAmelCase , lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase : Union[str, Any] = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowerCAmelCase : Optional[int] = model(**lowerCamelCase__ )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
lowerCAmelCase : str = copy.deepcopy(lowerCamelCase__ )
lowerCAmelCase : int = None
lowerCAmelCase : Optional[int] = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowerCAmelCase : Any = model(**lowerCamelCase__ )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
lowerCAmelCase : Optional[Any] = copy.deepcopy(lowerCamelCase__ )
lowerCAmelCase : Any = False
lowerCAmelCase : Dict = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
lowerCAmelCase : List[Any] = model(**lowerCamelCase__ )
| 348 | 1 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _lowerCamelCase ( A_ : Any ) -> str:
'''simple docstring'''
return 1 / (1 + np.exp(-z ))
def _lowerCamelCase ( A_ : Optional[Any] , A_ : int ) -> str:
'''simple docstring'''
return (-y * np.log(A_ ) - (1 - y) * np.log(1 - h )).mean()
def _lowerCamelCase ( A_ : Any , A_ : Union[str, Any] , A_ : str ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : Dict =np.dot(A_ , A_ )
return np.sum(y * scores - np.log(1 + np.exp(A_ ) ) )
def _lowerCamelCase ( A_ : Optional[int] , A_ : List[str] , A_ : Any , A_ : Dict=7_0_0_0_0 ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : Tuple =np.zeros(x.shape[1] )
for iterations in range(A_ ):
UpperCamelCase__ : List[Any] =np.dot(A_ , A_ )
UpperCamelCase__ : Optional[int] =sigmoid_function(A_ )
UpperCamelCase__ : Optional[Any] =np.dot(x.T , h - y ) / y.size
UpperCamelCase__ : Optional[int] =theta - alpha * gradient # updating the weights
UpperCamelCase__ : Union[str, Any] =np.dot(A_ , A_ )
UpperCamelCase__ : Any =sigmoid_function(A_ )
UpperCamelCase__ : Dict =cost_function(A_ , A_ )
if iterations % 1_0_0 == 0:
print(f'''loss: {j} \t''' ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
__UpperCAmelCase = datasets.load_iris()
__UpperCAmelCase = iris.data[:, :2]
__UpperCAmelCase = (iris.target != 0) * 1
__UpperCAmelCase = 0.1
__UpperCAmelCase = logistic_reg(alpha, x, y, max_iterations=7_0000)
print("""theta: """, theta) # printing the theta i.e our weights vector
def _lowerCamelCase ( A_ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return sigmoid_function(
np.dot(A_ , A_ ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
((__UpperCAmelCase) , (__UpperCAmelCase)) = (x[:, 0].min(), x[:, 0].max())
((__UpperCAmelCase) , (__UpperCAmelCase)) = (x[:, 1].min(), x[:, 1].max())
((__UpperCAmelCase) , (__UpperCAmelCase)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
__UpperCAmelCase = np.c_[xxa.ravel(), xxa.ravel()]
__UpperCAmelCase = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")
plt.legend()
plt.show()
| 582 |
from math import factorial
__UpperCAmelCase = {str(digit): factorial(digit) for digit in range(10)}
def _lowerCamelCase ( A_ : int ) -> int:
'''simple docstring'''
if not isinstance(A_ , A_ ):
raise TypeError("Parameter number must be int" )
if number < 0:
raise ValueError("Parameter number must be greater than or equal to 0" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(A_ ) )
def _lowerCamelCase ( A_ : int = 6_0 , A_ : int = 1_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
if not isinstance(A_ , A_ ) or not isinstance(A_ , A_ ):
raise TypeError("Parameters chain_length and number_limit must be int" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"Parameters chain_length and number_limit must be greater than 0" )
# the counter for the chains with the exact desired length
UpperCamelCase__ : str =0
# the cached sizes of the previous chains
UpperCamelCase__ : dict[int, int] ={}
for start_chain_element in range(1 , A_ ):
# The temporary set will contain the elements of the chain
UpperCamelCase__ : Any =set()
UpperCamelCase__ : Optional[Any] =0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
UpperCamelCase__ : str =start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(A_ )
chain_set_length += 1
UpperCamelCase__ : Tuple =digit_factorial_sum(A_ )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
UpperCamelCase__ : List[str] =chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{solution()}""")
| 582 | 1 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
A_ : List[Any] = logging.get_logger(__name__)
logging.set_verbosity_info()
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
if "xprophetnet" in prophetnet_checkpoint_path:
UpperCamelCase_: Union[str, Any] = XLMProphetNetForConditionalGenerationOld.from_pretrained(__SCREAMING_SNAKE_CASE )
UpperCamelCase_ ,UpperCamelCase_: str = XLMProphetNetForConditionalGeneration.from_pretrained(
__SCREAMING_SNAKE_CASE , output_loading_info=__SCREAMING_SNAKE_CASE )
else:
UpperCamelCase_: List[Any] = ProphetNetForConditionalGenerationOld.from_pretrained(__SCREAMING_SNAKE_CASE )
UpperCamelCase_ ,UpperCamelCase_: List[Any] = ProphetNetForConditionalGeneration.from_pretrained(
__SCREAMING_SNAKE_CASE , output_loading_info=__SCREAMING_SNAKE_CASE )
UpperCamelCase_: int = ['key_proj', 'value_proj', 'query_proj']
UpperCamelCase_: List[Any] = {
'self_attn': 'ngram_self_attn',
'cross_attn': 'encoder_attn',
'cross_attn_layer_norm': 'encoder_attn_layer_norm',
'feed_forward_layer_norm': 'final_layer_norm',
'feed_forward': '',
'intermediate': 'fc1',
'output': 'fc2',
'key_proj': 'k_proj',
'query_proj': 'q_proj',
'value_proj': 'v_proj',
'word_embeddings': 'embed_tokens',
'embeddings_layer_norm': 'emb_layer_norm',
'relative_pos_embeddings': 'relative_linear',
'ngram_embeddings': 'ngram_input_embed',
'position_embeddings': 'embed_positions',
}
for key in loading_info["missing_keys"]:
UpperCamelCase_: int = key.split('.' )
if attributes[0] == "lm_head":
UpperCamelCase_: List[str] = prophet
UpperCamelCase_: Union[str, Any] = prophet_old
else:
UpperCamelCase_: Dict = prophet.prophetnet
UpperCamelCase_: List[Any] = prophet_old.model
UpperCamelCase_: int = False
for attribute in attributes:
if attribute in mapping:
UpperCamelCase_: Optional[int] = mapping[attribute]
if not hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) and len(__SCREAMING_SNAKE_CASE ) > 0:
UpperCamelCase_: Dict = attribute
elif hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
UpperCamelCase_: Union[str, Any] = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
UpperCamelCase_: int = old_model.weight
logger.info(F'''{attribute} is initialized.''' )
UpperCamelCase_: Tuple = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
UpperCamelCase_: Optional[Any] = old_model.bias
logger.info(F'''{attribute} is initialized''' )
UpperCamelCase_: Dict = True
break
elif attribute in special_keys and hasattr(__SCREAMING_SNAKE_CASE , 'in_proj_weight' ):
UpperCamelCase_: Optional[int] = old_model.in_proj_weight.shape[0] // 3
UpperCamelCase_: Tuple = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
UpperCamelCase_: List[str] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
UpperCamelCase_: int = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
UpperCamelCase_: Tuple = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
UpperCamelCase_: List[Any] = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
UpperCamelCase_: List[Any] = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
UpperCamelCase_: Union[str, Any] = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
UpperCamelCase_: Dict = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
UpperCamelCase_: Optional[int] = nn.Parameter(old_model.embed_positions.weight[:5_1_2, :] )
UpperCamelCase_: Optional[Any] = True
break
if attribute.isdigit():
UpperCamelCase_: Tuple = model[int(__SCREAMING_SNAKE_CASE )]
UpperCamelCase_: Tuple = old_model[int(__SCREAMING_SNAKE_CASE )]
else:
UpperCamelCase_: Optional[Any] = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if old_attribute == "":
UpperCamelCase_: int = old_model
else:
if not hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError(F'''{old_model} does not have {old_attribute}''' )
UpperCamelCase_: int = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if not is_key_init:
raise ValueError(F'''{key} was not correctly initialized!''' )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
prophet.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
A_ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A_ : Union[str, Any] = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path) | 57 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
_UpperCamelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase = self.block_out_channels[i]
lowercase = self.block_out_channels[i + 1]
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = blocks
lowercase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case ):
lowercase = self.conv_in(snake_case )
lowercase = nn.silu(snake_case )
for block in self.blocks:
lowercase = block(snake_case )
lowercase = nn.silu(snake_case )
lowercase = self.conv_out(snake_case )
return embedding
@flax_register_to_config
class A_ ( nn.Module , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = 32
_UpperCamelCase : int = 4
_UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCamelCase : Union[bool, Tuple[bool]] = False
_UpperCamelCase : Tuple[int] = (320, 640, 1280, 1280)
_UpperCamelCase : int = 2
_UpperCamelCase : Union[int, Tuple[int]] = 8
_UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCamelCase : int = 1280
_UpperCamelCase : float = 0.0
_UpperCamelCase : bool = False
_UpperCamelCase : jnp.dtype = jnp.floataa
_UpperCamelCase : bool = True
_UpperCamelCase : int = 0
_UpperCamelCase : str = "rgb"
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# init input tensors
lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase = jnp.ones((1,) , dtype=jnp.intaa )
lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase , lowercase = jax.random.split(snake_case )
lowercase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case )["params"]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.block_out_channels
lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase = self.num_attention_heads or self.attention_head_dim
# input
lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase = []
lowercase = []
lowercase = block_out_channels[0]
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase = output_channel
lowercase = block_out_channels[i]
lowercase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
for _ in range(self.layers_per_block ):
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
if not is_final_block:
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
lowercase = down_blocks
lowercase = controlnet_down_blocks
# mid
lowercase = block_out_channels[-1]
lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ):
lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase = jnp.flip(snake_case , axis=1 )
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase = timesteps.astype(dtype=jnp.floataa )
lowercase = jnp.expand_dims(snake_case , 0 )
lowercase = self.time_proj(snake_case )
lowercase = self.time_embedding(snake_case )
# 2. pre-process
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.conv_in(snake_case )
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.controlnet_cond_embedding(snake_case )
sample += controlnet_cond
# 3. down
lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
lowercase , lowercase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
lowercase , lowercase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
# 5. contronet blocks
lowercase = ()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks ):
lowercase = controlnet_block(snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase = controlnet_down_block_res_samples
lowercase = self.controlnet_mid_block(snake_case )
# 6. scaling
lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case )
| 84 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCAmelCase : int = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC",
"KD 6S 9D TH AD",
"KS 8D 4D 9S 4S", # pair
"8C 4S KH JS 4D", # pair
"QH 8H KD JH 8S", # pair
"KC 4H KS 2H 8D", # pair
"KD 4S KC 3H 8S", # pair
"AH 8S AS KC JH", # pair
"3H 4C 4H 3S 2H", # 2 pairs
"5S 5D 2C KH KH", # 2 pairs
"3C KH 5D 5S KH", # 2 pairs
"AS 3C KH AD KH", # 2 pairs
"7C 7S 3S 7H 5S", # 3 of a kind
"7C 7S KH 2H 7H", # 3 of a kind
"AC KH QH AH AS", # 3 of a kind
"2H 4D 3C AS 5S", # straight (low ace)
"3C 5C 4C 2C 6H", # straight
"6S 8S 7S 5H 9H", # straight
"JS QS 9H TS KH", # straight
"QC KH TS JS AH", # straight (high ace)
"8C 9C 5C 3C TC", # flush
"3S 8S 9S 5S KS", # flush
"4C 5C 9C 8C KC", # flush
"JH 8H AH KH QH", # flush
"3D 2H 3H 2C 2D", # full house
"2H 2C 3S 3H 3D", # full house
"KH KC 3S 3H 3D", # full house
"JC 6H JS JD JH", # 4 of a kind
"JC 7H JS JD JH", # 4 of a kind
"JC KH JS JD JH", # 4 of a kind
"2S AS 4S 5S 3S", # straight flush (low ace)
"2D 6D 3D 4D 5D", # straight flush
"5C 6C 3C 7C 4C", # straight flush
"JH 9H TH KH QH", # straight flush
"JH AH TH KH QH", # royal flush (high ace straight flush)
)
_lowerCAmelCase : List[Any] = (
("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)
_lowerCAmelCase : int = (
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", True),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", False),
("AS 3S 4S 8S 2S", True),
)
_lowerCAmelCase : List[Any] = (
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", False),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", True),
)
_lowerCAmelCase : str = (
("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 1_4]),
("2H 5D 3C AS 5S", False, [1_4, 5, 5, 3, 2]),
("JH QD KC AS TS", False, [1_4, 1_3, 1_2, 1_1, 1_0]),
("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)
_lowerCAmelCase : Any = (
("JH AH TH KH QH", 0),
("JH 9H TH KH QH", 0),
("JC KH JS JD JH", 7),
("KH KC 3S 3H 3D", 6),
("8C 9C 5C 3C TC", 0),
("JS QS 9H TS KH", 0),
("7C 7S KH 2H 7H", 3),
("3C KH 5D 5S KH", 2),
("QH 8H KD JH 8S", 1),
("2D 6D 9D TH 7D", 0),
)
_lowerCAmelCase : List[str] = (
("JH AH TH KH QH", 2_3),
("JH 9H TH KH QH", 2_2),
("JC KH JS JD JH", 2_1),
("KH KC 3S 3H 3D", 2_0),
("8C 9C 5C 3C TC", 1_9),
("JS QS 9H TS KH", 1_8),
("7C 7S KH 2H 7H", 1_7),
("3C KH 5D 5S KH", 1_6),
("QH 8H KD JH 8S", 1_5),
("2D 6D 9D TH 7D", 1_4),
)
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = randrange(len(_lowerCAmelCase ) ), randrange(len(_lowerCAmelCase ) )
UpperCAmelCase__ = ["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
UpperCAmelCase__ , UpperCAmelCase__ = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def lowerCAmelCase ( _lowerCAmelCase : int = 100 ):
"""simple docstring"""
return (generate_random_hand() for _ in range(_lowerCAmelCase ))
@pytest.mark.parametrize("hand, expected" , _lowerCAmelCase )
def lowerCAmelCase ( _lowerCAmelCase : Any , _lowerCAmelCase : Dict ):
"""simple docstring"""
assert PokerHand(_lowerCAmelCase )._is_flush() == expected
@pytest.mark.parametrize("hand, expected" , _lowerCAmelCase )
def lowerCAmelCase ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
assert PokerHand(_lowerCAmelCase )._is_straight() == expected
@pytest.mark.parametrize("hand, expected, card_values" , _lowerCAmelCase )
def lowerCAmelCase ( _lowerCAmelCase : str , _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
UpperCAmelCase__ = PokerHand(_lowerCAmelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("hand, expected" , _lowerCAmelCase )
def lowerCAmelCase ( _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
assert PokerHand(_lowerCAmelCase )._is_same_kind() == expected
@pytest.mark.parametrize("hand, expected" , _lowerCAmelCase )
def lowerCAmelCase ( _lowerCAmelCase : str , _lowerCAmelCase : Dict ):
"""simple docstring"""
assert PokerHand(_lowerCAmelCase )._hand_type == expected
@pytest.mark.parametrize("hand, other, expected" , _lowerCAmelCase )
def lowerCAmelCase ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
assert PokerHand(_lowerCAmelCase ).compare_with(PokerHand(_lowerCAmelCase ) ) == expected
@pytest.mark.parametrize("hand, other, expected" , generate_random_hands() )
def lowerCAmelCase ( _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : str ):
"""simple docstring"""
assert PokerHand(_lowerCAmelCase ).compare_with(PokerHand(_lowerCAmelCase ) ) == expected
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = [PokerHand(_lowerCAmelCase ) for hand in SORTED_HANDS]
UpperCAmelCase__ = poker_hands.copy()
shuffle(_lowerCAmelCase )
UpperCAmelCase__ = chain(sorted(_lowerCAmelCase ) )
for index, hand in enumerate(_lowerCAmelCase ):
assert hand == poker_hands[index]
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = [PokerHand("2D AC 3H 4H 5S" ), PokerHand("2S 3H 4H 5S 6C" )]
pokerhands.sort(reverse=_lowerCAmelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = PokerHand("2C 4S AS 3D 5C" )
UpperCAmelCase__ = True
UpperCAmelCase__ = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = 0
UpperCAmelCase__ = os.path.abspath(os.path.dirname(_lowerCAmelCase ) )
UpperCAmelCase__ = os.path.join(_lowerCAmelCase , "poker_hands.txt" )
with open(_lowerCAmelCase ) as file_hand:
for line in file_hand:
UpperCAmelCase__ = line[:14].strip()
UpperCAmelCase__ = line[15:].strip()
UpperCAmelCase__ , UpperCAmelCase__ = PokerHand(_lowerCAmelCase ), PokerHand(_lowerCAmelCase )
UpperCAmelCase__ = player.compare_with(_lowerCAmelCase )
if output == "Win":
answer += 1
assert answer == 376
| 364 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
_lowerCAmelCase : str = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
_lowerCAmelCase : List[Any] = {
"allenai/led-base-16384": 1_6_3_8_4,
}
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = VOCAB_FILES_NAMES
UpperCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ = LEDTokenizer
UpperCAmelCase_ = ["""input_ids""", """attention_mask"""]
def __init__( self :Dict , lowerCamelCase :Any=None , lowerCamelCase :Dict=None , lowerCamelCase :Dict=None , lowerCamelCase :int="replace" , lowerCamelCase :List[Any]="<s>" , lowerCamelCase :Optional[Any]="</s>" , lowerCamelCase :Optional[Any]="</s>" , lowerCamelCase :int="<s>" , lowerCamelCase :Optional[Any]="<unk>" , lowerCamelCase :Dict="<pad>" , lowerCamelCase :Tuple="<mask>" , lowerCamelCase :str=False , lowerCamelCase :Union[str, Any]=True , **lowerCamelCase :List[str] , ) -> Tuple:
super().__init__(
lowerCamelCase , lowerCamelCase , tokenizer_file=lowerCamelCase , errors=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , unk_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , add_prefix_space=lowerCamelCase , trim_offsets=lowerCamelCase , **lowerCamelCase , )
UpperCAmelCase__ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , lowerCamelCase ) != add_prefix_space:
UpperCAmelCase__ = getattr(lowerCamelCase , pre_tok_state.pop("type" ) )
UpperCAmelCase__ = add_prefix_space
UpperCAmelCase__ = pre_tok_class(**lowerCamelCase )
UpperCAmelCase__ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
UpperCAmelCase__ = "post_processor"
UpperCAmelCase__ = getattr(self.backend_tokenizer , lowerCamelCase , lowerCamelCase )
if tokenizer_component_instance:
UpperCAmelCase__ = 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:
UpperCAmelCase__ = tuple(state["sep"] )
if "cls" in state:
UpperCAmelCase__ = tuple(state["cls"] )
UpperCAmelCase__ = False
if state.get("add_prefix_space" , lowerCamelCase ) != add_prefix_space:
UpperCAmelCase__ = add_prefix_space
UpperCAmelCase__ = True
if state.get("trim_offsets" , lowerCamelCase ) != trim_offsets:
UpperCAmelCase__ = trim_offsets
UpperCAmelCase__ = True
if changes_to_apply:
UpperCAmelCase__ = getattr(lowerCamelCase , state.pop("type" ) )
UpperCAmelCase__ = component_class(**lowerCamelCase )
setattr(self.backend_tokenizer , lowerCamelCase , lowerCamelCase )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def UpperCAmelCase_ ( self :List[str] ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase_ ( self :Union[str, Any] , lowerCamelCase :List[str] ) -> Dict:
UpperCAmelCase__ = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else value
UpperCAmelCase__ = value
def UpperCAmelCase_ ( self :Optional[Any] , *lowerCamelCase :Any , **lowerCamelCase :int ) -> BatchEncoding:
UpperCAmelCase__ = kwargs.get("is_split_into_words" , lowerCamelCase )
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(*lowerCamelCase , **lowerCamelCase )
def UpperCAmelCase_ ( self :int , *lowerCamelCase :Optional[Any] , **lowerCamelCase :Optional[int] ) -> BatchEncoding:
UpperCAmelCase__ = kwargs.get("is_split_into_words" , lowerCamelCase )
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(*lowerCamelCase , **lowerCamelCase )
def UpperCAmelCase_ ( self :Any , lowerCamelCase :str , lowerCamelCase :Optional[str] = None ) -> Tuple[str]:
UpperCAmelCase__ = self._tokenizer.model.save(lowerCamelCase , name=lowerCamelCase )
return tuple(lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[Any] , lowerCamelCase :Optional[int] , lowerCamelCase :Optional[Any]=None ) -> Dict:
UpperCAmelCase__ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self :Optional[int] , lowerCamelCase :List[int] , lowerCamelCase :Optional[List[int]] = None ) -> List[int]:
UpperCAmelCase__ = [self.sep_token_id]
UpperCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase_ ( self :List[str] , lowerCamelCase :Union[Dict[str, EncodedInput], BatchEncoding] , lowerCamelCase :Optional[int] = None , lowerCamelCase :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCamelCase :Optional[int] = None , lowerCamelCase :Optional[bool] = None , ) -> dict:
UpperCAmelCase__ = super()._pad(
encoded_inputs=lowerCamelCase , max_length=lowerCamelCase , padding_strategy=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , )
# Load from model defaults
if return_attention_mask is None:
UpperCAmelCase__ = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
UpperCAmelCase__ = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
UpperCAmelCase__ = len(encoded_inputs["global_attention_mask"] ) != len(lowerCamelCase )
if needs_to_be_padded:
UpperCAmelCase__ = len(lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
UpperCAmelCase__ = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
UpperCAmelCase__ = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 364 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowerCAmelCase_ ( __a , __a=False ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__: Dict =[]
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""module.blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""module.blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
("module.cls_token", "vit.embeddings.cls_token"),
("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("module.pos_embed", "vit.embeddings.position_embeddings"),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("module.norm.weight", "layernorm.weight"),
("module.norm.bias", "layernorm.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowerCamelCase__: int =[(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def lowerCAmelCase_ ( __a , __a , __a=False ) -> Any:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
lowerCamelCase__: List[Any] =""
else:
lowerCamelCase__: Dict ="vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase__: Tuple =state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight""" )
lowerCamelCase__: int =state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__: int =in_proj_weight[
: config.hidden_size, :
]
lowerCamelCase__: Optional[int] =in_proj_bias[: config.hidden_size]
lowerCamelCase__: Union[str, Any] =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase__: str =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase__: Dict =in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase__: str =in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( __a ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: Tuple =["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(__a , __a )
def lowerCAmelCase_ ( __a ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase__: Any =[
"module.fc.fc1.weight",
"module.fc.fc1.bias",
"module.fc.bn1.weight",
"module.fc.bn1.bias",
"module.fc.bn1.running_mean",
"module.fc.bn1.running_var",
"module.fc.bn1.num_batches_tracked",
"module.fc.fc2.weight",
"module.fc.fc2.bias",
"module.fc.bn2.weight",
"module.fc.bn2.bias",
"module.fc.bn2.running_mean",
"module.fc.bn2.running_var",
"module.fc.bn2.num_batches_tracked",
"module.fc.fc3.weight",
"module.fc.fc3.bias",
]
for k in ignore_keys:
state_dict.pop(__a , __a )
def lowerCAmelCase_ ( __a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__: int =dct.pop(__a )
lowerCamelCase__: Tuple =val
def lowerCAmelCase_ ( __a , __a ) -> str:
"""simple docstring"""
lowerCamelCase__: List[str] =ViTMSNConfig()
lowerCamelCase__: Optional[Any] =1000
lowerCamelCase__: Dict ="datasets/huggingface/label-files"
lowerCamelCase__: List[str] ="imagenet-1k-id2label.json"
lowerCamelCase__: List[str] =json.load(open(hf_hub_download(__a , __a ) , "r" ) )
lowerCamelCase__: int ={int(__a ): v for k, v in idalabel.items()}
lowerCamelCase__: List[str] =idalabel
lowerCamelCase__: Union[str, Any] ={v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
lowerCamelCase__: Dict =384
lowerCamelCase__: Union[str, Any] =1536
lowerCamelCase__: Dict =6
elif "l16" in checkpoint_url:
lowerCamelCase__: str =1024
lowerCamelCase__: List[Any] =4096
lowerCamelCase__: List[str] =24
lowerCamelCase__: List[Any] =16
lowerCamelCase__: Any =0.1
elif "b4" in checkpoint_url:
lowerCamelCase__: List[str] =4
elif "l7" in checkpoint_url:
lowerCamelCase__: Optional[Any] =7
lowerCamelCase__: int =1024
lowerCamelCase__: int =4096
lowerCamelCase__: Tuple =24
lowerCamelCase__: List[Any] =16
lowerCamelCase__: Tuple =0.1
lowerCamelCase__: Union[str, Any] =ViTMSNModel(__a )
lowerCamelCase__: Tuple =torch.hub.load_state_dict_from_url(__a , map_location="cpu" )["target_encoder"]
lowerCamelCase__: Union[str, Any] =ViTImageProcessor(size=config.image_size )
remove_projection_head(__a )
lowerCamelCase__: Optional[Any] =create_rename_keys(__a , base_model=__a )
for src, dest in rename_keys:
rename_key(__a , __a , __a )
read_in_q_k_v(__a , __a , base_model=__a )
model.load_state_dict(__a )
model.eval()
lowerCamelCase__: Dict ="http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase__: Any =Image.open(requests.get(__a , stream=__a ).raw )
lowerCamelCase__: Union[str, Any] =ViTImageProcessor(
size=config.image_size , image_mean=__a , image_std=__a )
lowerCamelCase__: List[str] =image_processor(images=__a , return_tensors="pt" )
# forward pass
torch.manual_seed(2 )
lowerCamelCase__: Union[str, Any] =model(**__a )
lowerCamelCase__: List[str] =outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
lowerCamelCase__: str =torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]] )
elif "b16" in checkpoint_url:
lowerCamelCase__: Tuple =torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]] )
elif "l16" in checkpoint_url:
lowerCamelCase__: List[Any] =torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]] )
elif "b4" in checkpoint_url:
lowerCamelCase__: Union[str, Any] =torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]] )
else:
lowerCamelCase__: Dict =torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , __a , atol=1e-4 )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__a )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__a )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
__A = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 59 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=1_3 , _a=3 , _a=True , _a=True , _a=0.1 , _a=0.1 , _a=2_2_4 , _a=1_0_0_0 , _a=[3, 3, 6, 4] , _a=[4_8, 5_6, 1_1_2, 2_2_0] , ) -> Tuple:
_a : Dict = parent
_a : Optional[int] = batch_size
_a : Optional[Any] = num_channels
_a : Union[str, Any] = is_training
_a : Tuple = use_labels
_a : Dict = hidden_dropout_prob
_a : List[Any] = attention_probs_dropout_prob
_a : Dict = num_labels
_a : List[str] = image_size
_a : Dict = layer_depths
_a : str = embed_dims
def __lowercase ( self ) -> Optional[Any]:
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : int = None
if self.use_labels:
_a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_a : Dict = self.get_config()
return config, pixel_values, labels
def __lowercase ( self ) -> int:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_a , layer_scale_init_value=1e-5 , )
def __lowercase ( self , _a , _a , _a ) -> str:
_a : List[Any] = SwiftFormerModel(config=_a )
model.to(_a )
model.eval()
_a : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __lowercase ( self , _a , _a , _a ) -> Optional[Any]:
_a : List[str] = self.num_labels
_a : Optional[int] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
_a : Union[str, Any] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : Optional[Any] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self ) -> Tuple:
((_a) , (_a) , (_a)) : Optional[int] = self.prepare_config_and_inputs()
_a : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
UpperCAmelCase__ : Optional[int] = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : str = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = False
def __lowercase ( self ) -> Optional[int]:
_a : Union[str, Any] = SwiftFormerModelTester(self )
_a : int = ConfigTester(
self , config_class=_a , has_text_modality=_a , hidden_size=3_7 , num_attention_heads=1_2 , num_hidden_layers=1_2 , )
def __lowercase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def __lowercase ( self ) -> Union[str, Any]:
pass
def __lowercase ( self ) -> Dict:
_a , _a : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Any = model_class(_a )
_a : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __lowercase ( self ) -> str:
_a , _a : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Optional[int] = model_class(_a )
_a : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a : Tuple = [*signature.parameters.keys()]
_a : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def __lowercase ( self ) -> int:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __lowercase ( self ) -> Optional[int]:
_a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def __lowercase ( self ) -> Optional[Any]:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : Any = SwiftFormerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def __lowercase ( self ) -> List[Any]:
pass
def __lowercase ( self ) -> int:
def check_hidden_states_output(_a , _a , _a ):
_a : Optional[int] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
_a : Union[str, Any] = model(**self._prepare_for_class(_a , _a ) )
_a : Optional[Any] = outputs.hidden_states
_a : Union[str, Any] = 8
self.assertEqual(len(_a ) , _a ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_a ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
_a , _a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : str = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a : List[str] = True
check_hidden_states_output(_a , _a , _a )
def __lowercase ( self ) -> str:
def _config_zero_init(_a ):
_a : List[Any] = copy.deepcopy(_a )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_a , _a , 1e-1_0 )
if isinstance(getattr(_a , _a , _a ) , _a ):
_a : int = _config_zero_init(getattr(_a , _a ) )
setattr(_a , _a , _a )
return configs_no_init
_a , _a : Any = self.model_tester.prepare_config_and_inputs_for_common()
_a : Dict = _config_zero_init(_a )
for model_class in self.all_model_classes:
_a : Dict = model_class(config=_a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_a : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self ) -> str:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def __lowercase ( self ) -> Dict:
_a : Any = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(_a )
_a : Any = self.default_image_processor
_a : Any = prepare_img()
_a : Any = image_processor(images=_a , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
_a : Optional[Any] = model(**_a )
# verify the logits
_a : List[str] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _a )
_a : int = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 14 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
'''configuration_encodec''': [
'''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EncodecConfig''',
],
'''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Tuple = [
'''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EncodecModel''',
'''EncodecPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
lowerCamelCase_ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 265 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowercase : int ) -> None:
'''simple docstring'''
UpperCamelCase__ = num_of_nodes
UpperCamelCase__ = []
UpperCamelCase__ = {}
def A ( self : Optional[Any] , lowercase : int , lowercase : int , lowercase : int ) -> None:
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def A ( self : str , lowercase : int ) -> int:
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def A ( self : Union[str, Any] , lowercase : int ) -> None:
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
UpperCamelCase__ = self.find_component(lowercase )
def A ( self : Tuple , lowercase : list[int] , lowercase : int , lowercase : int ) -> None:
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
UpperCamelCase__ = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowercase )
elif component_size[u_node] >= component_size[v_node]:
UpperCamelCase__ = self.find_component(lowercase )
component_size[u_node] += component_size[v_node]
self.set_component(lowercase )
def A ( self : int ) -> None:
'''simple docstring'''
UpperCamelCase__ = []
UpperCamelCase__ = 0
UpperCamelCase__ = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
UpperCamelCase__ = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = edge
UpperCamelCase__ = self.m_component[u]
UpperCamelCase__ = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
UpperCamelCase__ = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowercase , lowercase ):
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = edge
UpperCamelCase__ = self.m_component[u]
UpperCamelCase__ = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowercase , lowercase , lowercase )
print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n" )
num_of_components -= 1
UpperCamelCase__ = [-1] * self.m_num_of_nodes
print(f"The total weight of the minimal spanning tree is: {mst_weight}" )
def __magic_name__( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 265 | 1 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def _UpperCAmelCase ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Union[str, Any]=None , **__lowerCamelCase : Tuple ) -> Optional[int]:
_snake_case = [x.strip() for x in open(__lowerCamelCase ).readlines()]
_snake_case = [x.strip() for x in open(__lowerCamelCase ).readlines()][: len(__lowerCamelCase )]
_snake_case = calculate_rouge(__lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
if save_path is not None:
save_json(__lowerCamelCase , __lowerCamelCase , indent=__lowerCamelCase )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 224 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class lowerCAmelCase__ ( A_ ):
def __init__( self : Tuple , *_lowerCamelCase : Optional[Any] , **_lowerCamelCase : Dict ):
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def lowercase ( self : Tuple , _lowerCamelCase : Any , _lowerCamelCase : List[Any] ):
_snake_case = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(_lowerCamelCase )
_snake_case = self.values[key]
def lowercase ( self : int ):
return (
sum(self.charge_factor - len(_lowerCamelCase ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def lowercase ( self : Dict , _lowerCamelCase : List[str] , _lowerCamelCase : List[str]=None ):
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(_lowerCamelCase ) == 0
):
return key
return super()._collision_resolution(_lowerCamelCase , _lowerCamelCase )
| 224 | 1 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class UpperCamelCase_ ( a__ ):
'''simple docstring'''
a :Optional[int] = """MCTCTFeatureExtractor"""
a :str = """AutoTokenizer"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase):
super().__init__(lowercase__ , lowercase__)
lowerCAmelCase_ = self.feature_extractor
lowerCAmelCase_ = False
def __call__( self , *_UpperCAmelCase , **_UpperCAmelCase):
if self._in_target_context_manager:
return self.current_processor(*lowercase__ , **lowercase__)
if "raw_speech" in kwargs:
warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''')
lowerCAmelCase_ = kwargs.pop('''raw_speech''')
else:
lowerCAmelCase_ = kwargs.pop('''audio''' , lowercase__)
lowerCAmelCase_ = kwargs.pop('''sampling_rate''' , lowercase__)
lowerCAmelCase_ = kwargs.pop('''text''' , lowercase__)
if len(lowercase__) > 0:
lowerCAmelCase_ = args[0]
lowerCAmelCase_ = 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:
lowerCAmelCase_ = self.feature_extractor(lowercase__ , *lowercase__ , sampling_rate=lowercase__ , **lowercase__)
if text is not None:
lowerCAmelCase_ = self.tokenizer(lowercase__ , **lowercase__)
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCAmelCase_ = encodings['''input_ids''']
return inputs
def lowercase__ ( self , *_UpperCAmelCase , **_UpperCAmelCase):
return self.tokenizer.batch_decode(*lowercase__ , **lowercase__)
def lowercase__ ( self , *_UpperCAmelCase , **_UpperCAmelCase):
if self._in_target_context_manager:
return self.current_processor.pad(*lowercase__ , **lowercase__)
lowerCAmelCase_ = kwargs.pop('''input_features''' , lowercase__)
lowerCAmelCase_ = kwargs.pop('''labels''' , lowercase__)
if len(lowercase__) > 0:
lowerCAmelCase_ = args[0]
lowerCAmelCase_ = args[1:]
if input_features is not None:
lowerCAmelCase_ = self.feature_extractor.pad(lowercase__ , *lowercase__ , **lowercase__)
if labels is not None:
lowerCAmelCase_ = self.tokenizer.pad(lowercase__ , **lowercase__)
if labels is None:
return input_features
elif input_features is None:
return labels
else:
lowerCAmelCase_ = labels['''input_ids''']
return input_features
def lowercase__ ( self , *_UpperCAmelCase , **_UpperCAmelCase):
return self.tokenizer.decode(*lowercase__ , **lowercase__)
@contextmanager
def lowercase__ ( self):
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.''')
lowerCAmelCase_ = True
lowerCAmelCase_ = self.tokenizer
yield
lowerCAmelCase_ = self.feature_extractor
lowerCAmelCase_ = False
| 703 |
from math import pi
def lowerCamelCase_ ( A : int , A : int ):
"""simple docstring"""
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 413 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = (EulerDiscreteScheduler,)
UpperCamelCase = 10
def a__ ( self : Dict , **A_ : int ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = {
'num_train_timesteps': 1100,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**A_ )
return config
def a__ ( self : Tuple ) -> Dict:
"""simple docstring"""
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=A_ )
def a__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=A_ , beta_end=A_ )
def a__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=A_ )
def a__ ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A_ )
def a__ ( self : List[Any] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = self.scheduler_classes[0]
lowerCamelCase_ = self.get_scheduler_config()
lowerCamelCase_ = scheduler_class(**A_ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = self.dummy_model()
lowerCamelCase_ = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCamelCase_ = sample.to(A_ )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase_ = scheduler.scale_model_input(A_ , A_ )
lowerCamelCase_ = model(A_ , A_ )
lowerCamelCase_ = scheduler.step(A_ , A_ , A_ , generator=A_ )
lowerCamelCase_ = output.prev_sample
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 10.0807 ) < 1E-2
assert abs(result_mean.item() - 0.0131 ) < 1E-3
def a__ ( self : Dict ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = self.scheduler_classes[0]
lowerCamelCase_ = self.get_scheduler_config(prediction_type='v_prediction' )
lowerCamelCase_ = scheduler_class(**A_ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = self.dummy_model()
lowerCamelCase_ = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCamelCase_ = sample.to(A_ )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase_ = scheduler.scale_model_input(A_ , A_ )
lowerCamelCase_ = model(A_ , A_ )
lowerCamelCase_ = scheduler.step(A_ , A_ , A_ , generator=A_ )
lowerCamelCase_ = output.prev_sample
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 0.0002 ) < 1E-2
assert abs(result_mean.item() - 2.2_676E-06 ) < 1E-3
def a__ ( self : Any ) -> Any:
"""simple docstring"""
lowerCamelCase_ = self.scheduler_classes[0]
lowerCamelCase_ = self.get_scheduler_config()
lowerCamelCase_ = scheduler_class(**A_ )
scheduler.set_timesteps(self.num_inference_steps , device=A_ )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = self.dummy_model()
lowerCamelCase_ = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowerCamelCase_ = sample.to(A_ )
for t in scheduler.timesteps:
lowerCamelCase_ = scheduler.scale_model_input(A_ , A_ )
lowerCamelCase_ = model(A_ , A_ )
lowerCamelCase_ = scheduler.step(A_ , A_ , A_ , generator=A_ )
lowerCamelCase_ = output.prev_sample
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 10.0807 ) < 1E-2
assert abs(result_mean.item() - 0.0131 ) < 1E-3
def a__ ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ = self.scheduler_classes[0]
lowerCamelCase_ = self.get_scheduler_config()
lowerCamelCase_ = scheduler_class(**A_ , use_karras_sigmas=A_ )
scheduler.set_timesteps(self.num_inference_steps , device=A_ )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = self.dummy_model()
lowerCamelCase_ = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowerCamelCase_ = sample.to(A_ )
for t in scheduler.timesteps:
lowerCamelCase_ = scheduler.scale_model_input(A_ , A_ )
lowerCamelCase_ = model(A_ , A_ )
lowerCamelCase_ = scheduler.step(A_ , A_ , A_ , generator=A_ )
lowerCamelCase_ = output.prev_sample
lowerCamelCase_ = torch.sum(torch.abs(A_ ) )
lowerCamelCase_ = torch.mean(torch.abs(A_ ) )
assert abs(result_sum.item() - 124.52299499511719 ) < 1E-2
assert abs(result_mean.item() - 0.16213932633399963 ) < 1E-3
| 70 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 70 | 1 |
UpperCAmelCase_ =0 # The first color of the flag.
UpperCAmelCase_ =1 # The second color of the flag.
UpperCAmelCase_ =2 # The third color of the flag.
UpperCAmelCase_ =(red, white, blue)
def UpperCAmelCase ( _snake_case ):
if not sequence:
return []
if len(lowerCAmelCase__ ) == 1:
return list(lowerCAmelCase__ )
lowerCAmelCase = 0
lowerCAmelCase = len(lowerCAmelCase__ ) - 1
lowerCAmelCase = 0
while mid <= high:
if sequence[mid] == colors[0]:
lowerCAmelCase = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
lowerCAmelCase = sequence[high], sequence[mid]
high -= 1
else:
lowerCAmelCase = F"""The elements inside the sequence must contains only {colors} values"""
raise ValueError(lowerCAmelCase__ )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ =input("""Enter numbers separated by commas:\n""").strip()
UpperCAmelCase_ =[int(item.strip()) for item in user_input.split(""",""")]
print(F'''{dutch_national_flag_sort(unsorted)}''')
| 708 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def UpperCAmelCase ( _snake_case ):
lowerCAmelCase , lowerCAmelCase = analyze_text(_snake_case )
lowerCAmelCase = list(''' ''' + ascii_lowercase )
# what is our total sum of probabilities.
lowerCAmelCase = sum(single_char_strings.values() )
# one length string
lowerCAmelCase = 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:
lowerCAmelCase = single_char_strings[ch]
lowerCAmelCase = my_str / all_sum
my_fir_sum += prob * math.loga(_snake_case ) # entropy formula.
# print entropy
print(F"""{round(-1 * my_fir_sum ):.1f}""" )
# two len string
lowerCAmelCase = sum(two_char_strings.values() )
lowerCAmelCase = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
lowerCAmelCase = cha + cha
if sequence in two_char_strings:
lowerCAmelCase = two_char_strings[sequence]
lowerCAmelCase = int(_snake_case ) / all_sum
my_sec_sum += prob * math.loga(_snake_case )
# 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 UpperCAmelCase ( _snake_case ):
lowerCAmelCase = Counter() # type: ignore
lowerCAmelCase = 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(_snake_case ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def UpperCAmelCase ( ):
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()
| 33 | 0 |
"""simple docstring"""
from collections.abc import Callable
def lowercase (SCREAMING_SNAKE_CASE_ : Callable[[float], float] , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ) -> float:
SCREAMING_SNAKE_CASE = a
SCREAMING_SNAKE_CASE = b
if function(SCREAMING_SNAKE_CASE_ ) == 0: # one of the a or b is a root for the function
return a
elif function(SCREAMING_SNAKE_CASE_ ) == 0:
return b
elif (
function(SCREAMING_SNAKE_CASE_ ) * function(SCREAMING_SNAKE_CASE_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
SCREAMING_SNAKE_CASE = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(SCREAMING_SNAKE_CASE_ ) == 0:
return mid
elif function(SCREAMING_SNAKE_CASE_ ) * function(SCREAMING_SNAKE_CASE_ ) < 0:
SCREAMING_SNAKE_CASE = mid
else:
SCREAMING_SNAKE_CASE = mid
SCREAMING_SNAKE_CASE = start + (end - start) / 2.0
return mid
def lowercase (SCREAMING_SNAKE_CASE_ : float ) -> float:
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 247 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__UpperCamelCase = None
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''▁'''
__UpperCamelCase = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__UpperCamelCase = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
'''tokenizer_file''': {
'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
},
}
__UpperCamelCase = {
'''google/pegasus-xsum''': 512,
}
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : Any = PegasusTokenizer
SCREAMING_SNAKE_CASE_ : Optional[int] = ["""input_ids""", """attention_mask"""]
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__="<pad>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<mask_2>" , lowerCAmelCase__="<mask_1>" , lowerCAmelCase__=None , lowerCAmelCase__=103 , **lowerCAmelCase__ , ) -> Dict:
SCREAMING_SNAKE_CASE = offset
if additional_special_tokens is not None:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError(
F'additional_special_tokens should be of type {type(lowerCAmelCase__ )}, but is'
F' {type(lowerCAmelCase__ )}' )
SCREAMING_SNAKE_CASE = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F'<unk_{i}>' for i in range(len(lowerCAmelCase__ ) , self.offset - 1 )
]
if len(set(lowerCAmelCase__ ) ) != len(lowerCAmelCase__ ):
raise ValueError(
'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
SCREAMING_SNAKE_CASE = additional_special_tokens_extended
else:
SCREAMING_SNAKE_CASE = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F'<unk_{i}>' for i in range(2 , self.offset )]
super().__init__(
lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , mask_token_sent=lowerCAmelCase__ , offset=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE = vocab_file
SCREAMING_SNAKE_CASE = False if not self.vocab_file else True
def __A ( self , lowerCAmelCase__ ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
F' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}' )
return [1 if x in all_special_ids else 0 for x in seq]
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False ) -> List[int]:
if already_has_special_tokens:
return self._special_token_mask(lowerCAmelCase__ )
elif token_ids_a is None:
return self._special_token_mask(lowerCAmelCase__ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
SCREAMING_SNAKE_CASE = os.path.join(
lowerCAmelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,)
| 247 | 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, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase=13 , _lowerCamelCase=3 , _lowerCamelCase=224 , _lowerCamelCase=30 , _lowerCamelCase=400 , _lowerCamelCase=True , _lowerCamelCase=None , _lowerCamelCase=True , _lowerCamelCase=[0.5, 0.5, 0.5] , _lowerCamelCase=[0.5, 0.5, 0.5] , ) ->Dict:
SCREAMING_SNAKE_CASE : Any = size if size is not None else {'''height''': 18, '''width''': 18}
SCREAMING_SNAKE_CASE : str = parent
SCREAMING_SNAKE_CASE : str = batch_size
SCREAMING_SNAKE_CASE : Tuple = num_channels
SCREAMING_SNAKE_CASE : Optional[int] = image_size
SCREAMING_SNAKE_CASE : Optional[Any] = min_resolution
SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
SCREAMING_SNAKE_CASE : Tuple = do_resize
SCREAMING_SNAKE_CASE : int = size
SCREAMING_SNAKE_CASE : str = do_normalize
SCREAMING_SNAKE_CASE : Any = image_mean
SCREAMING_SNAKE_CASE : Any = image_std
def __lowerCAmelCase ( self ) ->Tuple:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a_ ( a__ , unittest.TestCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = ViTImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self ) ->str:
SCREAMING_SNAKE_CASE : Dict = EfficientFormerImageProcessorTester(self )
@property
def __lowerCAmelCase ( self ) ->List[Any]:
return self.image_proc_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self ) ->List[str]:
SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def __lowerCAmelCase ( self ) ->Tuple:
pass
def __lowerCAmelCase ( self ) ->Union[str, Any]:
# Initialize image_processor
SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE : str = prepare_image_inputs(self.image_proc_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE : str = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
SCREAMING_SNAKE_CASE : Tuple = image_processor(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
def __lowerCAmelCase ( self ) ->Tuple:
# Initialize image_processor
SCREAMING_SNAKE_CASE : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE : Optional[Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE : Dict = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
SCREAMING_SNAKE_CASE : List[str] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
def __lowerCAmelCase ( self ) ->Optional[Any]:
# Initialize image_processor
SCREAMING_SNAKE_CASE : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE : Dict = prepare_image_inputs(self.image_proc_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE : Optional[Any] = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
SCREAMING_SNAKE_CASE : List[str] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
| 721 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
a__ : Optional[int] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
a__ : int = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"{len(upper_files)} files contain uppercase characters:")
print('''\n'''.join(upper_files) + '''\n''')
a__ : str = [file for file in filepaths if ''' ''' in file]
if space_files:
print(F"{len(space_files)} files contain space characters:")
print('''\n'''.join(space_files) + '''\n''')
a__ : Optional[Any] = [file for file in filepaths if '''-''' in file]
if hyphen_files:
print(F"{len(hyphen_files)} files contain hyphen characters:")
print('''\n'''.join(hyphen_files) + '''\n''')
a__ : List[str] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"{len(nodir_files)} files are not in a directory:")
print('''\n'''.join(nodir_files) + '''\n''')
a__ : Optional[int] = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 333 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : int =(
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC",
"KD 6S 9D TH AD",
"KS 8D 4D 9S 4S", # pair
"8C 4S KH JS 4D", # pair
"QH 8H KD JH 8S", # pair
"KC 4H KS 2H 8D", # pair
"KD 4S KC 3H 8S", # pair
"AH 8S AS KC JH", # pair
"3H 4C 4H 3S 2H", # 2 pairs
"5S 5D 2C KH KH", # 2 pairs
"3C KH 5D 5S KH", # 2 pairs
"AS 3C KH AD KH", # 2 pairs
"7C 7S 3S 7H 5S", # 3 of a kind
"7C 7S KH 2H 7H", # 3 of a kind
"AC KH QH AH AS", # 3 of a kind
"2H 4D 3C AS 5S", # straight (low ace)
"3C 5C 4C 2C 6H", # straight
"6S 8S 7S 5H 9H", # straight
"JS QS 9H TS KH", # straight
"QC KH TS JS AH", # straight (high ace)
"8C 9C 5C 3C TC", # flush
"3S 8S 9S 5S KS", # flush
"4C 5C 9C 8C KC", # flush
"JH 8H AH KH QH", # flush
"3D 2H 3H 2C 2D", # full house
"2H 2C 3S 3H 3D", # full house
"KH KC 3S 3H 3D", # full house
"JC 6H JS JD JH", # 4 of a kind
"JC 7H JS JD JH", # 4 of a kind
"JC KH JS JD JH", # 4 of a kind
"2S AS 4S 5S 3S", # straight flush (low ace)
"2D 6D 3D 4D 5D", # straight flush
"5C 6C 3C 7C 4C", # straight flush
"JH 9H TH KH QH", # straight flush
"JH AH TH KH QH", # royal flush (high ace straight flush)
)
__SCREAMING_SNAKE_CASE : Any =(
("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)
__SCREAMING_SNAKE_CASE : Optional[int] =(
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", True),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", False),
("AS 3S 4S 8S 2S", True),
)
__SCREAMING_SNAKE_CASE : Dict =(
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", False),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", True),
)
__SCREAMING_SNAKE_CASE : Dict =(
("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)
__SCREAMING_SNAKE_CASE : int =(
("JH AH TH KH QH", 0),
("JH 9H TH KH QH", 0),
("JC KH JS JD JH", 7),
("KH KC 3S 3H 3D", 6),
("8C 9C 5C 3C TC", 0),
("JS QS 9H TS KH", 0),
("7C 7S KH 2H 7H", 3),
("3C KH 5D 5S KH", 2),
("QH 8H KD JH 8S", 1),
("2D 6D 9D TH 7D", 0),
)
__SCREAMING_SNAKE_CASE : List[Any] =(
("JH AH TH KH QH", 23),
("JH 9H TH KH QH", 22),
("JC KH JS JD JH", 21),
("KH KC 3S 3H 3D", 20),
("8C 9C 5C 3C TC", 19),
("JS QS 9H TS KH", 18),
("7C 7S KH 2H 7H", 17),
("3C KH 5D 5S KH", 16),
("QH 8H KD JH 8S", 15),
("2D 6D 9D TH 7D", 14),
)
def UpperCamelCase__ ( ):
lowercase , lowercase = randrange(len(_UpperCamelCase ) ), randrange(len(_UpperCamelCase ) )
lowercase = ["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
lowercase , lowercase = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def UpperCamelCase__ ( lowerCAmelCase__ = 100 ):
return (generate_random_hand() for _ in range(_UpperCamelCase ))
@pytest.mark.parametrize("""hand, expected""" ,_UpperCamelCase )
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ):
assert PokerHand(_UpperCamelCase )._is_flush() == expected
@pytest.mark.parametrize("""hand, expected""" ,_UpperCamelCase )
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ):
assert PokerHand(_UpperCamelCase )._is_straight() == expected
@pytest.mark.parametrize("""hand, expected, card_values""" ,_UpperCamelCase )
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
lowercase = PokerHand(_UpperCamelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("""hand, expected""" ,_UpperCamelCase )
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ):
assert PokerHand(_UpperCamelCase )._is_same_kind() == expected
@pytest.mark.parametrize("""hand, expected""" ,_UpperCamelCase )
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ):
assert PokerHand(_UpperCamelCase )._hand_type == expected
@pytest.mark.parametrize("""hand, other, expected""" ,_UpperCamelCase )
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
assert PokerHand(_UpperCamelCase ).compare_with(PokerHand(_UpperCamelCase ) ) == expected
@pytest.mark.parametrize("""hand, other, expected""" ,generate_random_hands() )
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
assert PokerHand(_UpperCamelCase ).compare_with(PokerHand(_UpperCamelCase ) ) == expected
def UpperCamelCase__ ( ):
lowercase = [PokerHand(_UpperCamelCase ) for hand in SORTED_HANDS]
lowercase = poker_hands.copy()
shuffle(_UpperCamelCase )
lowercase = chain(sorted(_UpperCamelCase ) )
for index, hand in enumerate(_UpperCamelCase ):
assert hand == poker_hands[index]
def UpperCamelCase__ ( ):
lowercase = [PokerHand("""2D AC 3H 4H 5S""" ), PokerHand("""2S 3H 4H 5S 6C""" )]
pokerhands.sort(reverse=_UpperCamelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def UpperCamelCase__ ( ):
lowercase = PokerHand("""2C 4S AS 3D 5C""" )
lowercase = True
lowercase = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def UpperCamelCase__ ( ):
lowercase = 0
lowercase = os.path.abspath(os.path.dirname(_UpperCamelCase ) )
lowercase = os.path.join(_UpperCamelCase ,"""poker_hands.txt""" )
with open(_UpperCamelCase ) as file_hand:
for line in file_hand:
lowercase = line[:14].strip()
lowercase = line[15:].strip()
lowercase , lowercase = PokerHand(_UpperCamelCase ), PokerHand(_UpperCamelCase )
lowercase = player.compare_with(_UpperCamelCase )
if output == "Win":
answer += 1
assert answer == 376
| 428 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
lowerCamelCase : Optional[int] = logging.getLogger(__name__)
require_version("pytorch_lightning>=1.0.4")
lowerCamelCase : Union[str, Any] = {
"base": AutoModel,
"sequence-classification": AutoModelForSequenceClassification,
"question-answering": AutoModelForQuestionAnswering,
"pretraining": AutoModelForPreTraining,
"token-classification": AutoModelForTokenClassification,
"language-modeling": AutoModelWithLMHead,
"summarization": AutoModelForSeqaSeqLM,
"translation": AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase : str = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase : Optional[Any] = sorted(arg_to_scheduler.keys())
lowerCamelCase : int = "{" + ", ".join(arg_to_scheduler_choices) + "}"
class A__ ( pl.LightningModule ):
def __init__( self : Optional[Any] , _a : argparse.Namespace , _a : Any=None , _a : int="base" , _a : Dict=None , _a : Tuple=None , _a : Any=None , **_a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(_a )
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =Path(self.hparams.output_dir )
_SCREAMING_SNAKE_CASE =self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
_SCREAMING_SNAKE_CASE =AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'num_labels': num_labels} if num_labels is not None else {}) , cache_dir=_a , **_a , )
else:
_SCREAMING_SNAKE_CASE =config
_SCREAMING_SNAKE_CASE =('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
for p in extra_model_params:
if getattr(self.hparams , _a , _a ):
assert hasattr(self.config , _a ), f"model config doesn't have a `{p}` attribute"
setattr(self.config , _a , getattr(self.hparams , _a ) )
if tokenizer is None:
_SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=_a , )
else:
_SCREAMING_SNAKE_CASE =tokenizer
_SCREAMING_SNAKE_CASE =MODEL_MODES[mode]
if model is None:
_SCREAMING_SNAKE_CASE =self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('.ckpt' in self.hparams.model_name_or_path ) , config=self.config , cache_dir=_a , )
else:
_SCREAMING_SNAKE_CASE =model
def A ( self : List[Any] , *_a : Optional[int] , **_a : int ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_type.from_pretrained(*_a , **_a )
def A ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =arg_to_scheduler[self.hparams.lr_scheduler]
_SCREAMING_SNAKE_CASE =get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
_SCREAMING_SNAKE_CASE ={'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
return scheduler
def A ( self : str ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model
_SCREAMING_SNAKE_CASE =['bias', 'LayerNorm.weight']
_SCREAMING_SNAKE_CASE =[
{
'params': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
'weight_decay': self.hparams.weight_decay,
},
{
'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
'weight_decay': 0.0,
},
]
if self.hparams.adafactor:
_SCREAMING_SNAKE_CASE =Adafactor(
_a , lr=self.hparams.learning_rate , scale_parameter=_a , relative_step=_a )
else:
_SCREAMING_SNAKE_CASE =AdamW(
_a , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
_SCREAMING_SNAKE_CASE =optimizer
_SCREAMING_SNAKE_CASE =self.get_lr_scheduler()
return [optimizer], [scheduler]
def A ( self : Tuple , _a : Dict , _a : List[str] ) -> str:
'''simple docstring'''
return self.validation_step(_a , _a )
def A ( self : Dict , _a : str ) -> Dict:
'''simple docstring'''
return self.validation_end(_a )
def A ( self : Union[str, Any] ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
_SCREAMING_SNAKE_CASE =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A ( self : int , _a : Any ) -> Union[str, Any]:
'''simple docstring'''
if stage == "test":
_SCREAMING_SNAKE_CASE =len(self.test_dataloader().dataset )
else:
_SCREAMING_SNAKE_CASE =self.get_dataloader('train' , self.hparams.train_batch_size , shuffle=_a )
_SCREAMING_SNAKE_CASE =len(self.train_dataloader().dataset )
def A ( self : Union[str, Any] , _a : str , _a : int , _a : bool = False ) -> Optional[int]:
'''simple docstring'''
raise NotImplementedError('You must implement this for your task' )
def A ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return self.train_loader
def A ( self : str ) -> str:
'''simple docstring'''
return self.get_dataloader('dev' , self.hparams.eval_batch_size , shuffle=_a )
def A ( self : Tuple ) -> Any:
'''simple docstring'''
return self.get_dataloader('test' , self.hparams.eval_batch_size , shuffle=_a )
def A ( self : int , _a : Dict ) -> Optional[Any]:
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , 'cached_{}_{}_{}'.format(
_a , list(filter(_a , self.hparams.model_name_or_path.split('/' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A ( self : Optional[Any] , _a : Dict[str, Any] ) -> None:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.output_dir.joinpath('best_tfmr' )
_SCREAMING_SNAKE_CASE =self.step_count
self.model.save_pretrained(_a )
self.tokenizer.save_pretrained(_a )
@staticmethod
def A ( _a : Any , _a : Any ) -> List[str]:
'''simple docstring'''
parser.add_argument(
'--model_name_or_path' , default=_a , type=_a , required=_a , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--config_name' , default='' , type=_a , help='Pretrained config name or path if not the same as model_name' )
parser.add_argument(
'--tokenizer_name' , default=_a , type=_a , help='Pretrained tokenizer name or path if not the same as model_name' , )
parser.add_argument(
'--cache_dir' , default=str(Path(_a ).parent / 'test_run' / 'cache' ) , type=_a , help='Where do you want to store the pre-trained models downloaded from huggingface.co' , )
parser.add_argument(
'--encoder_layerdrop' , type=_a , help='Encoder layer dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--decoder_layerdrop' , type=_a , help='Decoder layer dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--dropout' , type=_a , help='Dropout probability (Optional). Goes into model.config' , )
parser.add_argument(
'--attention_dropout' , type=_a , help='Attention dropout probability (Optional). Goes into model.config' , )
parser.add_argument('--learning_rate' , default=5e-5 , type=_a , help='The initial learning rate for Adam.' )
parser.add_argument(
'--lr_scheduler' , default='linear' , choices=_a , metavar=_a , type=_a , help='Learning rate scheduler' , )
parser.add_argument('--weight_decay' , default=0.0 , type=_a , help='Weight decay if we apply some.' )
parser.add_argument('--adam_epsilon' , default=1e-8 , type=_a , help='Epsilon for Adam optimizer.' )
parser.add_argument('--warmup_steps' , default=0 , type=_a , help='Linear warmup over warmup_steps.' )
parser.add_argument('--num_workers' , default=4 , type=_a , help='kwarg passed to DataLoader' )
parser.add_argument('--num_train_epochs' , dest='max_epochs' , default=3 , type=_a )
parser.add_argument('--train_batch_size' , default=32 , type=_a )
parser.add_argument('--eval_batch_size' , default=32 , type=_a )
parser.add_argument('--adafactor' , action='store_true' )
class A__ ( pl.Callback ):
def A ( self : Tuple , _a : str , _a : int ) -> Dict:
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class A__ ( pl.Callback ):
def A ( self : Tuple , _a : str , _a : Tuple ) -> Tuple:
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(_a )
class A__ ( pl.Callback ):
def A ( self : List[str] , _a : Tuple , _a : Optional[int] ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =trainer.lr_schedulers[0]['scheduler']
_SCREAMING_SNAKE_CASE ={f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(_a )
def A ( self : List[Any] , _a : pl.Trainer , _a : pl.LightningModule ) -> Optional[int]:
'''simple docstring'''
rank_zero_info('***** Validation results *****' )
_SCREAMING_SNAKE_CASE =trainer.callback_metrics
# Log results
for key in sorted(_a ):
if key not in ["log", "progress_bar"]:
rank_zero_info('{} = {}\n'.format(_a , str(metrics[key] ) ) )
def A ( self : Optional[Any] , _a : pl.Trainer , _a : pl.LightningModule ) -> List[str]:
'''simple docstring'''
rank_zero_info('***** Test results *****' )
_SCREAMING_SNAKE_CASE =trainer.callback_metrics
# Log and save results to file
_SCREAMING_SNAKE_CASE =os.path.join(pl_module.hparams.output_dir , 'test_results.txt' )
with open(_a , 'w' ) as writer:
for key in sorted(_a ):
if key not in ["log", "progress_bar"]:
rank_zero_info('{} = {}\n'.format(_a , str(metrics[key] ) ) )
writer.write('{} = {}\n'.format(_a , str(metrics[key] ) ) )
def _lowerCAmelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Union[str, Any] ) -> None:
"""simple docstring"""
parser.add_argument(
'--output_dir' , default=str(Path(_UpperCamelCase ).parent / 'test_run' / 'model_checkpoints' ) , type=_UpperCamelCase , help='The output directory where the model predictions and checkpoints will be written.' , )
parser.add_argument(
'--fp16' , action='store_true' , help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit' , )
parser.add_argument(
'--fp16_opt_level' , type=_UpperCamelCase , default='O2' , help=(
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
'See details at https://nvidia.github.io/apex/amp.html'
) , )
parser.add_argument('--n_tpu_cores' , dest='tpu_cores' , type=_UpperCamelCase )
parser.add_argument('--max_grad_norm' , dest='gradient_clip_val' , default=1.0 , type=_UpperCamelCase , help='Max gradient norm' )
parser.add_argument('--do_train' , action='store_true' , help='Whether to run training.' )
parser.add_argument('--do_predict' , action='store_true' , help='Whether to run predictions on the test set.' )
parser.add_argument(
'--gradient_accumulation_steps' , dest='accumulate_grad_batches' , type=_UpperCamelCase , default=1 , help='Number of updates steps to accumulate before performing a backward/update pass.' , )
parser.add_argument('--seed' , type=_UpperCamelCase , default=42 , help='random seed for initialization' )
parser.add_argument(
'--data_dir' , default=str(Path(_UpperCamelCase ).parent / 'test_run' / 'dummy-train-data' ) , type=_UpperCamelCase , help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.' , )
def _lowerCAmelCase ( _UpperCamelCase : BaseTransformer , _UpperCamelCase : argparse.Namespace , _UpperCamelCase : Any=None , _UpperCamelCase : str=True , _UpperCamelCase : Any=[] , _UpperCamelCase : Union[str, Any]=None , _UpperCamelCase : str=None , **_UpperCamelCase : Optional[int] , ) -> Tuple:
"""simple docstring"""
pl.seed_everything(args.seed )
# init model
_SCREAMING_SNAKE_CASE =Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_UpperCamelCase )
# add custom checkpoints
if checkpoint_callback is None:
_SCREAMING_SNAKE_CASE =pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='checkpoint' , monitor='val_loss' , mode='min' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_UpperCamelCase )
if logging_callback is None:
_SCREAMING_SNAKE_CASE =LoggingCallback()
_SCREAMING_SNAKE_CASE ={}
if args.fpaa:
_SCREAMING_SNAKE_CASE =16
if args.gpus > 1:
_SCREAMING_SNAKE_CASE ='auto'
_SCREAMING_SNAKE_CASE ='ddp'
_SCREAMING_SNAKE_CASE =args.accumulate_grad_batches
_SCREAMING_SNAKE_CASE =None
_SCREAMING_SNAKE_CASE ='auto'
_SCREAMING_SNAKE_CASE =pl.Trainer.from_argparse_args(
_UpperCamelCase , weights_summary=_UpperCamelCase , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_UpperCamelCase , val_check_interval=1 , num_sanity_val_steps=2 , **_UpperCamelCase , )
if args.do_train:
trainer.fit(_UpperCamelCase )
else:
print('RAG modeling tests with new set functions successfuly executed!' )
return trainer
| 405 | 0 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def A ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = AlbertConfig.from_json_file(snake_case__ )
print(f"""Building PyTorch model from configuration: {config}""" )
SCREAMING_SNAKE_CASE__ = AlbertForPreTraining(snake_case__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(snake_case__ , snake_case__ , snake_case__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , snake_case__ )
if __name__ == "__main__":
A_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--albert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained ALBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
A_ : List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 700 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Dict = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : int = 'unispeech'
def __init__( self : Union[str, Any] , __UpperCAmelCase : List[Any]=3_2 , __UpperCAmelCase : Union[str, Any]=7_6_8 , __UpperCAmelCase : Tuple=1_2 , __UpperCAmelCase : Dict=1_2 , __UpperCAmelCase : Optional[Any]=3_0_7_2 , __UpperCAmelCase : Optional[Any]="gelu" , __UpperCAmelCase : Union[str, Any]=0.1 , __UpperCAmelCase : List[Any]=0.1 , __UpperCAmelCase : List[str]=0.1 , __UpperCAmelCase : List[str]=0.0 , __UpperCAmelCase : Tuple=0.0 , __UpperCAmelCase : List[Any]=0.1 , __UpperCAmelCase : Union[str, Any]=0.1 , __UpperCAmelCase : Union[str, Any]=0.02 , __UpperCAmelCase : Union[str, Any]=1e-5 , __UpperCAmelCase : List[Any]="group" , __UpperCAmelCase : Union[str, Any]="gelu" , __UpperCAmelCase : Any=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , __UpperCAmelCase : List[str]=(5, 2, 2, 2, 2, 2, 2) , __UpperCAmelCase : int=(1_0, 3, 3, 3, 3, 2, 2) , __UpperCAmelCase : str=False , __UpperCAmelCase : Any=1_2_8 , __UpperCAmelCase : str=1_6 , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Union[str, Any]=0.05 , __UpperCAmelCase : str=1_0 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : List[Any]=0.0 , __UpperCAmelCase : Tuple=1_0 , __UpperCAmelCase : Tuple=0 , __UpperCAmelCase : Tuple=3_2_0 , __UpperCAmelCase : Any=2 , __UpperCAmelCase : Optional[int]=0.1 , __UpperCAmelCase : Any=1_0_0 , __UpperCAmelCase : str=2_5_6 , __UpperCAmelCase : Dict=2_5_6 , __UpperCAmelCase : Dict=0.1 , __UpperCAmelCase : List[str]="mean" , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : List[Any]=False , __UpperCAmelCase : str=2_5_6 , __UpperCAmelCase : Dict=8_0 , __UpperCAmelCase : List[Any]=0 , __UpperCAmelCase : int=1 , __UpperCAmelCase : Optional[int]=2 , __UpperCAmelCase : Any=0.5 , **__UpperCAmelCase : List[str] , ) -> Tuple:
super().__init__(**__UpperCAmelCase , pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = feat_extract_norm
SCREAMING_SNAKE_CASE__ = feat_extract_activation
SCREAMING_SNAKE_CASE__ = list(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = list(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = list(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = conv_bias
SCREAMING_SNAKE_CASE__ = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE__ = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE__ = len(self.conv_dim )
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = hidden_dropout
SCREAMING_SNAKE_CASE__ = attention_dropout
SCREAMING_SNAKE_CASE__ = activation_dropout
SCREAMING_SNAKE_CASE__ = feat_proj_dropout
SCREAMING_SNAKE_CASE__ = final_dropout
SCREAMING_SNAKE_CASE__ = layerdrop
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = num_ctc_classes
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = do_stable_layer_norm
SCREAMING_SNAKE_CASE__ = use_weighted_layer_sum
SCREAMING_SNAKE_CASE__ = 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
SCREAMING_SNAKE_CASE__ = apply_spec_augment
SCREAMING_SNAKE_CASE__ = mask_time_prob
SCREAMING_SNAKE_CASE__ = mask_time_length
SCREAMING_SNAKE_CASE__ = mask_time_min_masks
SCREAMING_SNAKE_CASE__ = mask_feature_prob
SCREAMING_SNAKE_CASE__ = mask_feature_length
SCREAMING_SNAKE_CASE__ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
SCREAMING_SNAKE_CASE__ = num_codevectors_per_group
SCREAMING_SNAKE_CASE__ = num_codevector_groups
SCREAMING_SNAKE_CASE__ = contrastive_logits_temperature
SCREAMING_SNAKE_CASE__ = feat_quantizer_dropout
SCREAMING_SNAKE_CASE__ = num_negatives
SCREAMING_SNAKE_CASE__ = codevector_dim
SCREAMING_SNAKE_CASE__ = proj_codevector_dim
SCREAMING_SNAKE_CASE__ = diversity_loss_weight
# ctc loss
SCREAMING_SNAKE_CASE__ = ctc_loss_reduction
SCREAMING_SNAKE_CASE__ = ctc_zero_infinity
# pretraining loss
SCREAMING_SNAKE_CASE__ = replace_prob
@property
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 616 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _A ( unittest.TestCase ):
def lowercase__ ( self : List[str] ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
def lowercase__ ( self : Any ) -> Tuple:
"""simple docstring"""
__snake_case , __snake_case : Dict = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" , from_pt=__magic_name__ , dtype=jnp.bfloataa )
__snake_case , __snake_case : Tuple = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=__magic_name__ , from_pt=__magic_name__ , dtype=jnp.bfloataa )
__snake_case : Tuple = controlnet_params
__snake_case : Tuple = """bird"""
__snake_case : Dict = jax.device_count()
__snake_case : Any = pipe.prepare_text_inputs([prompts] * num_samples )
__snake_case : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
__snake_case : int = pipe.prepare_image_inputs([canny_image] * num_samples )
__snake_case : int = jax.random.PRNGKey(0 )
__snake_case : Dict = jax.random.split(__magic_name__ , jax.device_count() )
__snake_case : Any = replicate(__magic_name__ )
__snake_case : str = shard(__magic_name__ )
__snake_case : Optional[Any] = shard(__magic_name__ )
__snake_case : List[str] = pipe(
prompt_ids=__magic_name__ , image=__magic_name__ , params=__magic_name__ , prng_seed=__magic_name__ , num_inference_steps=50 , jit=__magic_name__ , ).images
assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)
__snake_case : Tuple = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__snake_case : Optional[Any] = images[0, 2_53:2_56, 2_53:2_56, -1]
__snake_case : str = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__snake_case : Optional[int] = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
print(f'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def lowercase__ ( self : Any ) -> List[str]:
"""simple docstring"""
__snake_case , __snake_case : Any = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" , from_pt=__magic_name__ , dtype=jnp.bfloataa )
__snake_case , __snake_case : Tuple = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=__magic_name__ , from_pt=__magic_name__ , dtype=jnp.bfloataa )
__snake_case : List[Any] = controlnet_params
__snake_case : Dict = """Chef in the kitchen"""
__snake_case : Dict = jax.device_count()
__snake_case : str = pipe.prepare_text_inputs([prompts] * num_samples )
__snake_case : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
__snake_case : Any = pipe.prepare_image_inputs([pose_image] * num_samples )
__snake_case : Any = jax.random.PRNGKey(0 )
__snake_case : Optional[Any] = jax.random.split(__magic_name__ , jax.device_count() )
__snake_case : List[Any] = replicate(__magic_name__ )
__snake_case : Optional[Any] = shard(__magic_name__ )
__snake_case : Optional[Any] = shard(__magic_name__ )
__snake_case : Optional[int] = pipe(
prompt_ids=__magic_name__ , image=__magic_name__ , params=__magic_name__ , prng_seed=__magic_name__ , num_inference_steps=50 , jit=__magic_name__ , ).images
assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)
__snake_case : str = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
__snake_case : Union[str, Any] = images[0, 2_53:2_56, 2_53:2_56, -1]
__snake_case : Any = jnp.asarray(jax.device_get(image_slice.flatten() ) )
__snake_case : Optional[int] = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
print(f'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 26 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
lowerCamelCase = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
lowerCamelCase = subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()
lowerCamelCase = """|""".join(sys.argv[1:])
lowerCamelCase = re.compile(RF'^({joined_dirs}).*?\.py$')
lowerCamelCase = [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")
| 191 | 0 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = 0
for ch in input_str:
A_ = ord(SCREAMING_SNAKE_CASE )
A_ = pow(2 , SCREAMING_SNAKE_CASE )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 563 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__lowercase = logging.get_logger(__name__)
__lowercase = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__lowercase = {
"""vocab_file""": {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"""
),
},
}
__lowercase = {
"""yjernite/retribert-base-uncased""": 512,
}
__lowercase = {
"""yjernite/retribert-base-uncased""": {"""do_lower_case""": True},
}
class _lowercase ( __lowerCamelCase ):
_lowercase : Union[str, Any] = VOCAB_FILES_NAMES
_lowercase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : Tuple = PRETRAINED_INIT_CONFIGURATION
_lowercase : List[Any] = RetriBertTokenizer
_lowercase : Optional[int] = ['input_ids', 'attention_mask']
def __init__( self : Optional[Any] , lowerCamelCase__ : int=None , lowerCamelCase__ : Tuple=None , lowerCamelCase__ : Dict=True , lowerCamelCase__ : Union[str, Any]="[UNK]" , lowerCamelCase__ : Optional[Any]="[SEP]" , lowerCamelCase__ : List[Any]="[PAD]" , lowerCamelCase__ : Tuple="[CLS]" , lowerCamelCase__ : List[Any]="[MASK]" , lowerCamelCase__ : Dict=True , lowerCamelCase__ : str=None , **lowerCamelCase__ : List[Any] , ) -> Dict:
"""simple docstring"""
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , tokenize_chinese_chars=lowerCamelCase__ , strip_accents=lowerCamelCase__ , **lowerCamelCase__ , )
A_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , lowerCamelCase__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , lowerCamelCase__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , lowerCamelCase__ ) != tokenize_chinese_chars
):
A_ = getattr(lowerCamelCase__ , normalizer_state.pop('''type''' ) )
A_ = do_lower_case
A_ = strip_accents
A_ = tokenize_chinese_chars
A_ = normalizer_class(**lowerCamelCase__ )
A_ = do_lower_case
def UpperCamelCase ( self : Optional[Any] , lowerCamelCase__ : int , lowerCamelCase__ : List[str]=None ) -> Union[str, Any]:
"""simple docstring"""
A_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase ( self : Any , lowerCamelCase__ : List[int] , lowerCamelCase__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
A_ = [self.sep_token_id]
A_ = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase ( self : Union[str, Any] , lowerCamelCase__ : str , lowerCamelCase__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
A_ = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 563 | 1 |
import os
from pathlib import Path
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
from torch.utils.cpp_extension import load
snake_case : Optional[int] = Path(lowercase ).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""" ,lowercase ,with_cuda=lowercase ,extra_include_paths=[str(lowercase )] ,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
| 587 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> bool:
snake_case : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
snake_case : set[int] = set()
return any(
node not in visited and depth_first_search(lowercase ,lowercase ,lowercase ,lowercase )
for node in graph )
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ) -> bool:
visited.add(lowercase )
rec_stk.add(lowercase )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(lowercase ,lowercase ,lowercase ,lowercase ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(lowercase )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 587 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_a : List[Any] = logging.getLogger(__name__)
@dataclass
class __A :
snake_case :str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
snake_case :Optional[str] = field(
default=__magic_name__ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
snake_case :Optional[str] = field(
default="NER" , metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
snake_case :Optional[str] = field(
default=__magic_name__ , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
snake_case :bool = field(default=__magic_name__ , 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.
snake_case :Optional[str] = field(
default=__magic_name__ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
@dataclass
class __A :
snake_case :str = field(
metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
snake_case :Optional[str] = field(
default=__magic_name__ , metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} , )
snake_case :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."
)
} , )
snake_case :bool = field(
default=__magic_name__ , metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _lowercase ( ) -> str:
"""simple docstring"""
__UpperCAmelCase : int = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__UpperCAmelCase : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__UpperCAmelCase : List[Any] = 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." )
__UpperCAmelCase : List[str] = import_module("tasks" )
try:
__UpperCAmelCase : Optional[int] = getattr(lowerCamelCase__ , model_args.task_type )
__UpperCAmelCase : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , lowerCamelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
__UpperCAmelCase : Any = token_classification_task.get_labels(data_args.labels )
__UpperCAmelCase : Dict[int, str] = dict(enumerate(lowerCamelCase__ ) )
__UpperCAmelCase : Any = len(lowerCamelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__UpperCAmelCase : List[str] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase__ , idalabel=lowerCamelCase__ , labelaid={label: i for i, label in enumerate(lowerCamelCase__ )} , cache_dir=model_args.cache_dir , )
__UpperCAmelCase : List[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 , use_fast=model_args.use_fast , )
__UpperCAmelCase : Union[str, Any] = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowerCamelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
__UpperCAmelCase : Tuple = (
TokenClassificationDataset(
token_classification_task=lowerCamelCase__ , data_dir=data_args.data_dir , tokenizer=lowerCamelCase__ , labels=lowerCamelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
__UpperCAmelCase : int = (
TokenClassificationDataset(
token_classification_task=lowerCamelCase__ , data_dir=data_args.data_dir , tokenizer=lowerCamelCase__ , labels=lowerCamelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(lowerCamelCase__ , lowerCamelCase__ ) -> Tuple[List[int], List[int]]:
__UpperCAmelCase : Tuple = np.argmax(lowerCamelCase__ , axis=2 )
__UpperCAmelCase : str = preds.shape
__UpperCAmelCase : List[Any] = [[] for _ in range(lowerCamelCase__ )]
__UpperCAmelCase : Union[str, Any] = [[] for _ in range(lowerCamelCase__ )]
for i in range(lowerCamelCase__ ):
for j in range(lowerCamelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(lowerCamelCase__ ) -> Dict:
__UpperCAmelCase : Any = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(lowerCamelCase__ , lowerCamelCase__ ),
"precision": precision_score(lowerCamelCase__ , lowerCamelCase__ ),
"recall": recall_score(lowerCamelCase__ , lowerCamelCase__ ),
"f1": fa_score(lowerCamelCase__ , lowerCamelCase__ ),
}
# Data collator
__UpperCAmelCase : Dict = DataCollatorWithPadding(lowerCamelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
__UpperCAmelCase : List[str] = Trainer(
model=lowerCamelCase__ , args=lowerCamelCase__ , train_dataset=lowerCamelCase__ , eval_dataset=lowerCamelCase__ , compute_metrics=lowerCamelCase__ , data_collator=lowerCamelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__UpperCAmelCase : List[str] = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
__UpperCAmelCase : Tuple = trainer.evaluate()
__UpperCAmelCase : List[str] = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(lowerCamelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , lowerCamelCase__ , lowerCamelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(lowerCamelCase__ )
# Predict
if training_args.do_predict:
__UpperCAmelCase : List[Any] = TokenClassificationDataset(
token_classification_task=lowerCamelCase__ , data_dir=data_args.data_dir , tokenizer=lowerCamelCase__ , labels=lowerCamelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
__UpperCAmelCase : List[Any] = trainer.predict(lowerCamelCase__ )
__UpperCAmelCase : int = align_predictions(lowerCamelCase__ , lowerCamelCase__ )
__UpperCAmelCase : str = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(lowerCamelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , lowerCamelCase__ , lowerCamelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
__UpperCAmelCase : Any = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(lowerCamelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return results
def _lowercase ( lowerCamelCase__ ) -> Optional[int]:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 707 | '''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
_a : int = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all BART models at https://huggingface.co/models?filter=bart
_a : Tuple = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
}
_a : List[Any] = {
"facebook/bart-base": 1024,
"facebook/bart-large": 1024,
"facebook/bart-large-mnli": 1024,
"facebook/bart-large-cnn": 1024,
"facebook/bart-large-xsum": 1024,
"yjernite/bart_eli5": 1024,
}
@lru_cache()
def _lowercase ( ) -> List[Any]:
"""simple docstring"""
__UpperCAmelCase : Dict = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
__UpperCAmelCase : Optional[Any] = bs[:]
__UpperCAmelCase : Optional[int] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCamelCase__ )
cs.append(2**8 + n )
n += 1
__UpperCAmelCase : Dict = [chr(lowerCamelCase__ ) for n in cs]
return dict(zip(lowerCamelCase__ , lowerCamelCase__ ) )
def _lowercase ( lowerCamelCase__ ) -> str:
"""simple docstring"""
__UpperCAmelCase : Dict = set()
__UpperCAmelCase : Union[str, Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__UpperCAmelCase : Optional[Any] = char
return pairs
class __A (__magic_name__ ):
snake_case :Optional[int] = VOCAB_FILES_NAMES
snake_case :List[Any] = PRETRAINED_VOCAB_FILES_MAP
snake_case :Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case :Optional[int] = ["input_ids", "attention_mask"]
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_="replace" , UpperCamelCase_="<s>" , UpperCamelCase_="</s>" , UpperCamelCase_="</s>" , UpperCamelCase_="<s>" , UpperCamelCase_="<unk>" , UpperCamelCase_="<pad>" , UpperCamelCase_="<mask>" , UpperCamelCase_=False , **UpperCamelCase_ , ):
__UpperCAmelCase : str = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else bos_token
__UpperCAmelCase : List[str] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else eos_token
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else sep_token
__UpperCAmelCase : int = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else cls_token
__UpperCAmelCase : Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else unk_token
__UpperCAmelCase : Dict = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__UpperCAmelCase : Union[str, Any] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else mask_token
super().__init__(
errors=UpperCamelCase_ , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , **UpperCamelCase_ , )
with open(UpperCamelCase_ , encoding="utf-8" ) as vocab_handle:
__UpperCAmelCase : int = json.load(UpperCamelCase_ )
__UpperCAmelCase : Any = {v: k for k, v in self.encoder.items()}
__UpperCAmelCase : Any = errors # how to handle errors in decoding
__UpperCAmelCase : str = bytes_to_unicode()
__UpperCAmelCase : List[str] = {v: k for k, v in self.byte_encoder.items()}
with open(UpperCamelCase_ , encoding="utf-8" ) as merges_handle:
__UpperCAmelCase : str = merges_handle.read().split("\n" )[1:-1]
__UpperCAmelCase : List[str] = [tuple(merge.split() ) for merge in bpe_merges]
__UpperCAmelCase : Union[str, Any] = dict(zip(UpperCamelCase_ , range(len(UpperCamelCase_ ) ) ) )
__UpperCAmelCase : Optional[int] = {}
__UpperCAmelCase : Optional[int] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__UpperCAmelCase : Dict = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def _snake_case ( self ):
return len(self.encoder )
def _snake_case ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self , UpperCamelCase_ ):
if token in self.cache:
return self.cache[token]
__UpperCAmelCase : List[str] = tuple(UpperCamelCase_ )
__UpperCAmelCase : str = get_pairs(UpperCamelCase_ )
if not pairs:
return token
while True:
__UpperCAmelCase : str = min(UpperCamelCase_ , key=lambda UpperCamelCase_ : self.bpe_ranks.get(UpperCamelCase_ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__UpperCAmelCase , __UpperCAmelCase : List[Any] = bigram
__UpperCAmelCase : Any = []
__UpperCAmelCase : List[str] = 0
while i < len(UpperCamelCase_ ):
try:
__UpperCAmelCase : Union[str, Any] = word.index(UpperCamelCase_ , UpperCamelCase_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__UpperCAmelCase : str = j
if word[i] == first and i < len(UpperCamelCase_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__UpperCAmelCase : Dict = tuple(UpperCamelCase_ )
__UpperCAmelCase : str = new_word
if len(UpperCamelCase_ ) == 1:
break
else:
__UpperCAmelCase : int = get_pairs(UpperCamelCase_ )
__UpperCAmelCase : Optional[int] = " ".join(UpperCamelCase_ )
__UpperCAmelCase : Dict = word
return word
def _snake_case ( self , UpperCamelCase_ ):
__UpperCAmelCase : Optional[Any] = []
for token in re.findall(self.pat , UpperCamelCase_ ):
__UpperCAmelCase : Any = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase_ ).split(" " ) )
return bpe_tokens
def _snake_case ( self , UpperCamelCase_ ):
return self.encoder.get(UpperCamelCase_ , self.encoder.get(self.unk_token ) )
def _snake_case ( self , UpperCamelCase_ ):
return self.decoder.get(UpperCamelCase_ )
def _snake_case ( self , UpperCamelCase_ ):
__UpperCAmelCase : List[str] = "".join(UpperCamelCase_ )
__UpperCAmelCase : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
if not os.path.isdir(UpperCamelCase_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__UpperCAmelCase : Any = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__UpperCAmelCase : Optional[int] = os.path.join(
UpperCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(UpperCamelCase_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCamelCase_ , ensure_ascii=UpperCamelCase_ ) + "\n" )
__UpperCAmelCase : str = 0
with open(UpperCamelCase_ , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda UpperCamelCase_ : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
__UpperCAmelCase : str = token_index
writer.write(" ".join(UpperCamelCase_ ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__UpperCAmelCase : List[Any] = [self.cls_token_id]
__UpperCAmelCase : Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase_ , token_ids_a=UpperCamelCase_ , already_has_special_tokens=UpperCamelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase_ )) + [1]
return [1] + ([0] * len(UpperCamelCase_ )) + [1, 1] + ([0] * len(UpperCamelCase_ )) + [1]
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
__UpperCAmelCase : int = [self.sep_token_id]
__UpperCAmelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_=False , **UpperCamelCase_ ):
__UpperCAmelCase : List[str] = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_ ) > 0 and not text[0].isspace()):
__UpperCAmelCase : Tuple = " " + text
return (text, kwargs)
| 10 | 0 |
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