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import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class __SCREAMING_SNAKE_CASE ( A__ ):
A : bool = field(default=A__ , metadata={'help': 'Whether to use SortishSampler or not.'} )
A : bool = field(
default=A__ , metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
A : Optional[int] = field(
default=A__ , metadata={
'help': (
'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
'to the `max_length` value of the model configuration.'
)
} , )
A : Optional[int] = field(
default=A__ , metadata={
'help': (
'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
'to the `num_beams` value of the model configuration.'
)
} , )
A : Optional[Union[str, Path, GenerationConfig]] = field(
default=A__ , metadata={
'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
} , )
def __lowerCamelCase ( self ):
lowercase : List[str] = super().to_dict()
for k, v in d.items():
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = v.to_dict()
return d
| 337 |
import logging
import os
from .state import PartialState
class __SCREAMING_SNAKE_CASE ( logging.LoggerAdapter ):
@staticmethod
def __lowerCamelCase ( SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
lowercase : List[str] = kwargs.pop('''main_process_only''' , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = kwargs.pop('''in_order''' , SCREAMING_SNAKE_CASE__ )
if self.isEnabledFor(SCREAMING_SNAKE_CASE__ ):
if self._should_log(SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : str = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
elif in_order:
lowercase : List[Any] = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
lowercase , lowercase : Union[str, Any] = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
state.wait_for_everyone()
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->List[Any]:
"""simple docstring"""
if log_level is None:
lowercase : str = os.environ.get('''ACCELERATE_LOG_LEVEL''', _UpperCamelCase )
lowercase : str = logging.getLogger(_UpperCamelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_UpperCamelCase, {} )
| 337 | 1 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
if index == number_of_items:
return 0
lowercase : Dict = 0
lowercase : Union[str, Any] = 0
lowercase : Optional[int] = knapsack(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, index + 1 )
if weights[index] <= max_weight:
lowercase : str = values[index] + knapsack(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, max_weight - weights[index], index + 1 )
return max(_UpperCamelCase, _UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super().__init__()
lowercase : Any = model
lowercase : Optional[Any] = 2
lowercase : Optional[int] = nn.Linear(self.model.config.hidden_size , self.num_labels )
def __lowerCamelCase ( self ):
pass
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : str = LongformerModel.from_pretrained(_UpperCamelCase )
lowercase : int = LightningModel(_UpperCamelCase )
lowercase : Union[str, Any] = torch.load(_UpperCamelCase, map_location=torch.device('''cpu''' ) )
lightning_model.load_state_dict(ckpt['''state_dict'''] )
# init longformer question answering model
lowercase : List[Any] = LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_UpperCamelCase )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 337 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 |
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
__a = logging.get_logger(__name__)
__a = {
'''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Any = 'yolos'
def __init__( self , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=3072 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=[512, 864] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=100 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = hidden_size
lowercase : int = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : str = intermediate_size
lowercase : Dict = hidden_act
lowercase : int = hidden_dropout_prob
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : List[Any] = initializer_range
lowercase : Optional[int] = layer_norm_eps
lowercase : str = image_size
lowercase : Dict = patch_size
lowercase : str = num_channels
lowercase : Optional[int] = qkv_bias
lowercase : List[str] = num_detection_tokens
lowercase : List[str] = use_mid_position_embeddings
lowercase : Dict = auxiliary_loss
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : Any = bbox_cost
lowercase : int = giou_cost
# Loss coefficients
lowercase : Dict = bbox_loss_coefficient
lowercase : Optional[Any] = giou_loss_coefficient
lowercase : Tuple = eos_coefficient
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = version.parse('1.11' )
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
@property
def __lowerCamelCase ( self ):
return 12
| 337 | 1 |
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : Optional[Any] = len(_UpperCamelCase )
lowercase : Dict = sum(_UpperCamelCase )
lowercase : int = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1, n + 1 ):
lowercase : Any = True
for i in range(1, s + 1 ):
lowercase : str = False
for i in range(1, n + 1 ):
for j in range(1, s + 1 ):
lowercase : Any = dp[i][j - 1]
if arr[i - 1] <= j:
lowercase : int = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ), -1, -1 ):
if dp[n][j] is True:
lowercase : List[Any] = s - 2 * j
break
return diff
| 337 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__a = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""simple docstring"""
if got_ver is None or want_ver is None:
raise ValueError(
f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
f""" reinstalling {pkg}.""" )
if not ops[op](version.parse(_UpperCamelCase ), version.parse(_UpperCamelCase ) ):
raise ImportError(
f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->None:
"""simple docstring"""
lowercase : List[Any] = f"""\n{hint}""" if hint is not None else ''''''
# non-versioned check
if re.match(R'''^[\w_\-\d]+$''', _UpperCamelCase ):
lowercase , lowercase , lowercase : Optional[Any] = requirement, None, None
else:
lowercase : List[Any] = re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
f""" got {requirement}""" )
lowercase , lowercase : str = match[0]
lowercase : Tuple = want_full.split(''',''' ) # there could be multiple requirements
lowercase : List[Any] = {}
for w in want_range:
lowercase : str = re.findall(R'''^([\s!=<>]{1,2})(.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
f""" but got {requirement}""" )
lowercase , lowercase : Optional[int] = match[0]
lowercase : Dict = want_ver
if op not in ops:
raise ValueError(f"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )
# special case
if pkg == "python":
lowercase : int = '''.'''.join([str(_UpperCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
return
# check if any version is installed
try:
lowercase : List[str] = importlib.metadata.version(_UpperCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
f"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : Optional[int] = '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
return require_version(_UpperCamelCase, _UpperCamelCase )
| 337 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
lowercase : Optional[int] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
lowercase : Union[str, Any] = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=3 , )
return model
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
lowercase : List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : List[Any] = self.dummy_uncond_unet
lowercase : Optional[int] = DDIMScheduler()
lowercase : Optional[int] = self.dummy_vq_model
lowercase : Optional[Any] = LDMPipeline(unet=SCREAMING_SNAKE_CASE__ , vqvae=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
ldm.to(SCREAMING_SNAKE_CASE__ )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = torch.manual_seed(0 )
lowercase : Dict = ldm(generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type='''numpy''' ).images
lowercase : Union[str, Any] = torch.manual_seed(0 )
lowercase : List[str] = ldm(generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type='''numpy''' , return_dict=SCREAMING_SNAKE_CASE__ )[0]
lowercase : str = image[0, -3:, -3:, -1]
lowercase : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase : Union[str, Any] = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
lowercase : Tuple = 1E-2 if torch_device != '''mps''' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256''' )
ldm.to(SCREAMING_SNAKE_CASE__ )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = torch.manual_seed(0 )
lowercase : Tuple = ldm(generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=5 , output_type='''numpy''' ).images
lowercase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase : Any = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447] )
lowercase : Union[str, Any] = 1E-2 if torch_device != '''mps''' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 337 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'deta'
A : Dict = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=900 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=1024 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="relu" , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="sine" , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=300 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.25 , **SCREAMING_SNAKE_CASE__ , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase : Tuple = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = backbone_config.pop('''model_type''' )
lowercase : Any = CONFIG_MAPPING[backbone_model_type]
lowercase : List[Any] = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = backbone_config
lowercase : Union[str, Any] = num_queries
lowercase : Any = max_position_embeddings
lowercase : int = d_model
lowercase : Any = encoder_ffn_dim
lowercase : Optional[int] = encoder_layers
lowercase : Tuple = encoder_attention_heads
lowercase : Optional[Any] = decoder_ffn_dim
lowercase : Optional[int] = decoder_layers
lowercase : int = decoder_attention_heads
lowercase : Any = dropout
lowercase : int = attention_dropout
lowercase : Dict = activation_dropout
lowercase : int = activation_function
lowercase : Dict = init_std
lowercase : List[str] = init_xavier_std
lowercase : Optional[Any] = encoder_layerdrop
lowercase : Tuple = auxiliary_loss
lowercase : Tuple = position_embedding_type
# deformable attributes
lowercase : List[str] = num_feature_levels
lowercase : Tuple = encoder_n_points
lowercase : Optional[int] = decoder_n_points
lowercase : Tuple = two_stage
lowercase : Optional[Any] = two_stage_num_proposals
lowercase : Union[str, Any] = with_box_refine
lowercase : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : str = bbox_cost
lowercase : List[Any] = giou_cost
# Loss coefficients
lowercase : Tuple = mask_loss_coefficient
lowercase : Any = dice_loss_coefficient
lowercase : Dict = bbox_loss_coefficient
lowercase : Tuple = giou_loss_coefficient
lowercase : Union[str, Any] = eos_coefficient
lowercase : Tuple = focal_alpha
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __lowerCamelCase ( self ):
return self.encoder_attention_heads
@property
def __lowerCamelCase ( self ):
return self.d_model
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase : Any = self.backbone_config.to_dict()
lowercase : List[str] = self.__class__.model_type
return output
| 337 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__a = logging.get_logger('''transformers.models.speecht5''')
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
hf_model.apply_weight_norm()
lowercase : str = checkpoint['''input_conv.weight_g''']
lowercase : Optional[Any] = checkpoint['''input_conv.weight_v''']
lowercase : Tuple = checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
lowercase : List[Any] = checkpoint[f"""upsamples.{i}.1.weight_g"""]
lowercase : Dict = checkpoint[f"""upsamples.{i}.1.weight_v"""]
lowercase : Any = checkpoint[f"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
lowercase : Dict = checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
lowercase : Dict = checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
lowercase : List[str] = checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]
lowercase : int = checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
lowercase : str = checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
lowercase : str = checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]
lowercase : Dict = checkpoint['''output_conv.1.weight_g''']
lowercase : List[str] = checkpoint['''output_conv.1.weight_v''']
lowercase : str = checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=None, _UpperCamelCase=None, ) ->Tuple:
"""simple docstring"""
if config_path is not None:
lowercase : Optional[Any] = SpeechTaHifiGanConfig.from_pretrained(_UpperCamelCase )
else:
lowercase : Optional[int] = SpeechTaHifiGanConfig()
lowercase : Optional[int] = SpeechTaHifiGan(_UpperCamelCase )
lowercase : List[Any] = torch.load(_UpperCamelCase )
load_weights(orig_checkpoint['''model''']['''generator'''], _UpperCamelCase, _UpperCamelCase )
lowercase : Optional[Any] = np.load(_UpperCamelCase )
lowercase : List[str] = stats[0].reshape(-1 )
lowercase : List[Any] = stats[1].reshape(-1 )
lowercase : Dict = torch.from_numpy(_UpperCamelCase ).float()
lowercase : Union[str, Any] = torch.from_numpy(_UpperCamelCase ).float()
model.save_pretrained(_UpperCamelCase )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(_UpperCamelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
parser.add_argument('''--stats_path''', required=True, default=None, type=str, help='''Path to stats.npy file''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
__a = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 337 |
def __lowercase ( ) ->List[Any]:
"""simple docstring"""
lowercase : Union[str, Any] = 0
for i in range(1, 1001 ):
total += i**i
return str(_UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 337 | 1 |
from collections.abc import Callable
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
# Stores actual heap items.
lowercase : list = []
# Stores indexes of each item for supporting updates and deletion.
lowercase : dict = {}
# Stores current size of heap.
lowercase : str = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowercase : Tuple = key or (lambda SCREAMING_SNAKE_CASE__ : x)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return int((i - 1) / 2 ) if i > 0 else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : Dict = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowercase , lowercase : int = self.arr[j], self.arr[i]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.arr[i][1] < self.arr[j][1]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : int = self._left(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = self._right(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = i
if left is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = left
if right is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = right
return valid_parent
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = self._parent(SCREAMING_SNAKE_CASE__ )
while parent is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : Optional[int] = parent, self._parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
while valid_parent != index:
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : str = valid_parent, self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : str = self.pos_map[item]
lowercase : Optional[int] = [item, self.key(SCREAMING_SNAKE_CASE__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : List[str] = self.pos_map[item]
del self.pos_map[item]
lowercase : Optional[int] = self.arr[self.size - 1]
lowercase : int = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(SCREAMING_SNAKE_CASE__ )] )
else:
lowercase : int = [item, self.key(SCREAMING_SNAKE_CASE__ )]
lowercase : str = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowerCamelCase ( self ):
return self.arr[0] if self.size else None
def __lowerCamelCase ( self ):
lowercase : str = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def __lowercase ( ) ->None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__a = 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_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__a = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self ):
lowercase : str = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
lowercase : Any = self.diffusers_dir
shutil.copy(
os.path.join(SCREAMING_SNAKE_CASE__ , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def __lowerCamelCase ( self ):
lowercase : List[Any] = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
lowercase : Tuple = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
lowercase : str = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
lowercase : Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowercase : List[Any] = black.format_str(SCREAMING_SNAKE_CASE__ , mode=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , newline='''\n''' ) as f:
f.write(SCREAMING_SNAKE_CASE__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , '''r''' ) as f:
self.assertTrue(f.read() , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Tuple = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with a really long name
lowercase : List[Any] = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , overwrite_result=re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
| 337 | 1 |
def __lowercase ( ) ->List[Any]:
"""simple docstring"""
lowercase : Union[str, Any] = 0
for i in range(1, 1001 ):
total += i**i
return str(_UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 337 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=0 ): # a graph with Node 0,1,...,N-1
lowercase : List[Any] = n
lowercase : List[Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # adjacency matrix for weight
lowercase : Union[str, Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # dp[i][j] stores minimum distance from i to j
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : int = w
def __lowerCamelCase ( self ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase : Any = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.dp[u][v]
if __name__ == "__main__":
__a = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 337 | 1 |
def __lowercase ( _UpperCamelCase ) ->str:
"""simple docstring"""
lowercase : Union[str, Any] = int(_UpperCamelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(_UpperCamelCase )
lowercase , lowercase : Optional[int] = divmod(_UpperCamelCase, 2 )
return binary_recursive(_UpperCamelCase ) + str(_UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->str:
"""simple docstring"""
lowercase : str = str(_UpperCamelCase ).strip()
if not number:
raise ValueError('''No input value was provided''' )
lowercase : List[Any] = '''-''' if number.startswith('''-''' ) else ''''''
lowercase : List[Any] = number.lstrip('''-''' )
if not number.isnumeric():
raise ValueError('''Input value is not an integer''' )
return f"""{negative}0b{binary_recursive(int(_UpperCamelCase ) )}"""
if __name__ == "__main__":
from doctest import testmod
testmod()
| 337 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(_UpperCamelCase ) / len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
from __future__ import annotations
__a = []
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_UpperCamelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, -1, -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, len(_UpperCamelCase ) ) ):
if board[i][j] == 1:
return False
return True
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
if row >= len(_UpperCamelCase ):
solution.append(_UpperCamelCase )
printboard(_UpperCamelCase )
print()
return True
for i in range(len(_UpperCamelCase ) ):
if is_safe(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : int = 1
solve(_UpperCamelCase, row + 1 )
lowercase : Tuple = 0
return False
def __lowercase ( _UpperCamelCase ) ->None:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
for j in range(len(_UpperCamelCase ) ):
if board[i][j] == 1:
print('''Q''', end=''' ''' )
else:
print('''.''', end=''' ''' )
print()
# n=int(input("The no. of queens"))
__a = 8
__a = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 337 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 337 | 1 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->list[list[str]]:
"""simple docstring"""
lowercase : Dict = word_bank or []
# create a table
lowercase : int = len(_UpperCamelCase ) + 1
lowercase : list[list[list[str]]] = []
for _ in range(_UpperCamelCase ):
table.append([] )
# seed value
lowercase : List[Any] = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(_UpperCamelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(_UpperCamelCase )] == word:
lowercase : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(_UpperCamelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(_UpperCamelCase )]:
combination.reverse()
return table[len(_UpperCamelCase )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
)
| 337 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase ) ->List[int]:
"""simple docstring"""
if isinstance(_UpperCamelCase, np.ndarray ):
return list(tensor.shape )
lowercase : Optional[Any] = tf.shape(_UpperCamelCase )
if tensor.shape == tf.TensorShape(_UpperCamelCase ):
return dynamic
lowercase : Tuple = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_UpperCamelCase )]
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None, _UpperCamelCase = None ) ->tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9, axis=_UpperCamelCase, name=_UpperCamelCase )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=1e-5, _UpperCamelCase=-1 ) ->int:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_UpperCamelCase, _UpperCamelCase ):
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
lowercase , lowercase : Union[str, Any] = tf.nn.moments(_UpperCamelCase, axes=[axis], keepdims=_UpperCamelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase : int = [1] * inputs.shape.rank
lowercase : Union[str, Any] = shape_list(_UpperCamelCase )[axis]
lowercase : List[str] = tf.reshape(_UpperCamelCase, _UpperCamelCase )
lowercase : Dict = tf.reshape(_UpperCamelCase, _UpperCamelCase )
# Compute layer normalization using the batch_normalization
# function.
lowercase : List[str] = tf.nn.batch_normalization(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, offset=_UpperCamelCase, scale=_UpperCamelCase, variance_epsilon=_UpperCamelCase, )
return outputs
def __lowercase ( _UpperCamelCase, _UpperCamelCase=0, _UpperCamelCase=-1 ) ->List[Any]:
"""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
lowercase : Dict = tf.shape(_UpperCamelCase )
lowercase : Optional[Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase : List[str] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]], axis=0 )
return tf.reshape(_UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->tf.Tensor:
"""simple docstring"""
if not isinstance(_UpperCamelCase, tf.Tensor ):
lowercase : Optional[Any] = tf.convert_to_tensor(_UpperCamelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase : Tuple = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase : str = (
tf.cast(1, encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = "input_ids" ) ->None:
"""simple docstring"""
tf.debugging.assert_less(
_UpperCamelCase, tf.cast(_UpperCamelCase, dtype=tensor.dtype ), message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(_UpperCamelCase )}) must be smaller than the embedding """
f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
), )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : List[Any] = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase : Optional[int] = [x for x in data if len(_UpperCamelCase ) > 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}""" )
lowercase : Any = np.asarray(_UpperCamelCase )
lowercase : List[Any] = 1
lowercase : Tuple = np.array_split(_UpperCamelCase, _UpperCamelCase )
# 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
lowercase : Dict = np.array_split(_UpperCamelCase, _UpperCamelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_UpperCamelCase ):
lowercase : Optional[int] = chunk_data
else:
lowercase : int = data
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
if name in group.attrs:
lowercase : str = [n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs[name]]
else:
lowercase : Optional[Any] = []
lowercase : List[str] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
def _expand_single_ad_tensor(_UpperCamelCase ):
if isinstance(_UpperCamelCase, tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_UpperCamelCase, axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor, _UpperCamelCase )
| 337 | 1 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
lowercase : Union[str, Any] = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_UpperCamelCase ) )
return round(_UpperCamelCase, ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
def __lowercase ( _UpperCamelCase = 4000000 ) ->int:
"""simple docstring"""
lowercase : int = []
lowercase , lowercase : str = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCamelCase )
lowercase , lowercase : Dict = b, a + b
return sum(_UpperCamelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__a = logging.get_logger(__name__)
# TODO: upload to AWS
__a = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
),
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Optional[Any] = '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__ , ):
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = vocab_size
lowercase : Any = hidden_size
lowercase : Optional[Any] = num_hidden_layers
lowercase : List[str] = num_attention_heads
lowercase : str = hidden_act
lowercase : Dict = intermediate_size
lowercase : Union[str, Any] = hidden_dropout_prob
lowercase : str = attention_probs_dropout_prob
lowercase : List[str] = max_position_embeddings
lowercase : int = type_vocab_size
lowercase : Union[str, Any] = initializer_range
lowercase : str = layer_norm_eps
lowercase : List[Any] = share_encoders
lowercase : str = projection_dim
| 337 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
__a = logging.get_logger(__name__)
__a = {
'''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'perceiver'
def __init__( self , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=1280 , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=26 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="kv" , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=262 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=56 , SCREAMING_SNAKE_CASE__=[368, 496] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=1920 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=[1, 16, 224, 224] , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Any = num_latents
lowercase : Union[str, Any] = d_latents
lowercase : str = d_model
lowercase : int = num_blocks
lowercase : str = num_self_attends_per_block
lowercase : List[str] = num_self_attention_heads
lowercase : List[str] = num_cross_attention_heads
lowercase : int = qk_channels
lowercase : List[Any] = v_channels
lowercase : int = cross_attention_shape_for_attention
lowercase : Tuple = self_attention_widening_factor
lowercase : Dict = cross_attention_widening_factor
lowercase : Any = hidden_act
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : Union[str, Any] = initializer_range
lowercase : Any = layer_norm_eps
lowercase : Any = use_query_residual
# masked language modeling attributes
lowercase : List[str] = vocab_size
lowercase : Dict = max_position_embeddings
# image classification attributes
lowercase : int = image_size
# flow attributes
lowercase : List[Any] = train_size
# multimodal autoencoding attributes
lowercase : List[Any] = num_frames
lowercase : Union[str, Any] = audio_samples_per_frame
lowercase : int = samples_per_patch
lowercase : Optional[int] = output_shape
class __SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCamelCase ( self ):
if self.task == "multiple-choice":
lowercase : Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase : Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''inputs''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 3 , SCREAMING_SNAKE_CASE__ = 40 , SCREAMING_SNAKE_CASE__ = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase : str = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase : Union[str, Any] = preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=SCREAMING_SNAKE_CASE__ )
# Generate dummy inputs according to compute batch and sequence
lowercase : Optional[Any] = [''' '''.join(['''a'''] ) * seq_length] * batch_size
lowercase : Any = dict(preprocessor(SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = inputs.pop('''input_ids''' )
return inputs
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase : List[str] = compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_batch )
lowercase : List[str] = self._generate_dummy_images(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = dict(preprocessor(images=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = inputs.pop('''pixel_values''' )
return inputs
else:
raise ValueError(
'''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''' )
| 337 | 1 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->list[int]:
"""simple docstring"""
return [ord(_UpperCamelCase ) - 96 for elem in plain]
def __lowercase ( _UpperCamelCase ) ->str:
"""simple docstring"""
return "".join(chr(elem + 96 ) for elem in encoded )
def __lowercase ( ) ->None:
"""simple docstring"""
lowercase : Optional[Any] = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''', _UpperCamelCase )
print('''Decoded:''', decode(_UpperCamelCase ) )
if __name__ == "__main__":
main()
| 337 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowercase ( _UpperCamelCase = 8 ) ->str:
"""simple docstring"""
lowercase : List[str] = ascii_letters + digits + punctuation
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
i -= len(_UpperCamelCase )
lowercase : Dict = i // 3
lowercase : List[str] = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowercase : Union[str, Any] = (
chars_incl
+ random(_UpperCamelCase, quotient + remainder )
+ random(_UpperCamelCase, _UpperCamelCase )
+ random(_UpperCamelCase, _UpperCamelCase )
)
lowercase : Union[str, Any] = list(_UpperCamelCase )
shuffle(_UpperCamelCase )
return "".join(_UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Dict:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[Any]:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase = 8 ) ->bool:
"""simple docstring"""
if len(_UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowercase : str = any(char in ascii_uppercase for char in password )
lowercase : List[str] = any(char in ascii_lowercase for char in password )
lowercase : Dict = any(char in digits for char in password )
lowercase : Tuple = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def __lowercase ( ) ->Dict:
"""simple docstring"""
lowercase : Union[str, Any] = int(input('''Please indicate the max length of your password: ''' ).strip() )
lowercase : Optional[Any] = input(
'''Please indicate the characters that must be in your password: ''' ).strip()
print('''Password generated:''', password_generator(_UpperCamelCase ) )
print(
'''Alternative Password generated:''', alternative_password_generator(_UpperCamelCase, _UpperCamelCase ), )
print('''[If you are thinking of using this passsword, You better save it.]''' )
if __name__ == "__main__":
main()
| 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__a = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''UniSpeechForCTC''',
'''UniSpeechForPreTraining''',
'''UniSpeechForSequenceClassification''',
'''UniSpeechModel''',
'''UniSpeechPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 |
from __future__ import annotations
__a = []
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_UpperCamelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, -1, -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, len(_UpperCamelCase ) ) ):
if board[i][j] == 1:
return False
return True
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
if row >= len(_UpperCamelCase ):
solution.append(_UpperCamelCase )
printboard(_UpperCamelCase )
print()
return True
for i in range(len(_UpperCamelCase ) ):
if is_safe(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : int = 1
solve(_UpperCamelCase, row + 1 )
lowercase : Tuple = 0
return False
def __lowercase ( _UpperCamelCase ) ->None:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
for j in range(len(_UpperCamelCase ) ):
if board[i][j] == 1:
print('''Q''', end=''' ''' )
else:
print('''.''', end=''' ''' )
print()
# n=int(input("The no. of queens"))
__a = 8
__a = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 337 | 1 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__a = HfApi()
__a = {}
# fmt: off
__a = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
-1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
__a = torch.tensor([
-2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
-2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
__a = torch.tensor([
-0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
-0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
-0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
__a = torch.tensor([
0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
-0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
-0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
__a = torch.tensor([
0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
-0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
-0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
__a = torch.tensor([
0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
-0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
-0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
__a = torch.tensor([
0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
-0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
-0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
__a = torch.tensor([
0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
-0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
-0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
__a = torch.tensor([
-1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
-2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
__a = torch.tensor([
-1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
-2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
__a = torch.tensor([
-1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
-2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
__a = torch.tensor([
-2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
-3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
__a = torch.tensor([
-2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
-2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
__a = torch.tensor([
-2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
-3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
__a = torch.tensor([
-1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
-2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on
__a = api.list_models(filter='''diffusers''')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
__a = '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]
print(F'''Started running {mod.modelId}!!!''')
if mod.modelId.startswith('''CompVis'''):
__a = UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
else:
__a = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
__a = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
__a = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
__a = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1e-3
)
print(F'''{mod.modelId} has passed successfully!!!''')
| 337 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CTRLForSequenceClassification''',
'''CTRLLMHeadModel''',
'''CTRLModel''',
'''CTRLPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCTRLForSequenceClassification''',
'''TFCTRLLMHeadModel''',
'''TFCTRLModel''',
'''TFCTRLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 1 / sqrt(2 ) ) ->IIRFilter:
"""simple docstring"""
lowercase : Optional[Any] = tau * frequency / samplerate
lowercase : Dict = sin(_UpperCamelCase )
lowercase : Tuple = cos(_UpperCamelCase )
lowercase : str = _sin / (2 * q_factor)
lowercase : List[Any] = (1 - _cos) / 2
lowercase : int = 1 - _cos
lowercase : Dict = 1 + alpha
lowercase : Any = -2 * _cos
lowercase : Union[str, Any] = 1 - alpha
lowercase : List[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa], [ba, ba, ba] )
return filt
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 1 / sqrt(2 ) ) ->IIRFilter:
"""simple docstring"""
lowercase : str = tau * frequency / samplerate
lowercase : List[Any] = sin(_UpperCamelCase )
lowercase : int = cos(_UpperCamelCase )
lowercase : List[Any] = _sin / (2 * q_factor)
lowercase : List[Any] = (1 + _cos) / 2
lowercase : Optional[Any] = -1 - _cos
lowercase : Tuple = 1 + alpha
lowercase : Tuple = -2 * _cos
lowercase : Optional[int] = 1 - alpha
lowercase : Dict = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa], [ba, ba, ba] )
return filt
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 1 / sqrt(2 ) ) ->IIRFilter:
"""simple docstring"""
lowercase : Dict = tau * frequency / samplerate
lowercase : str = sin(_UpperCamelCase )
lowercase : Optional[int] = cos(_UpperCamelCase )
lowercase : Optional[int] = _sin / (2 * q_factor)
lowercase : Optional[int] = _sin / 2
lowercase : Tuple = 0
lowercase : List[str] = -ba
lowercase : int = 1 + alpha
lowercase : Any = -2 * _cos
lowercase : Any = 1 - alpha
lowercase : List[str] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa], [ba, ba, ba] )
return filt
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 1 / sqrt(2 ) ) ->IIRFilter:
"""simple docstring"""
lowercase : str = tau * frequency / samplerate
lowercase : Union[str, Any] = sin(_UpperCamelCase )
lowercase : Any = cos(_UpperCamelCase )
lowercase : Optional[Any] = _sin / (2 * q_factor)
lowercase : List[str] = 1 - alpha
lowercase : List[Any] = -2 * _cos
lowercase : Union[str, Any] = 1 + alpha
lowercase : Union[str, Any] = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba], [ba, ba, ba] )
return filt
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 1 / sqrt(2 ), ) ->IIRFilter:
"""simple docstring"""
lowercase : Optional[int] = tau * frequency / samplerate
lowercase : Tuple = sin(_UpperCamelCase )
lowercase : Union[str, Any] = cos(_UpperCamelCase )
lowercase : List[Any] = _sin / (2 * q_factor)
lowercase : Optional[Any] = 10 ** (gain_db / 40)
lowercase : List[str] = 1 + alpha * big_a
lowercase : str = -2 * _cos
lowercase : Tuple = 1 - alpha * big_a
lowercase : int = 1 + alpha / big_a
lowercase : Optional[Any] = -2 * _cos
lowercase : Optional[Any] = 1 - alpha / big_a
lowercase : Union[str, Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa], [ba, ba, ba] )
return filt
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 1 / sqrt(2 ), ) ->IIRFilter:
"""simple docstring"""
lowercase : Tuple = tau * frequency / samplerate
lowercase : List[Any] = sin(_UpperCamelCase )
lowercase : List[str] = cos(_UpperCamelCase )
lowercase : Union[str, Any] = _sin / (2 * q_factor)
lowercase : Dict = 10 ** (gain_db / 40)
lowercase : Optional[int] = (big_a + 1) - (big_a - 1) * _cos
lowercase : str = (big_a + 1) + (big_a - 1) * _cos
lowercase : Union[str, Any] = (big_a - 1) - (big_a + 1) * _cos
lowercase : int = (big_a - 1) + (big_a + 1) * _cos
lowercase : str = 2 * sqrt(_UpperCamelCase ) * alpha
lowercase : Dict = big_a * (pmc + aaa)
lowercase : List[str] = 2 * big_a * mpc
lowercase : Optional[int] = big_a * (pmc - aaa)
lowercase : Tuple = ppmc + aaa
lowercase : Optional[int] = -2 * pmpc
lowercase : Union[str, Any] = ppmc - aaa
lowercase : Any = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa], [ba, ba, ba] )
return filt
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 1 / sqrt(2 ), ) ->IIRFilter:
"""simple docstring"""
lowercase : Dict = tau * frequency / samplerate
lowercase : int = sin(_UpperCamelCase )
lowercase : Optional[Any] = cos(_UpperCamelCase )
lowercase : Optional[int] = _sin / (2 * q_factor)
lowercase : List[str] = 10 ** (gain_db / 40)
lowercase : Any = (big_a + 1) - (big_a - 1) * _cos
lowercase : Dict = (big_a + 1) + (big_a - 1) * _cos
lowercase : List[str] = (big_a - 1) - (big_a + 1) * _cos
lowercase : List[str] = (big_a - 1) + (big_a + 1) * _cos
lowercase : List[str] = 2 * sqrt(_UpperCamelCase ) * alpha
lowercase : Dict = big_a * (ppmc + aaa)
lowercase : Dict = -2 * big_a * pmpc
lowercase : Optional[int] = big_a * (ppmc - aaa)
lowercase : Dict = pmc + aaa
lowercase : List[Any] = 2 * mpc
lowercase : Union[str, Any] = pmc - aaa
lowercase : Union[str, Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa], [ba, ba, ba] )
return filt
| 337 |
from collections.abc import Callable
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
# Stores actual heap items.
lowercase : list = []
# Stores indexes of each item for supporting updates and deletion.
lowercase : dict = {}
# Stores current size of heap.
lowercase : str = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowercase : Tuple = key or (lambda SCREAMING_SNAKE_CASE__ : x)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return int((i - 1) / 2 ) if i > 0 else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : Dict = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowercase , lowercase : int = self.arr[j], self.arr[i]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.arr[i][1] < self.arr[j][1]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : int = self._left(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = self._right(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = i
if left is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = left
if right is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = right
return valid_parent
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = self._parent(SCREAMING_SNAKE_CASE__ )
while parent is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : Optional[int] = parent, self._parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
while valid_parent != index:
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : str = valid_parent, self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : str = self.pos_map[item]
lowercase : Optional[int] = [item, self.key(SCREAMING_SNAKE_CASE__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : List[str] = self.pos_map[item]
del self.pos_map[item]
lowercase : Optional[int] = self.arr[self.size - 1]
lowercase : int = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(SCREAMING_SNAKE_CASE__ )] )
else:
lowercase : int = [item, self.key(SCREAMING_SNAKE_CASE__ )]
lowercase : str = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowerCamelCase ( self ):
return self.arr[0] if self.size else None
def __lowerCamelCase ( self ):
lowercase : str = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def __lowercase ( ) ->None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
from __future__ import annotations
__a = 8.988e9 # units = N * m^s * C^-2
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->dict[str, float]:
"""simple docstring"""
lowercase : Tuple = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
lowercase : List[Any] = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
lowercase : List[str] = abs(_UpperCamelCase ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
lowercase : str = abs(_UpperCamelCase ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
lowercase : Union[str, Any] = (COULOMBS_CONSTANT * charge_product / abs(_UpperCamelCase )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[List[np.ndarray], torch.FloatTensor]
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 .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 337 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[str, Any] = ['image_processor', 'tokenizer']
A : str = 'AutoImageProcessor'
A : Dict = 'AutoTokenizer'
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
super().__init__(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : int = self.image_processor
def __call__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ ):
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:
lowercase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
if images is not None:
lowercase : Optional[Any] = self.image_processor(SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
if text is not None and images is not None:
lowercase : Union[str, Any] = 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 __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __lowerCamelCase ( self ):
return ["input_ids", "attention_mask", "pixel_values"]
| 337 |
import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__a = logging.get_logger(__name__)
__a = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__a = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__a = {'''facebook/blenderbot-3B''': 1_28}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Dict = VOCAB_FILES_NAMES
A : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Optional[int] = ['input_ids', 'attention_mask']
A : str = BlenderbotTokenizer
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="replace" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , tokenizer_file=SCREAMING_SNAKE_CASE__ , errors=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ , trim_offsets=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
lowercase : str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : List[Any] = getattr(SCREAMING_SNAKE_CASE__ , pre_tok_state.pop('''type''' ) )
lowercase : str = add_prefix_space
lowercase : List[Any] = pre_tok_class(**SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = add_prefix_space
lowercase : str = '''post_processor'''
lowercase : str = getattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if tokenizer_component_instance:
lowercase : Optional[int] = 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:
lowercase : Tuple = tuple(state['''sep'''] )
if "cls" in state:
lowercase : Union[str, Any] = tuple(state['''cls'''] )
lowercase : Optional[int] = False
if state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : Any = add_prefix_space
lowercase : Tuple = True
if state.get('''trim_offsets''' , SCREAMING_SNAKE_CASE__ ) != trim_offsets:
lowercase : List[str] = trim_offsets
lowercase : Optional[int] = True
if changes_to_apply:
lowercase : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE__ , state.pop('''type''' ) )
lowercase : Union[str, Any] = component_class(**SCREAMING_SNAKE_CASE__ )
setattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def __lowerCamelCase ( self ):
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else value
lowercase : Any = value
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
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(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Any = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
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(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : int = self._tokenizer.model.save(SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
return tuple(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Tuple = [self.sep_token_id]
lowercase : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
return token_ids_a + [self.eos_token_id]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = ''' '''.join(SCREAMING_SNAKE_CASE__ )
lowercase : Any = self.encode(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > self.model_max_length:
lowercase : Tuple = input_ids[-self.model_max_length :]
logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 337 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super().__init__()
lowercase : Any = model
lowercase : Optional[Any] = 2
lowercase : Optional[int] = nn.Linear(self.model.config.hidden_size , self.num_labels )
def __lowerCamelCase ( self ):
pass
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : str = LongformerModel.from_pretrained(_UpperCamelCase )
lowercase : int = LightningModel(_UpperCamelCase )
lowercase : Union[str, Any] = torch.load(_UpperCamelCase, map_location=torch.device('''cpu''' ) )
lightning_model.load_state_dict(ckpt['''state_dict'''] )
# init longformer question answering model
lowercase : List[Any] = LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_UpperCamelCase )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 337 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ) ->int:
"""simple docstring"""
lowercase : Tuple = HfArgumentParser(_UpperCamelCase )
lowercase : List[str] = parser.parse_args_into_dataclasses()[0]
lowercase : Optional[int] = TensorFlowBenchmark(args=_UpperCamelCase )
try:
lowercase : Any = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase : Optional[int] = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
lowercase : Any = ''' '''.join(str(_UpperCamelCase ).split(''' ''' )[:-1] )
lowercase : Any = ''''''
lowercase : str = eval(str(_UpperCamelCase ).split(''' ''' )[-1] )
lowercase : List[str] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
lowercase : Union[str, Any] = full_error_msg + begin_error_msg + str(_UpperCamelCase )
raise ValueError(_UpperCamelCase )
benchmark.run()
if __name__ == "__main__":
main()
| 337 | 1 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Dict = 'vision-encoder-decoder'
A : Optional[int] = True
def __init__( self , **SCREAMING_SNAKE_CASE__ ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"""A configuraton of type {self.model_type} cannot be instantiated because """
f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
lowercase : Optional[int] = kwargs.pop('''encoder''' )
lowercase : str = encoder_config.pop('''model_type''' )
lowercase : Optional[Any] = kwargs.pop('''decoder''' )
lowercase : Any = decoder_config.pop('''model_type''' )
lowercase : Optional[int] = AutoConfig.for_model(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
lowercase : int = AutoConfig.for_model(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
lowercase : int = True
@classmethod
def __lowerCamelCase ( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
lowercase : Optional[Any] = True
lowercase : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = copy.deepcopy(self.__dict__ )
lowercase : List[Any] = self.encoder.to_dict()
lowercase : Dict = self.decoder.to_dict()
lowercase : List[Any] = self.__class__.model_type
return output
class __SCREAMING_SNAKE_CASE ( A__ ):
A : str = version.parse('1.11' )
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
@property
def __lowerCamelCase ( self ):
return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}} )
class __SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCamelCase ( self ):
lowercase : Dict = OrderedDict()
lowercase : Optional[Any] = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
lowercase : Optional[int] = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
lowercase : Optional[Any] = {0: '''batch''', 1: '''encoder_sequence'''}
return common_inputs
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , ):
import torch
lowercase : List[Any] = OrderedDict()
lowercase : Optional[Any] = super().generate_dummy_inputs(
SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , seq_length=SCREAMING_SNAKE_CASE__ , is_pair=SCREAMING_SNAKE_CASE__ , framework=SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : Optional[int] = dummy_input['''input_ids'''].shape
lowercase : Dict = (batch, encoder_sequence, self._config.encoder_hidden_size)
lowercase : Dict = dummy_input.pop('''input_ids''' )
lowercase : Union[str, Any] = dummy_input.pop('''attention_mask''' )
lowercase : Tuple = torch.zeros(SCREAMING_SNAKE_CASE__ )
return common_inputs
class __SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "default" ):
lowercase : int = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
| 337 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : Union[str, Any] = [False] * len(_UpperCamelCase )
lowercase : Optional[int] = []
queue.append(_UpperCamelCase )
lowercase : Union[str, Any] = True
while queue:
lowercase : List[str] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_UpperCamelCase )
lowercase : Tuple = True
lowercase : Optional[Any] = u
return visited[t]
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : List[str] = [-1] * (len(_UpperCamelCase ))
lowercase : int = 0
while bfs(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : List[str] = float('''Inf''' )
lowercase : int = sink
while s != source:
# Find the minimum value in select path
lowercase : List[Any] = min(_UpperCamelCase, graph[parent[s]][s] )
lowercase : Union[str, Any] = parent[s]
max_flow += path_flow
lowercase : Optional[int] = sink
while v != source:
lowercase : Any = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowercase : Union[str, Any] = parent[v]
return max_flow
__a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__a , __a = 0, 5
print(ford_fulkerson(graph, source, sink))
| 337 | 1 |
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
if not isinstance(_UpperCamelCase, _UpperCamelCase ):
lowercase : List[str] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(_UpperCamelCase )
if number < 1:
lowercase : str = f"""Input value of [number={number}] must be > 0"""
raise ValueError(_UpperCamelCase )
lowercase : str = 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()
| 337 |
from typing import List
from .keymap import KEYMAP, get_character
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : str = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += [key]
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
def __lowercase ( *_UpperCamelCase ) ->Any:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : List[Any] = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += keys
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
class __SCREAMING_SNAKE_CASE ( A__ ):
def __new__( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = super().__new__(cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not hasattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' ):
setattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' , {} )
setattr(SCREAMING_SNAKE_CASE__ , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
lowercase : Dict = getattr(SCREAMING_SNAKE_CASE__ , '''handle_key''' , [] )
for key in handled_keys:
lowercase : List[Any] = value
return new_cls
@staticmethod
def __lowerCamelCase ( cls ):
lowercase : Dict = get_character()
if char != KEYMAP["undefined"]:
lowercase : Optional[int] = ord(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = cls.key_handler.get(SCREAMING_SNAKE_CASE__ )
if handler:
lowercase : Tuple = char
return handler(cls )
else:
return None
def __lowercase ( cls ) ->Any:
"""simple docstring"""
return KeyHandler(cls.__name__, cls.__bases__, cls.__dict__.copy() )
| 337 | 1 |
import math
def __lowercase ( _UpperCamelCase = 100 ) ->int:
"""simple docstring"""
lowercase : Tuple = sum(i * i for i in range(1, n + 1 ) )
lowercase : List[Any] = int(math.pow(sum(range(1, n + 1 ) ), 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 |
import logging
import os
from .state import PartialState
class __SCREAMING_SNAKE_CASE ( logging.LoggerAdapter ):
@staticmethod
def __lowerCamelCase ( SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
lowercase : List[str] = kwargs.pop('''main_process_only''' , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = kwargs.pop('''in_order''' , SCREAMING_SNAKE_CASE__ )
if self.isEnabledFor(SCREAMING_SNAKE_CASE__ ):
if self._should_log(SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : str = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
elif in_order:
lowercase : List[Any] = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
lowercase , lowercase : Union[str, Any] = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
state.wait_for_everyone()
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->List[Any]:
"""simple docstring"""
if log_level is None:
lowercase : str = os.environ.get('''ACCELERATE_LOG_LEVEL''', _UpperCamelCase )
lowercase : str = logging.getLogger(_UpperCamelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_UpperCamelCase, {} )
| 337 | 1 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
random.seed(_UpperCamelCase )
np.random.seed(_UpperCamelCase )
torch.manual_seed(_UpperCamelCase )
torch.cuda.manual_seed_all(_UpperCamelCase )
# ^^ safe to call this function even if cuda is not available
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0.9999 , SCREAMING_SNAKE_CASE__ = 0.0 , SCREAMING_SNAKE_CASE__ = 0 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = 1.0 , SCREAMING_SNAKE_CASE__ = 2 / 3 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
if isinstance(SCREAMING_SNAKE_CASE__ , torch.nn.Module ):
lowercase : List[str] = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage`''' , '''1.0.0''' , SCREAMING_SNAKE_CASE__ , standard_warn=SCREAMING_SNAKE_CASE__ , )
lowercase : str = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
lowercase : Optional[int] = True
if kwargs.get('''max_value''' , SCREAMING_SNAKE_CASE__ ) is not None:
lowercase : Any = '''The `max_value` argument is deprecated. Please use `decay` instead.'''
deprecate('''max_value''' , '''1.0.0''' , SCREAMING_SNAKE_CASE__ , standard_warn=SCREAMING_SNAKE_CASE__ )
lowercase : str = kwargs['''max_value''']
if kwargs.get('''min_value''' , SCREAMING_SNAKE_CASE__ ) is not None:
lowercase : Any = '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
deprecate('''min_value''' , '''1.0.0''' , SCREAMING_SNAKE_CASE__ , standard_warn=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = kwargs['''min_value''']
lowercase : Dict = list(SCREAMING_SNAKE_CASE__ )
lowercase : str = [p.clone().detach() for p in parameters]
if kwargs.get('''device''' , SCREAMING_SNAKE_CASE__ ) is not None:
lowercase : int = '''The `device` argument is deprecated. Please use `to` instead.'''
deprecate('''device''' , '''1.0.0''' , SCREAMING_SNAKE_CASE__ , standard_warn=SCREAMING_SNAKE_CASE__ )
self.to(device=kwargs['''device'''] )
lowercase : List[str] = None
lowercase : List[Any] = decay
lowercase : Union[str, Any] = min_decay
lowercase : Union[str, Any] = update_after_step
lowercase : Dict = use_ema_warmup
lowercase : Optional[Any] = inv_gamma
lowercase : Tuple = power
lowercase : Dict = 0
lowercase : Dict = None # set in `step()`
lowercase : Dict = model_cls
lowercase : Tuple = model_config
@classmethod
def __lowerCamelCase ( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : str = model_cls.load_config(SCREAMING_SNAKE_CASE__ , return_unused_kwargs=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = model_cls.from_pretrained(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = cls(model.parameters() , model_cls=SCREAMING_SNAKE_CASE__ , model_config=model.config )
ema_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
return ema_model
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if self.model_cls is None:
raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''' )
if self.model_config is None:
raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''' )
lowercase : Optional[int] = self.model_cls.from_config(self.model_config )
lowercase : List[Any] = self.state_dict()
state_dict.pop('''shadow_params''' , SCREAMING_SNAKE_CASE__ )
model.register_to_config(**SCREAMING_SNAKE_CASE__ )
self.copy_to(model.parameters() )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
lowercase : Union[str, Any] = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
lowercase : List[str] = (1 + step) / (10 + step)
lowercase : List[Any] = min(SCREAMING_SNAKE_CASE__ , self.decay )
# make sure decay is not smaller than min_decay
lowercase : Tuple = max(SCREAMING_SNAKE_CASE__ , self.min_decay )
return cur_decay_value
@torch.no_grad()
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if isinstance(SCREAMING_SNAKE_CASE__ , torch.nn.Module ):
lowercase : List[str] = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`''' , '''1.0.0''' , SCREAMING_SNAKE_CASE__ , standard_warn=SCREAMING_SNAKE_CASE__ , )
lowercase : str = parameters.parameters()
lowercase : Optional[int] = list(SCREAMING_SNAKE_CASE__ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
lowercase : Any = self.get_decay(self.optimization_step )
lowercase : str = decay
lowercase : List[str] = 1 - decay
lowercase : Union[str, Any] = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , SCREAMING_SNAKE_CASE__ ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
lowercase : str = deepspeed.zero.GatheredParameters(SCREAMING_SNAKE_CASE__ , modifier_rank=SCREAMING_SNAKE_CASE__ )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = list(SCREAMING_SNAKE_CASE__ )
for s_param, param in zip(self.shadow_params , SCREAMING_SNAKE_CASE__ ):
param.data.copy_(s_param.to(param.device ).data )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None ):
lowercase : Union[str, Any] = [
p.to(device=SCREAMING_SNAKE_CASE__ , dtype=SCREAMING_SNAKE_CASE__ ) if p.is_floating_point() else p.to(device=SCREAMING_SNAKE_CASE__ )
for p in self.shadow_params
]
def __lowerCamelCase ( self ):
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = [param.detach().cpu().clone() for param in parameters]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if self.temp_stored_params is None:
raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''' )
for c_param, param in zip(self.temp_stored_params , SCREAMING_SNAKE_CASE__ ):
param.data.copy_(c_param.data )
# Better memory-wise.
lowercase : Optional[Any] = None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : int = copy.deepcopy(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = state_dict.get('''decay''' , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError('''Decay must be between 0 and 1''' )
lowercase : Tuple = state_dict.get('''min_decay''' , self.min_decay )
if not isinstance(self.min_decay , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''Invalid min_decay''' )
lowercase : List[Any] = state_dict.get('''optimization_step''' , self.optimization_step )
if not isinstance(self.optimization_step , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''Invalid optimization_step''' )
lowercase : Optional[Any] = state_dict.get('''update_after_step''' , self.update_after_step )
if not isinstance(self.update_after_step , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''Invalid update_after_step''' )
lowercase : Optional[Any] = state_dict.get('''use_ema_warmup''' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''Invalid use_ema_warmup''' )
lowercase : int = state_dict.get('''inv_gamma''' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('''Invalid inv_gamma''' )
lowercase : Optional[int] = state_dict.get('''power''' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('''Invalid power''' )
lowercase : List[str] = state_dict.get('''shadow_params''' , SCREAMING_SNAKE_CASE__ )
if shadow_params is not None:
lowercase : List[Any] = shadow_params
if not isinstance(self.shadow_params , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''shadow_params must be a list''' )
if not all(isinstance(SCREAMING_SNAKE_CASE__ , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('''shadow_params must all be Tensors''' )
| 337 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super().__init__()
lowercase : Any = model
lowercase : Optional[Any] = 2
lowercase : Optional[int] = nn.Linear(self.model.config.hidden_size , self.num_labels )
def __lowerCamelCase ( self ):
pass
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : str = LongformerModel.from_pretrained(_UpperCamelCase )
lowercase : int = LightningModel(_UpperCamelCase )
lowercase : Union[str, Any] = torch.load(_UpperCamelCase, map_location=torch.device('''cpu''' ) )
lightning_model.load_state_dict(ckpt['''state_dict'''] )
# init longformer question answering model
lowercase : List[Any] = LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_UpperCamelCase )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 337 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
__a = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
__a = logging.WARNING
def __lowercase ( ) ->Union[str, Any]:
"""simple docstring"""
lowercase : Optional[int] = os.getenv('''DATASETS_VERBOSITY''', _UpperCamelCase )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ', '.join(log_levels.keys() ) }""" )
return _default_log_level
def __lowercase ( ) ->str:
"""simple docstring"""
return __name__.split('''.''' )[0]
def __lowercase ( ) ->logging.Logger:
"""simple docstring"""
return logging.getLogger(_get_library_name() )
def __lowercase ( ) ->None:
"""simple docstring"""
lowercase : List[Any] = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def __lowercase ( ) ->None:
"""simple docstring"""
lowercase : List[Any] = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def __lowercase ( _UpperCamelCase = None ) ->logging.Logger:
"""simple docstring"""
if name is None:
lowercase : Optional[int] = _get_library_name()
return logging.getLogger(_UpperCamelCase )
def __lowercase ( ) ->int:
"""simple docstring"""
return _get_library_root_logger().getEffectiveLevel()
def __lowercase ( _UpperCamelCase ) ->None:
"""simple docstring"""
_get_library_root_logger().setLevel(_UpperCamelCase )
def __lowercase ( ) ->Dict:
"""simple docstring"""
return set_verbosity(_UpperCamelCase )
def __lowercase ( ) ->Any:
"""simple docstring"""
return set_verbosity(_UpperCamelCase )
def __lowercase ( ) ->int:
"""simple docstring"""
return set_verbosity(_UpperCamelCase )
def __lowercase ( ) ->Optional[int]:
"""simple docstring"""
return set_verbosity(_UpperCamelCase )
def __lowercase ( ) ->None:
"""simple docstring"""
lowercase : Union[str, Any] = False
def __lowercase ( ) ->None:
"""simple docstring"""
lowercase : Optional[int] = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class __SCREAMING_SNAKE_CASE :
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ): # pylint: disable=unused-argument
lowercase : List[str] = args[0] if args else None
def __iter__( self ):
return iter(self._iterator )
def __getattr__( self , SCREAMING_SNAKE_CASE__ ):
def empty_fn(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ):
return self
def __exit__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return
__a = True
class __SCREAMING_SNAKE_CASE :
def __call__( self , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , **SCREAMING_SNAKE_CASE__ ):
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
else:
return EmptyTqdm(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__a = _tqdm_cls()
def __lowercase ( ) ->bool:
"""simple docstring"""
global _tqdm_active
return bool(_tqdm_active )
def __lowercase ( ) ->Optional[int]:
"""simple docstring"""
global _tqdm_active
lowercase : List[str] = True
def __lowercase ( ) ->Optional[Any]:
"""simple docstring"""
global _tqdm_active
lowercase : Optional[Any] = False
| 337 |
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
__a = logging.get_logger(__name__)
__a = {
'''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Any = 'yolos'
def __init__( self , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=3072 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=[512, 864] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=100 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = hidden_size
lowercase : int = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : str = intermediate_size
lowercase : Dict = hidden_act
lowercase : int = hidden_dropout_prob
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : List[Any] = initializer_range
lowercase : Optional[int] = layer_norm_eps
lowercase : str = image_size
lowercase : Dict = patch_size
lowercase : str = num_channels
lowercase : Optional[int] = qkv_bias
lowercase : List[str] = num_detection_tokens
lowercase : List[str] = use_mid_position_embeddings
lowercase : Dict = auxiliary_loss
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : Any = bbox_cost
lowercase : int = giou_cost
# Loss coefficients
lowercase : Dict = bbox_loss_coefficient
lowercase : Optional[Any] = giou_loss_coefficient
lowercase : Tuple = eos_coefficient
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = version.parse('1.11' )
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
@property
def __lowerCamelCase ( self ):
return 12
| 337 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = '''▁'''
__a = {'''vocab_file''': '''sentencepiece.bpe.model'''}
__a = {
'''vocab_file''': {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large-finetuned-conll02-dutch''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll02-spanish''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-english''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-german''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
),
}
}
__a = {
'''xlm-roberta-base''': 5_12,
'''xlm-roberta-large''': 5_12,
'''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
'''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
'''xlm-roberta-large-finetuned-conll03-english''': 5_12,
'''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Optional[int] = VOCAB_FILES_NAMES
A : str = PRETRAINED_VOCAB_FILES_MAP
A : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Any = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
# Mask token behave like a normal word, i.e. include the space before it
lowercase : Optional[Any] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else mask_token
lowercase : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , )
lowercase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE__ ) )
lowercase : str = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
lowercase : List[str] = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowercase : str = 1
lowercase : Any = len(self.sp_model ) + self.fairseq_offset
lowercase : Dict = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
lowercase : Any = self.__dict__.copy()
lowercase : Any = None
lowercase : List[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , SCREAMING_SNAKE_CASE__ ):
lowercase : int = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase : Optional[Any] = {}
lowercase : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase : int = [self.cls_token_id]
lowercase : Any = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Dict = [self.sep_token_id]
lowercase : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def __lowerCamelCase ( self ):
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def __lowerCamelCase ( self ):
lowercase : List[str] = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase : Tuple = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = ''''''.join(SCREAMING_SNAKE_CASE__ ).replace(SCREAMING_SNAKE_CASE__ , ''' ''' ).strip()
return out_string
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase : Optional[Any] = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE__ , '''wb''' ) as fi:
lowercase : int = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,)
| 337 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__a = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""simple docstring"""
if got_ver is None or want_ver is None:
raise ValueError(
f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
f""" reinstalling {pkg}.""" )
if not ops[op](version.parse(_UpperCamelCase ), version.parse(_UpperCamelCase ) ):
raise ImportError(
f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->None:
"""simple docstring"""
lowercase : List[Any] = f"""\n{hint}""" if hint is not None else ''''''
# non-versioned check
if re.match(R'''^[\w_\-\d]+$''', _UpperCamelCase ):
lowercase , lowercase , lowercase : Optional[Any] = requirement, None, None
else:
lowercase : List[Any] = re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
f""" got {requirement}""" )
lowercase , lowercase : str = match[0]
lowercase : Tuple = want_full.split(''',''' ) # there could be multiple requirements
lowercase : List[Any] = {}
for w in want_range:
lowercase : str = re.findall(R'''^([\s!=<>]{1,2})(.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
f""" but got {requirement}""" )
lowercase , lowercase : Optional[int] = match[0]
lowercase : Dict = want_ver
if op not in ops:
raise ValueError(f"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )
# special case
if pkg == "python":
lowercase : int = '''.'''.join([str(_UpperCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
return
# check if any version is installed
try:
lowercase : List[str] = importlib.metadata.version(_UpperCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
f"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : Optional[int] = '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
return require_version(_UpperCamelCase, _UpperCamelCase )
| 337 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Tuple:
"""simple docstring"""
def get_masked_lm_array(_UpperCamelCase ):
lowercase : Dict = f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
lowercase : List[Any] = tf.train.load_variable(_UpperCamelCase, _UpperCamelCase )
if "kernel" in name:
lowercase : Tuple = array.transpose()
return torch.from_numpy(_UpperCamelCase )
def get_encoder_array(_UpperCamelCase ):
lowercase : Any = f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
lowercase : Optional[Any] = tf.train.load_variable(_UpperCamelCase, _UpperCamelCase )
if "kernel" in name:
lowercase : Dict = array.transpose()
return torch.from_numpy(_UpperCamelCase )
def get_encoder_layer_array(_UpperCamelCase, _UpperCamelCase ):
lowercase : Optional[Any] = f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
lowercase : List[Any] = tf.train.load_variable(_UpperCamelCase, _UpperCamelCase )
if "kernel" in name:
lowercase : List[str] = array.transpose()
return torch.from_numpy(_UpperCamelCase )
def get_encoder_attention_layer_array(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : Any = f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
lowercase : Tuple = tf.train.load_variable(_UpperCamelCase, _UpperCamelCase )
lowercase : List[str] = array.reshape(_UpperCamelCase )
if "kernel" in name:
lowercase : List[Any] = array.transpose()
return torch.from_numpy(_UpperCamelCase )
print(f"""Loading model based on config from {config_path}...""" )
lowercase : Union[str, Any] = BertConfig.from_json_file(_UpperCamelCase )
lowercase : int = BertForMaskedLM(_UpperCamelCase )
# Layers
for layer_index in range(0, config.num_hidden_layers ):
lowercase : BertLayer = model.bert.encoder.layer[layer_index]
# Self-attention
lowercase : BertSelfAttention = layer.attention.self
lowercase : Optional[int] = get_encoder_attention_layer_array(
_UpperCamelCase, '''_query_dense/kernel''', self_attn.query.weight.data.shape )
lowercase : Optional[Any] = get_encoder_attention_layer_array(
_UpperCamelCase, '''_query_dense/bias''', self_attn.query.bias.data.shape )
lowercase : Any = get_encoder_attention_layer_array(
_UpperCamelCase, '''_key_dense/kernel''', self_attn.key.weight.data.shape )
lowercase : Any = get_encoder_attention_layer_array(
_UpperCamelCase, '''_key_dense/bias''', self_attn.key.bias.data.shape )
lowercase : Any = get_encoder_attention_layer_array(
_UpperCamelCase, '''_value_dense/kernel''', self_attn.value.weight.data.shape )
lowercase : Tuple = get_encoder_attention_layer_array(
_UpperCamelCase, '''_value_dense/bias''', self_attn.value.bias.data.shape )
# Self-attention Output
lowercase : BertSelfOutput = layer.attention.output
lowercase : Optional[int] = get_encoder_attention_layer_array(
_UpperCamelCase, '''_output_dense/kernel''', self_output.dense.weight.data.shape )
lowercase : str = get_encoder_attention_layer_array(
_UpperCamelCase, '''_output_dense/bias''', self_output.dense.bias.data.shape )
lowercase : Tuple = get_encoder_layer_array(_UpperCamelCase, '''_attention_layer_norm/gamma''' )
lowercase : str = get_encoder_layer_array(_UpperCamelCase, '''_attention_layer_norm/beta''' )
# Intermediate
lowercase : BertIntermediate = layer.intermediate
lowercase : List[Any] = get_encoder_layer_array(_UpperCamelCase, '''_intermediate_dense/kernel''' )
lowercase : List[str] = get_encoder_layer_array(_UpperCamelCase, '''_intermediate_dense/bias''' )
# Output
lowercase : BertOutput = layer.output
lowercase : Dict = get_encoder_layer_array(_UpperCamelCase, '''_output_dense/kernel''' )
lowercase : Optional[Any] = get_encoder_layer_array(_UpperCamelCase, '''_output_dense/bias''' )
lowercase : Tuple = get_encoder_layer_array(_UpperCamelCase, '''_output_layer_norm/gamma''' )
lowercase : List[str] = get_encoder_layer_array(_UpperCamelCase, '''_output_layer_norm/beta''' )
# Embeddings
lowercase : Optional[int] = get_encoder_array('''_position_embedding_layer/embeddings''' )
lowercase : Union[str, Any] = get_encoder_array('''_type_embedding_layer/embeddings''' )
lowercase : Any = get_encoder_array('''_embedding_norm_layer/gamma''' )
lowercase : Tuple = get_encoder_array('''_embedding_norm_layer/beta''' )
# LM Head
lowercase : Tuple = model.cls.predictions.transform
lowercase : List[str] = get_masked_lm_array('''dense/kernel''' )
lowercase : Any = get_masked_lm_array('''dense/bias''' )
lowercase : Any = get_masked_lm_array('''layer_norm/gamma''' )
lowercase : Union[str, Any] = get_masked_lm_array('''layer_norm/beta''' )
lowercase : List[Any] = get_masked_lm_array('''embedding_table''' )
# Pooling
lowercase : int = BertPooler(config=_UpperCamelCase )
lowercase : BertPooler = get_encoder_array('''_pooler_layer/kernel''' )
lowercase : BertPooler = get_encoder_array('''_pooler_layer/bias''' )
# Export final model
model.save_pretrained(_UpperCamelCase )
# Integration test - should load without any errors ;)
lowercase : Tuple = BertForMaskedLM.from_pretrained(_UpperCamelCase )
print(new_model.eval() )
print('''Model conversion was done sucessfully!''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument(
'''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
type=str,
required=True,
help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''',
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
__a = parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 337 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'deta'
A : Dict = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=900 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=1024 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="relu" , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="sine" , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=300 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.25 , **SCREAMING_SNAKE_CASE__ , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase : Tuple = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = backbone_config.pop('''model_type''' )
lowercase : Any = CONFIG_MAPPING[backbone_model_type]
lowercase : List[Any] = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = backbone_config
lowercase : Union[str, Any] = num_queries
lowercase : Any = max_position_embeddings
lowercase : int = d_model
lowercase : Any = encoder_ffn_dim
lowercase : Optional[int] = encoder_layers
lowercase : Tuple = encoder_attention_heads
lowercase : Optional[Any] = decoder_ffn_dim
lowercase : Optional[int] = decoder_layers
lowercase : int = decoder_attention_heads
lowercase : Any = dropout
lowercase : int = attention_dropout
lowercase : Dict = activation_dropout
lowercase : int = activation_function
lowercase : Dict = init_std
lowercase : List[str] = init_xavier_std
lowercase : Optional[Any] = encoder_layerdrop
lowercase : Tuple = auxiliary_loss
lowercase : Tuple = position_embedding_type
# deformable attributes
lowercase : List[str] = num_feature_levels
lowercase : Tuple = encoder_n_points
lowercase : Optional[int] = decoder_n_points
lowercase : Tuple = two_stage
lowercase : Optional[Any] = two_stage_num_proposals
lowercase : Union[str, Any] = with_box_refine
lowercase : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : str = bbox_cost
lowercase : List[Any] = giou_cost
# Loss coefficients
lowercase : Tuple = mask_loss_coefficient
lowercase : Any = dice_loss_coefficient
lowercase : Dict = bbox_loss_coefficient
lowercase : Tuple = giou_loss_coefficient
lowercase : Union[str, Any] = eos_coefficient
lowercase : Tuple = focal_alpha
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __lowerCamelCase ( self ):
return self.encoder_attention_heads
@property
def __lowerCamelCase ( self ):
return self.d_model
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase : Any = self.backbone_config.to_dict()
lowercase : List[str] = self.__class__.model_type
return output
| 337 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Dict = (DDIMParallelScheduler,)
A : Union[str, Any] = (('eta', 0.0), ('num_inference_steps', 50))
def __lowerCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
lowercase : Any = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''clip_sample''': True,
}
config.update(**SCREAMING_SNAKE_CASE__ )
return config
def __lowerCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = self.scheduler_classes[0]
lowercase : Optional[Any] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = scheduler_class(**SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : Optional[int] = 10, 0.0
lowercase : Dict = self.dummy_model()
lowercase : int = self.dummy_sample_deter
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
for t in scheduler.timesteps:
lowercase : str = model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).prev_sample
return sample
def __lowerCamelCase ( self ):
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = self.scheduler_classes[0]
lowercase : Dict = self.get_scheduler_config(steps_offset=1 )
lowercase : Dict = scheduler_class(**SCREAMING_SNAKE_CASE__ )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def __lowerCamelCase ( self ):
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__ , beta_end=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=SCREAMING_SNAKE_CASE__ , prediction_type=SCREAMING_SNAKE_CASE__ , sample_max_value=SCREAMING_SNAKE_CASE__ , )
def __lowerCamelCase ( self ):
for t in [1, 10, 49]:
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__ , num_inference_steps=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__ , eta=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = self.scheduler_classes[0]
lowercase : str = self.get_scheduler_config()
lowercase : Optional[Any] = scheduler_class(**SCREAMING_SNAKE_CASE__ )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.14771 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.32460 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.00979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.02 ) ) < 1E-5
def __lowerCamelCase ( self ):
lowercase : Any = self.scheduler_classes[0]
lowercase : Union[str, Any] = self.get_scheduler_config()
lowercase : str = scheduler_class(**SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : Dict = 10, 0.0
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = self.dummy_model()
lowercase : Union[str, Any] = self.dummy_sample_deter
lowercase : Tuple = self.dummy_sample_deter + 0.1
lowercase : Tuple = self.dummy_sample_deter - 0.1
lowercase : Dict = samplea.shape[0]
lowercase : List[str] = torch.stack([samplea, samplea, samplea] , dim=0 )
lowercase : str = torch.arange(SCREAMING_SNAKE_CASE__ )[0:3, None].repeat(1 , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
lowercase : Any = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 1147.7904 ) < 1E-2
assert abs(result_mean.item() - 0.4982 ) < 1E-3
def __lowerCamelCase ( self ):
lowercase : int = self.full_loop()
lowercase : int = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowercase : str = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 172.0067 ) < 1E-2
assert abs(result_mean.item() - 0.223967 ) < 1E-3
def __lowerCamelCase ( self ):
lowercase : List[str] = self.full_loop(prediction_type='''v_prediction''' )
lowercase : int = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowercase : str = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 52.5302 ) < 1E-2
assert abs(result_mean.item() - 0.0684 ) < 1E-3
def __lowerCamelCase ( self ):
# We specify different beta, so that the first alpha is 0.99
lowercase : Tuple = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__ , beta_start=0.01 )
lowercase : List[Any] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowercase : int = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 149.8295 ) < 1E-2
assert abs(result_mean.item() - 0.1951 ) < 1E-3
def __lowerCamelCase ( self ):
# We specify different beta, so that the first alpha is 0.99
lowercase : Optional[Any] = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__ , beta_start=0.01 )
lowercase : Tuple = torch.sum(torch.abs(SCREAMING_SNAKE_CASE__ ) )
lowercase : List[Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_sum.item() - 149.0784 ) < 1E-2
assert abs(result_mean.item() - 0.1941 ) < 1E-3
| 337 |
def __lowercase ( ) ->List[Any]:
"""simple docstring"""
lowercase : Union[str, Any] = 0
for i in range(1, 1001 ):
total += i**i
return str(_UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 337 | 1 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(_UpperCamelCase ) / len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__a = 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_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__a = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self ):
lowercase : str = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
lowercase : Any = self.diffusers_dir
shutil.copy(
os.path.join(SCREAMING_SNAKE_CASE__ , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def __lowerCamelCase ( self ):
lowercase : List[Any] = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
lowercase : Tuple = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
lowercase : str = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
lowercase : Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowercase : List[Any] = black.format_str(SCREAMING_SNAKE_CASE__ , mode=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , newline='''\n''' ) as f:
f.write(SCREAMING_SNAKE_CASE__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , '''r''' ) as f:
self.assertTrue(f.read() , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Tuple = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with a really long name
lowercase : List[Any] = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , overwrite_result=re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
| 337 | 1 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : int = multiprocessing.Manager()
lowercase : int = manager.list()
lowercase : List[str] = multiprocessing.Process(target=_UpperCamelCase, args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append('''timed out''' )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Dict:
"""simple docstring"""
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
lowercase : Tuple = shutil.rmtree
lowercase : Tuple = os.rmdir
lowercase : List[Any] = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
lowercase : Union[str, Any] = {}
with swallow_io():
with time_limit(_UpperCamelCase ):
exec(_UpperCamelCase, _UpperCamelCase )
result.append('''passed''' )
except TimeoutException:
result.append('''timed out''' )
except BaseException as e:
result.append(f"""failed: {e}""" )
# Needed for cleaning up.
lowercase : Tuple = rmtree
lowercase : Union[str, Any] = rmdir
lowercase : Optional[Any] = chdir
@contextlib.contextmanager
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
def signal_handler(_UpperCamelCase, _UpperCamelCase ):
raise TimeoutException('''Timed out!''' )
signal.setitimer(signal.ITIMER_REAL, _UpperCamelCase )
signal.signal(signal.SIGALRM, _UpperCamelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL, 0 )
@contextlib.contextmanager
def __lowercase ( ) ->Dict:
"""simple docstring"""
lowercase : List[str] = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCamelCase ):
with contextlib.redirect_stderr(_UpperCamelCase ):
with redirect_stdin(_UpperCamelCase ):
yield
@contextlib.contextmanager
def __lowercase ( ) ->int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCamelCase ):
yield dirname
class __SCREAMING_SNAKE_CASE ( A__ ):
pass
class __SCREAMING_SNAKE_CASE ( io.StringIO ):
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
raise OSError
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
raise OSError
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
raise OSError
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
return False
class __SCREAMING_SNAKE_CASE ( contextlib._RedirectStream ): # type: ignore
A : List[Any] = 'stdin'
@contextlib.contextmanager
def __lowercase ( _UpperCamelCase ) ->Dict:
"""simple docstring"""
if root == ".":
yield
return
lowercase : Dict = os.getcwd()
os.chdir(_UpperCamelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCamelCase )
def __lowercase ( _UpperCamelCase=None ) ->int:
"""simple docstring"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
lowercase : str = None
lowercase : List[str] = None
import os
lowercase : str = '''1'''
lowercase : Optional[Any] = None
lowercase : Optional[int] = None
lowercase : int = None
lowercase : Any = None
lowercase : Dict = None
lowercase : Tuple = None
lowercase : List[Any] = None
lowercase : Optional[int] = None
lowercase : Any = None
lowercase : Optional[int] = None
lowercase : Tuple = None
lowercase : Dict = None
lowercase : Any = None
lowercase : Optional[int] = None
lowercase : List[Any] = None
lowercase : str = None
lowercase : Optional[Any] = None
lowercase : List[str] = None
lowercase : List[str] = None
lowercase : Dict = None
lowercase : Optional[int] = None
lowercase : Any = None
lowercase : str = None
lowercase : Optional[int] = None
lowercase : Union[str, Any] = None
lowercase : Tuple = None
lowercase : Union[str, Any] = None
import shutil
lowercase : Optional[Any] = None
lowercase : Dict = None
lowercase : Optional[int] = None
import subprocess
lowercase : str = None # type: ignore
lowercase : int = None
import sys
lowercase : int = None
lowercase : str = None
lowercase : Optional[int] = None
lowercase : Optional[Any] = None
lowercase : Optional[int] = None
| 337 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=0 ): # a graph with Node 0,1,...,N-1
lowercase : List[Any] = n
lowercase : List[Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # adjacency matrix for weight
lowercase : Union[str, Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # dp[i][j] stores minimum distance from i to j
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : int = w
def __lowerCamelCase ( self ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase : Any = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.dp[u][v]
if __name__ == "__main__":
__a = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 337 | 1 |
from string import ascii_uppercase
__a = {str(ord(c) - 55): c for c in ascii_uppercase}
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
if isinstance(_UpperCamelCase, _UpperCamelCase ):
raise TypeError('''int() can\'t convert non-string with explicit base''' )
if num < 0:
raise ValueError('''parameter must be positive int''' )
if isinstance(_UpperCamelCase, _UpperCamelCase ):
raise TypeError('''\'str\' object cannot be interpreted as an integer''' )
if isinstance(_UpperCamelCase, _UpperCamelCase ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if base in (0, 1):
raise ValueError('''base must be >= 2''' )
if base > 36:
raise ValueError('''base must be <= 36''' )
lowercase : Dict = ''''''
lowercase : Optional[int] = 0
lowercase : Tuple = 0
while div != 1:
lowercase , lowercase : int = divmod(_UpperCamelCase, _UpperCamelCase )
if base >= 11 and 9 < mod < 36:
lowercase : List[Any] = ALPHABET_VALUES[str(_UpperCamelCase )]
else:
lowercase : Dict = str(_UpperCamelCase )
new_value += actual_value
lowercase : List[Any] = num // base
lowercase : Optional[int] = div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(_UpperCamelCase )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(10_00):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 337 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(_UpperCamelCase ) / len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
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, logging
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = 1 / 255 , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Dict = size if size is not None else {'''shortest_edge''': 256}
lowercase : Dict = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
lowercase : Any = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase : Optional[int] = get_size_dict(SCREAMING_SNAKE_CASE__ )
lowercase : int = do_resize
lowercase : int = size
lowercase : Tuple = resample
lowercase : Optional[int] = do_center_crop
lowercase : Optional[int] = crop_size
lowercase : Union[str, Any] = do_rescale
lowercase : str = rescale_factor
lowercase : Dict = do_normalize
lowercase : Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
lowercase : Any = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
lowercase : Optional[Any] = get_resize_output_image_size(SCREAMING_SNAKE_CASE__ , size=size['''shortest_edge'''] , default_to_square=SCREAMING_SNAKE_CASE__ )
return resize(SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
lowercase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE__ )
return center_crop(SCREAMING_SNAKE_CASE__ , size=(size['''height'''], size['''width''']) , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ ):
return rescale(SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
return normalize(SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE__ , ):
lowercase : str = do_resize if do_resize is not None else self.do_resize
lowercase : List[str] = size if size is not None else self.size
lowercase : Dict = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = resample if resample is not None else self.resample
lowercase : str = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase : Optional[int] = crop_size if crop_size is not None else self.crop_size
lowercase : str = get_size_dict(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = do_rescale if do_rescale is not None else self.do_rescale
lowercase : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
lowercase : Tuple = image_mean if image_mean is not None else self.image_mean
lowercase : Tuple = image_std if image_std is not None else self.image_std
lowercase : Dict = make_list_of_images(SCREAMING_SNAKE_CASE__ )
if not valid_images(SCREAMING_SNAKE_CASE__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
lowercase : List[str] = [to_numpy_array(SCREAMING_SNAKE_CASE__ ) for image in images]
if do_resize:
lowercase : List[str] = [self.resize(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_center_crop:
lowercase : List[str] = [self.center_crop(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_rescale:
lowercase : int = [self.rescale(image=SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_normalize:
lowercase : str = [self.normalize(image=SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ ) for image in images]
lowercase : Tuple = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for image in images]
lowercase : str = {'''pixel_values''': images}
return BatchFeature(data=SCREAMING_SNAKE_CASE__ , tensor_type=SCREAMING_SNAKE_CASE__ )
| 337 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 337 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__a = {
'''cola''': 2,
'''mnli''': 3,
'''mrpc''': 2,
'''sst-2''': 2,
'''sts-b''': 1,
'''qqp''': 2,
'''qnli''': 2,
'''rte''': 2,
'''wnli''': 2,
}
logging.set_verbosity_info()
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=None ) ->Union[str, Any]:
"""simple docstring"""
lowercase : Optional[Any] = XLNetConfig.from_json_file(_UpperCamelCase )
lowercase : Union[str, Any] = finetuning_task.lower() if finetuning_task is not None else ''''''
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(f"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}""" )
lowercase : Any = finetuning_task
lowercase : Tuple = GLUE_TASKS_NUM_LABELS[finetuning_task]
lowercase : str = XLNetForSequenceClassification(_UpperCamelCase )
elif "squad" in finetuning_task:
lowercase : Tuple = finetuning_task
lowercase : Union[str, Any] = XLNetForQuestionAnswering(_UpperCamelCase )
else:
lowercase : Optional[int] = XLNetLMHeadModel(_UpperCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
# Save pytorch-model
lowercase : Dict = os.path.join(_UpperCamelCase, _UpperCamelCase )
lowercase : Dict = os.path.join(_UpperCamelCase, _UpperCamelCase )
print(f"""Save PyTorch model to {os.path.abspath(_UpperCamelCase )}""" )
torch.save(model.state_dict(), _UpperCamelCase )
print(f"""Save configuration file to {os.path.abspath(_UpperCamelCase )}""" )
with open(_UpperCamelCase, '''w''', encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--xlnet_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained XLNet model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--finetuning_task''',
default=None,
type=str,
help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
)
__a = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 337 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase ) ->List[int]:
"""simple docstring"""
if isinstance(_UpperCamelCase, np.ndarray ):
return list(tensor.shape )
lowercase : Optional[Any] = tf.shape(_UpperCamelCase )
if tensor.shape == tf.TensorShape(_UpperCamelCase ):
return dynamic
lowercase : Tuple = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_UpperCamelCase )]
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None, _UpperCamelCase = None ) ->tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9, axis=_UpperCamelCase, name=_UpperCamelCase )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=1e-5, _UpperCamelCase=-1 ) ->int:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_UpperCamelCase, _UpperCamelCase ):
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
lowercase , lowercase : Union[str, Any] = tf.nn.moments(_UpperCamelCase, axes=[axis], keepdims=_UpperCamelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase : int = [1] * inputs.shape.rank
lowercase : Union[str, Any] = shape_list(_UpperCamelCase )[axis]
lowercase : List[str] = tf.reshape(_UpperCamelCase, _UpperCamelCase )
lowercase : Dict = tf.reshape(_UpperCamelCase, _UpperCamelCase )
# Compute layer normalization using the batch_normalization
# function.
lowercase : List[str] = tf.nn.batch_normalization(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, offset=_UpperCamelCase, scale=_UpperCamelCase, variance_epsilon=_UpperCamelCase, )
return outputs
def __lowercase ( _UpperCamelCase, _UpperCamelCase=0, _UpperCamelCase=-1 ) ->List[Any]:
"""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
lowercase : Dict = tf.shape(_UpperCamelCase )
lowercase : Optional[Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase : List[str] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]], axis=0 )
return tf.reshape(_UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->tf.Tensor:
"""simple docstring"""
if not isinstance(_UpperCamelCase, tf.Tensor ):
lowercase : Optional[Any] = tf.convert_to_tensor(_UpperCamelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase : Tuple = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase : str = (
tf.cast(1, encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = "input_ids" ) ->None:
"""simple docstring"""
tf.debugging.assert_less(
_UpperCamelCase, tf.cast(_UpperCamelCase, dtype=tensor.dtype ), message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(_UpperCamelCase )}) must be smaller than the embedding """
f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
), )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : List[Any] = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase : Optional[int] = [x for x in data if len(_UpperCamelCase ) > 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}""" )
lowercase : Any = np.asarray(_UpperCamelCase )
lowercase : List[Any] = 1
lowercase : Tuple = np.array_split(_UpperCamelCase, _UpperCamelCase )
# 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
lowercase : Dict = np.array_split(_UpperCamelCase, _UpperCamelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_UpperCamelCase ):
lowercase : Optional[int] = chunk_data
else:
lowercase : int = data
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
if name in group.attrs:
lowercase : str = [n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs[name]]
else:
lowercase : Optional[Any] = []
lowercase : List[str] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
def _expand_single_ad_tensor(_UpperCamelCase ):
if isinstance(_UpperCamelCase, tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_UpperCamelCase, axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor, _UpperCamelCase )
| 337 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DeformableDetrImageProcessor
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=30 , SCREAMING_SNAKE_CASE__=400 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=1 / 255 , SCREAMING_SNAKE_CASE__=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase : str = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
lowercase : List[Any] = parent
lowercase : Optional[Any] = batch_size
lowercase : List[str] = num_channels
lowercase : Any = min_resolution
lowercase : Any = max_resolution
lowercase : Tuple = do_resize
lowercase : List[str] = size
lowercase : int = do_normalize
lowercase : Union[str, Any] = image_mean
lowercase : str = image_std
lowercase : str = do_rescale
lowercase : str = rescale_factor
lowercase : Union[str, Any] = do_pad
def __lowerCamelCase ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
if not batched:
lowercase : Union[str, Any] = image_inputs[0]
if isinstance(SCREAMING_SNAKE_CASE__ , Image.Image ):
lowercase , lowercase : Union[str, Any] = image.size
else:
lowercase , lowercase : List[Any] = image.shape[1], image.shape[2]
if w < h:
lowercase : Tuple = int(self.size['''shortest_edge'''] * h / w )
lowercase : Union[str, Any] = self.size['''shortest_edge''']
elif w > h:
lowercase : Tuple = self.size['''shortest_edge''']
lowercase : int = int(self.size['''shortest_edge'''] * w / h )
else:
lowercase : List[Any] = self.size['''shortest_edge''']
lowercase : int = self.size['''shortest_edge''']
else:
lowercase : Optional[Any] = []
for image in image_inputs:
lowercase , lowercase : str = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase : Tuple = max(SCREAMING_SNAKE_CASE__ , key=lambda SCREAMING_SNAKE_CASE__ : item[0] )[0]
lowercase : Union[str, Any] = max(SCREAMING_SNAKE_CASE__ , key=lambda SCREAMING_SNAKE_CASE__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
A : Optional[int] = DeformableDetrImageProcessor if is_vision_available() else None
def __lowerCamelCase ( self ):
lowercase : Dict = DeformableDetrImageProcessingTester(self )
@property
def __lowerCamelCase ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCamelCase ( self ):
lowercase : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''image_mean''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''image_std''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_resize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_rescale''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''do_pad''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , '''size''' ) )
def __lowerCamelCase ( self ):
lowercase : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , SCREAMING_SNAKE_CASE__ )
lowercase : Any = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__ )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
# Initialize image_processing
lowercase : int = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE__ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , Image.Image )
# Test not batched input
lowercase : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : Optional[int] = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase , lowercase : List[Any] = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ )
lowercase : 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,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self ):
# Initialize image_processing
lowercase : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase : int = prepare_image_inputs(self.image_processor_tester , 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
lowercase : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : Union[str, Any] = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase : Tuple = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : str = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self ):
# Initialize image_processing
lowercase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase : List[str] = prepare_image_inputs(self.image_processor_tester , 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
lowercase : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : Any = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase : Optional[int] = image_processing(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' ).pixel_values
lowercase , lowercase : Union[str, Any] = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __lowerCamelCase ( self ):
# prepare image and target
lowercase : Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase : str = json.loads(f.read() )
lowercase : str = {'''image_id''': 39769, '''annotations''': target}
# encode them
lowercase : Optional[int] = DeformableDetrImageProcessor()
lowercase : int = image_processing(images=SCREAMING_SNAKE_CASE__ , annotations=SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' )
# verify pixel values
lowercase : int = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , SCREAMING_SNAKE_CASE__ )
lowercase : int = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) )
# verify area
lowercase : Union[str, Any] = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , SCREAMING_SNAKE_CASE__ ) )
# verify boxes
lowercase : str = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , SCREAMING_SNAKE_CASE__ , atol=1E-3 ) )
# verify image_id
lowercase : Optional[Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , SCREAMING_SNAKE_CASE__ ) )
# verify is_crowd
lowercase : Any = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , SCREAMING_SNAKE_CASE__ ) )
# verify class_labels
lowercase : Dict = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , SCREAMING_SNAKE_CASE__ ) )
# verify orig_size
lowercase : Tuple = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , SCREAMING_SNAKE_CASE__ ) )
# verify size
lowercase : int = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , SCREAMING_SNAKE_CASE__ ) )
@slow
def __lowerCamelCase ( self ):
# prepare image, target and masks_path
lowercase : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase : int = json.loads(f.read() )
lowercase : Tuple = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
lowercase : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase : Optional[int] = DeformableDetrImageProcessor(format='''coco_panoptic''' )
lowercase : Optional[Any] = image_processing(images=SCREAMING_SNAKE_CASE__ , annotations=SCREAMING_SNAKE_CASE__ , masks_path=SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' )
# verify pixel values
lowercase : Union[str, Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , SCREAMING_SNAKE_CASE__ )
lowercase : str = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) )
# verify area
lowercase : int = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , SCREAMING_SNAKE_CASE__ ) )
# verify boxes
lowercase : Union[str, Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , SCREAMING_SNAKE_CASE__ )
lowercase : Dict = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , SCREAMING_SNAKE_CASE__ , atol=1E-3 ) )
# verify image_id
lowercase : int = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , SCREAMING_SNAKE_CASE__ ) )
# verify is_crowd
lowercase : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , SCREAMING_SNAKE_CASE__ ) )
# verify class_labels
lowercase : int = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , SCREAMING_SNAKE_CASE__ ) )
# verify masks
lowercase : Dict = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , SCREAMING_SNAKE_CASE__ )
# verify orig_size
lowercase : Optional[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , SCREAMING_SNAKE_CASE__ ) )
# verify size
lowercase : str = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , SCREAMING_SNAKE_CASE__ ) )
| 337 |
def __lowercase ( _UpperCamelCase = 4000000 ) ->int:
"""simple docstring"""
lowercase : int = []
lowercase , lowercase : str = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCamelCase )
lowercase , lowercase : Dict = b, a + b
return sum(_UpperCamelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 | 1 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger()
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = True ) ->List[Any]:
"""simple docstring"""
print(f"""Converting {name}...""" )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
lowercase : Union[str, Any] = timm.create_model('''levit_128s''', pretrained=_UpperCamelCase )
else:
lowercase : Optional[int] = timm.create_model('''levit_128''', pretrained=_UpperCamelCase )
if hidden_sizes == 192:
lowercase : Tuple = timm.create_model('''levit_192''', pretrained=_UpperCamelCase )
if hidden_sizes == 256:
lowercase : str = timm.create_model('''levit_256''', pretrained=_UpperCamelCase )
if hidden_sizes == 384:
lowercase : Optional[Any] = timm.create_model('''levit_384''', pretrained=_UpperCamelCase )
from_model.eval()
lowercase : Optional[Any] = LevitForImageClassificationWithTeacher(_UpperCamelCase ).eval()
lowercase : Dict = OrderedDict()
lowercase : List[Any] = from_model.state_dict()
lowercase : List[str] = list(from_model.state_dict().keys() )
lowercase : List[str] = list(our_model.state_dict().keys() )
print(len(_UpperCamelCase ), len(_UpperCamelCase ) )
for i in range(len(_UpperCamelCase ) ):
lowercase : Tuple = weights[og_keys[i]]
our_model.load_state_dict(_UpperCamelCase )
lowercase : Tuple = torch.randn((2, 3, 224, 224) )
lowercase : Union[str, Any] = from_model(_UpperCamelCase )
lowercase : str = our_model(_UpperCamelCase ).logits
assert torch.allclose(_UpperCamelCase, _UpperCamelCase ), "The model logits don't match the original one."
lowercase : str = name
print(_UpperCamelCase )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
lowercase : Any = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f"""Pushed {checkpoint_name}""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None, _UpperCamelCase = True ) ->Any:
"""simple docstring"""
lowercase : Dict = '''imagenet-1k-id2label.json'''
lowercase : Any = 1000
lowercase : Tuple = (1, num_labels)
lowercase : Dict = '''huggingface/label-files'''
lowercase : int = num_labels
lowercase : Any = json.load(open(hf_hub_download(_UpperCamelCase, _UpperCamelCase, repo_type='''dataset''' ), '''r''' ) )
lowercase : int = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
lowercase : List[str] = idalabel
lowercase : str = {v: k for k, v in idalabel.items()}
lowercase : Union[str, Any] = partial(_UpperCamelCase, num_labels=_UpperCamelCase, idalabel=_UpperCamelCase, labelaid=_UpperCamelCase )
lowercase : str = {
'''levit-128S''': 128,
'''levit-128''': 128,
'''levit-192''': 192,
'''levit-256''': 256,
'''levit-384''': 384,
}
lowercase : List[Any] = {
'''levit-128S''': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384], num_attention_heads=[4, 6, 8], depths=[2, 3, 4], key_dim=[16, 16, 16], drop_path_rate=0, ),
'''levit-128''': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384], num_attention_heads=[4, 8, 12], depths=[4, 4, 4], key_dim=[16, 16, 16], drop_path_rate=0, ),
'''levit-192''': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384], num_attention_heads=[3, 5, 6], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0, ),
'''levit-256''': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512], num_attention_heads=[4, 6, 8], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0, ),
'''levit-384''': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768], num_attention_heads=[6, 9, 12], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0.1, ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name], _UpperCamelCase, names_to_config[model_name], _UpperCamelCase, _UpperCamelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name], _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
return config, expected_shape
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''levit-dump-folder/''',
type=Path,
required=False,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
__a = parser.parse_args()
__a = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 337 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
__a = logging.get_logger(__name__)
__a = {
'''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'perceiver'
def __init__( self , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=1280 , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=26 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="kv" , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=262 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=56 , SCREAMING_SNAKE_CASE__=[368, 496] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=1920 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=[1, 16, 224, 224] , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Any = num_latents
lowercase : Union[str, Any] = d_latents
lowercase : str = d_model
lowercase : int = num_blocks
lowercase : str = num_self_attends_per_block
lowercase : List[str] = num_self_attention_heads
lowercase : List[str] = num_cross_attention_heads
lowercase : int = qk_channels
lowercase : List[Any] = v_channels
lowercase : int = cross_attention_shape_for_attention
lowercase : Tuple = self_attention_widening_factor
lowercase : Dict = cross_attention_widening_factor
lowercase : Any = hidden_act
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : Union[str, Any] = initializer_range
lowercase : Any = layer_norm_eps
lowercase : Any = use_query_residual
# masked language modeling attributes
lowercase : List[str] = vocab_size
lowercase : Dict = max_position_embeddings
# image classification attributes
lowercase : int = image_size
# flow attributes
lowercase : List[Any] = train_size
# multimodal autoencoding attributes
lowercase : List[Any] = num_frames
lowercase : Union[str, Any] = audio_samples_per_frame
lowercase : int = samples_per_patch
lowercase : Optional[int] = output_shape
class __SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCamelCase ( self ):
if self.task == "multiple-choice":
lowercase : Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase : Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''inputs''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 3 , SCREAMING_SNAKE_CASE__ = 40 , SCREAMING_SNAKE_CASE__ = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase : str = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase : Union[str, Any] = preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=SCREAMING_SNAKE_CASE__ )
# Generate dummy inputs according to compute batch and sequence
lowercase : Optional[Any] = [''' '''.join(['''a'''] ) * seq_length] * batch_size
lowercase : Any = dict(preprocessor(SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = inputs.pop('''input_ids''' )
return inputs
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase : List[str] = compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_batch )
lowercase : List[str] = self._generate_dummy_images(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = dict(preprocessor(images=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = inputs.pop('''pixel_values''' )
return inputs
else:
raise ValueError(
'''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''' )
| 337 | 1 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__a = logging.get_logger(__name__) # pylint: disable=invalid-name
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=768 ):
super().__init__(SCREAMING_SNAKE_CASE__ )
lowercase : int = proj_size
lowercase : Dict = CLIPVisionModel(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = PaintByExampleMapper(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = nn.LayerNorm(config.hidden_size )
lowercase : Union[str, Any] = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
lowercase : Tuple = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
lowercase : Any = self.model(pixel_values=SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = clip_output.pooler_output
lowercase : List[Any] = self.mapper(latent_states[:, None] )
lowercase : Any = self.final_layer_norm(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = self.proj_out(SCREAMING_SNAKE_CASE__ )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class __SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super().__init__()
lowercase : Dict = (config.num_hidden_layers + 1) // 5
lowercase : Dict = config.hidden_size
lowercase : int = 1
lowercase : Optional[Any] = nn.ModuleList(
[
BasicTransformerBlock(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , activation_fn='''gelu''' , attention_bias=SCREAMING_SNAKE_CASE__ )
for _ in range(SCREAMING_SNAKE_CASE__ )
] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
for block in self.blocks:
lowercase : Union[str, Any] = block(SCREAMING_SNAKE_CASE__ )
return hidden_states
| 337 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowercase ( _UpperCamelCase = 8 ) ->str:
"""simple docstring"""
lowercase : List[str] = ascii_letters + digits + punctuation
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
i -= len(_UpperCamelCase )
lowercase : Dict = i // 3
lowercase : List[str] = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowercase : Union[str, Any] = (
chars_incl
+ random(_UpperCamelCase, quotient + remainder )
+ random(_UpperCamelCase, _UpperCamelCase )
+ random(_UpperCamelCase, _UpperCamelCase )
)
lowercase : Union[str, Any] = list(_UpperCamelCase )
shuffle(_UpperCamelCase )
return "".join(_UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Dict:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[Any]:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase = 8 ) ->bool:
"""simple docstring"""
if len(_UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowercase : str = any(char in ascii_uppercase for char in password )
lowercase : List[str] = any(char in ascii_lowercase for char in password )
lowercase : Dict = any(char in digits for char in password )
lowercase : Tuple = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def __lowercase ( ) ->Dict:
"""simple docstring"""
lowercase : Union[str, Any] = int(input('''Please indicate the max length of your password: ''' ).strip() )
lowercase : Optional[Any] = input(
'''Please indicate the characters that must be in your password: ''' ).strip()
print('''Password generated:''', password_generator(_UpperCamelCase ) )
print(
'''Alternative Password generated:''', alternative_password_generator(_UpperCamelCase, _UpperCamelCase ), )
print('''[If you are thinking of using this passsword, You better save it.]''' )
if __name__ == "__main__":
main()
| 337 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 |
from __future__ import annotations
__a = []
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_UpperCamelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, -1, -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, len(_UpperCamelCase ) ) ):
if board[i][j] == 1:
return False
return True
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
if row >= len(_UpperCamelCase ):
solution.append(_UpperCamelCase )
printboard(_UpperCamelCase )
print()
return True
for i in range(len(_UpperCamelCase ) ):
if is_safe(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : int = 1
solve(_UpperCamelCase, row + 1 )
lowercase : Tuple = 0
return False
def __lowercase ( _UpperCamelCase ) ->None:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
for j in range(len(_UpperCamelCase ) ):
if board[i][j] == 1:
print('''Q''', end=''' ''' )
else:
print('''.''', end=''' ''' )
print()
# n=int(input("The no. of queens"))
__a = 8
__a = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 337 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
A : Tuple = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = hf_hub_download(
repo_id='''nateraw/video-demo''' , filename='''archery.mp4''' , repo_type='''dataset''' )
lowercase : Any = VideoClassificationPipeline(model=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ , top_k=2 )
lowercase : List[Any] = [
example_video_filepath,
'''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
]
return video_classifier, examples
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
for example in examples:
lowercase : List[Any] = video_classifier(SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{'''score''': ANY(SCREAMING_SNAKE_CASE__ ), '''label''': ANY(SCREAMING_SNAKE_CASE__ )},
{'''score''': ANY(SCREAMING_SNAKE_CASE__ ), '''label''': ANY(SCREAMING_SNAKE_CASE__ )},
] , )
@require_torch
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
lowercase : Tuple = VideoMAEFeatureExtractor(
size={'''shortest_edge''': 10} , crop_size={'''height''': 10, '''width''': 10} )
lowercase : List[str] = pipeline(
'''video-classification''' , model=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , frame_sampling_rate=4 )
lowercase : Optional[int] = hf_hub_download(repo_id='''nateraw/video-demo''' , filename='''archery.mp4''' , repo_type='''dataset''' )
lowercase : Optional[int] = video_classifier(SCREAMING_SNAKE_CASE__ , top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ , decimals=4 ) , [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}] , )
lowercase : str = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ , decimals=4 ) , [
[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
] , )
@require_tf
def __lowerCamelCase ( self ):
pass
| 337 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CTRLForSequenceClassification''',
'''CTRLLMHeadModel''',
'''CTRLModel''',
'''CTRLPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCTRLForSequenceClassification''',
'''TFCTRLLMHeadModel''',
'''TFCTRLModel''',
'''TFCTRLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = 3
lowercase : Tuple = 250
lowercase : List[str] = ids_tensor((batch_size, length) , SCREAMING_SNAKE_CASE__ )
lowercase : Any = torch.ones((batch_size, length) , device=SCREAMING_SNAKE_CASE__ , dtype=torch.float ) / length
return input_ids, scores
def __lowerCamelCase ( self ):
lowercase , lowercase : Optional[Any] = self._get_tensors(5 )
lowercase : int = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase , lowercase : Dict = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase , lowercase : Tuple = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
def __lowerCamelCase ( self ):
lowercase : Tuple = MaxLengthCriteria(max_length=10 )
lowercase , lowercase : Tuple = self._get_tensors(5 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase , lowercase : Dict = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase , lowercase : List[str] = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
lowercase , lowercase : int = self._get_tensors(5 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase , lowercase : int = self._get_tensors(9 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase , lowercase : Optional[Any] = self._get_tensors(10 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase : Tuple = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 10 )
def __lowerCamelCase ( self ):
lowercase , lowercase : List[str] = self._get_tensors(5 )
lowercase : str = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase : Tuple = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
def __lowerCamelCase ( self ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 10 )
with self.assertWarns(SCREAMING_SNAKE_CASE__ ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 11 )
lowercase : Any = validate_stopping_criteria(StoppingCriteriaList() , 11 )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 1 )
| 337 |
from collections.abc import Callable
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
# Stores actual heap items.
lowercase : list = []
# Stores indexes of each item for supporting updates and deletion.
lowercase : dict = {}
# Stores current size of heap.
lowercase : str = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowercase : Tuple = key or (lambda SCREAMING_SNAKE_CASE__ : x)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return int((i - 1) / 2 ) if i > 0 else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : Dict = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowercase , lowercase : int = self.arr[j], self.arr[i]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.arr[i][1] < self.arr[j][1]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : int = self._left(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = self._right(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = i
if left is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = left
if right is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = right
return valid_parent
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = self._parent(SCREAMING_SNAKE_CASE__ )
while parent is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : Optional[int] = parent, self._parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
while valid_parent != index:
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : str = valid_parent, self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : str = self.pos_map[item]
lowercase : Optional[int] = [item, self.key(SCREAMING_SNAKE_CASE__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : List[str] = self.pos_map[item]
del self.pos_map[item]
lowercase : Optional[int] = self.arr[self.size - 1]
lowercase : int = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(SCREAMING_SNAKE_CASE__ )] )
else:
lowercase : int = [item, self.key(SCREAMING_SNAKE_CASE__ )]
lowercase : str = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowerCamelCase ( self ):
return self.arr[0] if self.size else None
def __lowerCamelCase ( self ):
lowercase : str = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def __lowercase ( ) ->None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
A : float
A : TreeNode | None = None
A : TreeNode | None = None
def __lowercase ( _UpperCamelCase ) ->bool:
"""simple docstring"""
def is_valid_tree(_UpperCamelCase ) -> bool:
if node is None:
return True
if not isinstance(_UpperCamelCase, _UpperCamelCase ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(_UpperCamelCase ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left, _UpperCamelCase, node.data )
and is_binary_search_tree_recursive_check(
node.right, node.data, _UpperCamelCase )
)
return is_binary_search_tree_recursive_check(_UpperCamelCase, -float('''inf''' ), float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[List[np.ndarray], torch.FloatTensor]
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 .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 337 | 1 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Optional[int] = 'efficientnet'
def __init__( self , SCREAMING_SNAKE_CASE__ = 3 , SCREAMING_SNAKE_CASE__ = 600 , SCREAMING_SNAKE_CASE__ = 2.0 , SCREAMING_SNAKE_CASE__ = 3.1 , SCREAMING_SNAKE_CASE__ = 8 , SCREAMING_SNAKE_CASE__ = [3, 3, 5, 3, 5, 5, 3] , SCREAMING_SNAKE_CASE__ = [32, 16, 24, 40, 80, 112, 192] , SCREAMING_SNAKE_CASE__ = [16, 24, 40, 80, 112, 192, 320] , SCREAMING_SNAKE_CASE__ = [] , SCREAMING_SNAKE_CASE__ = [1, 2, 2, 2, 1, 2, 1] , SCREAMING_SNAKE_CASE__ = [1, 2, 2, 3, 3, 4, 1] , SCREAMING_SNAKE_CASE__ = [1, 6, 6, 6, 6, 6, 6] , SCREAMING_SNAKE_CASE__ = 0.25 , SCREAMING_SNAKE_CASE__ = "swish" , SCREAMING_SNAKE_CASE__ = 2560 , SCREAMING_SNAKE_CASE__ = "mean" , SCREAMING_SNAKE_CASE__ = 0.02 , SCREAMING_SNAKE_CASE__ = 0.001 , SCREAMING_SNAKE_CASE__ = 0.99 , SCREAMING_SNAKE_CASE__ = 0.5 , SCREAMING_SNAKE_CASE__ = 0.2 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = num_channels
lowercase : Optional[Any] = image_size
lowercase : int = width_coefficient
lowercase : Union[str, Any] = depth_coefficient
lowercase : Optional[int] = depth_divisor
lowercase : Optional[Any] = kernel_sizes
lowercase : int = in_channels
lowercase : Union[str, Any] = out_channels
lowercase : List[str] = depthwise_padding
lowercase : Optional[int] = strides
lowercase : Any = num_block_repeats
lowercase : str = expand_ratios
lowercase : Any = squeeze_expansion_ratio
lowercase : List[Any] = hidden_act
lowercase : Optional[int] = hidden_dim
lowercase : List[Any] = pooling_type
lowercase : List[str] = initializer_range
lowercase : str = batch_norm_eps
lowercase : Optional[int] = batch_norm_momentum
lowercase : Any = dropout_rate
lowercase : int = drop_connect_rate
lowercase : Dict = sum(SCREAMING_SNAKE_CASE__ ) * 4
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[Any] = version.parse('1.11' )
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
return 1E-5
| 337 |
import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__a = logging.get_logger(__name__)
__a = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__a = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__a = {'''facebook/blenderbot-3B''': 1_28}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Dict = VOCAB_FILES_NAMES
A : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Optional[int] = ['input_ids', 'attention_mask']
A : str = BlenderbotTokenizer
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="replace" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , tokenizer_file=SCREAMING_SNAKE_CASE__ , errors=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ , trim_offsets=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
lowercase : str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : List[Any] = getattr(SCREAMING_SNAKE_CASE__ , pre_tok_state.pop('''type''' ) )
lowercase : str = add_prefix_space
lowercase : List[Any] = pre_tok_class(**SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = add_prefix_space
lowercase : str = '''post_processor'''
lowercase : str = getattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if tokenizer_component_instance:
lowercase : Optional[int] = 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:
lowercase : Tuple = tuple(state['''sep'''] )
if "cls" in state:
lowercase : Union[str, Any] = tuple(state['''cls'''] )
lowercase : Optional[int] = False
if state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : Any = add_prefix_space
lowercase : Tuple = True
if state.get('''trim_offsets''' , SCREAMING_SNAKE_CASE__ ) != trim_offsets:
lowercase : List[str] = trim_offsets
lowercase : Optional[int] = True
if changes_to_apply:
lowercase : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE__ , state.pop('''type''' ) )
lowercase : Union[str, Any] = component_class(**SCREAMING_SNAKE_CASE__ )
setattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def __lowerCamelCase ( self ):
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else value
lowercase : Any = value
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
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(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Any = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
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(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : int = self._tokenizer.model.save(SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
return tuple(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Tuple = [self.sep_token_id]
lowercase : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
return token_ids_a + [self.eos_token_id]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = ''' '''.join(SCREAMING_SNAKE_CASE__ )
lowercase : Any = self.encode(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > self.model_max_length:
lowercase : Tuple = input_ids[-self.model_max_length :]
logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 337 | 1 |
def __lowercase ( _UpperCamelCase = 10**12 ) ->int:
"""simple docstring"""
lowercase : Tuple = 1
lowercase : List[str] = 0
lowercase : List[str] = 1
lowercase : List[Any] = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ) ->int:
"""simple docstring"""
lowercase : Tuple = HfArgumentParser(_UpperCamelCase )
lowercase : List[str] = parser.parse_args_into_dataclasses()[0]
lowercase : Optional[int] = TensorFlowBenchmark(args=_UpperCamelCase )
try:
lowercase : Any = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase : Optional[int] = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
lowercase : Any = ''' '''.join(str(_UpperCamelCase ).split(''' ''' )[:-1] )
lowercase : Any = ''''''
lowercase : str = eval(str(_UpperCamelCase ).split(''' ''' )[-1] )
lowercase : List[str] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
lowercase : Union[str, Any] = full_error_msg + begin_error_msg + str(_UpperCamelCase )
raise ValueError(_UpperCamelCase )
benchmark.run()
if __name__ == "__main__":
main()
| 337 | 1 |
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 __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , ):
super().__init__()
self.register_modules(transformer=SCREAMING_SNAKE_CASE__ , vae=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
# create a imagenet -> id dictionary for easier use
lowercase : Any = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''',''' ):
lowercase : List[str] = int(SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = dict(sorted(self.labels.items() ) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Union[str, Any] = list(SCREAMING_SNAKE_CASE__ )
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 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 4.0 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 50 , SCREAMING_SNAKE_CASE__ = "pil" , SCREAMING_SNAKE_CASE__ = True , ):
lowercase : int = len(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = self.transformer.config.sample_size
lowercase : Optional[Any] = self.transformer.config.in_channels
lowercase : Dict = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=SCREAMING_SNAKE_CASE__ , device=self.device , dtype=self.transformer.dtype , )
lowercase : Any = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
lowercase : Dict = torch.tensor(SCREAMING_SNAKE_CASE__ , device=self.device ).reshape(-1 )
lowercase : Any = torch.tensor([1000] * batch_size , device=self.device )
lowercase : Optional[Any] = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
lowercase : int = latent_model_input[: len(SCREAMING_SNAKE_CASE__ ) // 2]
lowercase : List[Any] = torch.cat([half, half] , dim=0 )
lowercase : Any = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = t
if not torch.is_tensor(SCREAMING_SNAKE_CASE__ ):
# 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+)
lowercase : Any = latent_model_input.device.type == '''mps'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = torch.floataa if is_mps else torch.floataa
else:
lowercase : Optional[Any] = torch.intaa if is_mps else torch.intaa
lowercase : int = torch.tensor([timesteps] , dtype=SCREAMING_SNAKE_CASE__ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
lowercase : Optional[Any] = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowercase : str = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
lowercase : Optional[Any] = self.transformer(
SCREAMING_SNAKE_CASE__ , timestep=SCREAMING_SNAKE_CASE__ , class_labels=SCREAMING_SNAKE_CASE__ ).sample
# perform guidance
if guidance_scale > 1:
lowercase , lowercase : Tuple = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
lowercase , lowercase : Union[str, Any] = torch.split(SCREAMING_SNAKE_CASE__ , len(SCREAMING_SNAKE_CASE__ ) // 2 , dim=0 )
lowercase : Tuple = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
lowercase : int = torch.cat([half_eps, half_eps] , dim=0 )
lowercase : Tuple = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
lowercase , lowercase : int = torch.split(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , dim=1 )
else:
lowercase : Any = noise_pred
# compute previous image: x_t -> x_t-1
lowercase : Dict = self.scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).prev_sample
if guidance_scale > 1:
lowercase , lowercase : Optional[int] = latent_model_input.chunk(2 , dim=0 )
else:
lowercase : int = latent_model_input
lowercase : Any = 1 / self.vae.config.scaling_factor * latents
lowercase : str = self.vae.decode(SCREAMING_SNAKE_CASE__ ).sample
lowercase : List[str] = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase : Any = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase : Optional[Any] = self.numpy_to_pil(SCREAMING_SNAKE_CASE__ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__ )
| 337 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : Union[str, Any] = [False] * len(_UpperCamelCase )
lowercase : Optional[int] = []
queue.append(_UpperCamelCase )
lowercase : Union[str, Any] = True
while queue:
lowercase : List[str] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_UpperCamelCase )
lowercase : Tuple = True
lowercase : Optional[Any] = u
return visited[t]
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : List[str] = [-1] * (len(_UpperCamelCase ))
lowercase : int = 0
while bfs(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : List[str] = float('''Inf''' )
lowercase : int = sink
while s != source:
# Find the minimum value in select path
lowercase : List[Any] = min(_UpperCamelCase, graph[parent[s]][s] )
lowercase : Union[str, Any] = parent[s]
max_flow += path_flow
lowercase : Optional[int] = sink
while v != source:
lowercase : Any = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowercase : Union[str, Any] = parent[v]
return max_flow
__a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__a , __a = 0, 5
print(ford_fulkerson(graph, source, sink))
| 337 | 1 |
def __lowercase ( _UpperCamelCase ) ->bool:
"""simple docstring"""
lowercase : str = 0
for ch in input_str:
lowercase : Dict = ord(_UpperCamelCase )
lowercase : str = pow(2, _UpperCamelCase )
# 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()
| 337 |
from typing import List
from .keymap import KEYMAP, get_character
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : str = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += [key]
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
def __lowercase ( *_UpperCamelCase ) ->Any:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : List[Any] = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += keys
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
class __SCREAMING_SNAKE_CASE ( A__ ):
def __new__( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = super().__new__(cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not hasattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' ):
setattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' , {} )
setattr(SCREAMING_SNAKE_CASE__ , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
lowercase : Dict = getattr(SCREAMING_SNAKE_CASE__ , '''handle_key''' , [] )
for key in handled_keys:
lowercase : List[Any] = value
return new_cls
@staticmethod
def __lowerCamelCase ( cls ):
lowercase : Dict = get_character()
if char != KEYMAP["undefined"]:
lowercase : Optional[int] = ord(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = cls.key_handler.get(SCREAMING_SNAKE_CASE__ )
if handler:
lowercase : Tuple = char
return handler(cls )
else:
return None
def __lowercase ( cls ) ->Any:
"""simple docstring"""
return KeyHandler(cls.__name__, cls.__bases__, cls.__dict__.copy() )
| 337 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class __SCREAMING_SNAKE_CASE ( A__ ):
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__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=99 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=512 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__="last" , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , ):
lowercase : List[str] = parent
lowercase : List[Any] = batch_size
lowercase : Optional[int] = seq_length
lowercase : str = is_training
lowercase : Tuple = use_input_lengths
lowercase : Optional[Any] = use_token_type_ids
lowercase : Optional[Any] = use_labels
lowercase : Tuple = gelu_activation
lowercase : Tuple = sinusoidal_embeddings
lowercase : str = causal
lowercase : Optional[int] = asm
lowercase : Optional[Any] = n_langs
lowercase : Optional[int] = vocab_size
lowercase : List[Any] = n_special
lowercase : Dict = hidden_size
lowercase : List[Any] = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : Tuple = hidden_dropout_prob
lowercase : str = attention_probs_dropout_prob
lowercase : Tuple = max_position_embeddings
lowercase : Optional[Any] = type_vocab_size
lowercase : Optional[int] = type_sequence_label_size
lowercase : Union[str, Any] = initializer_range
lowercase : int = num_labels
lowercase : str = num_choices
lowercase : str = summary_type
lowercase : Optional[Any] = use_proj
lowercase : List[Any] = scope
def __lowerCamelCase ( self ):
lowercase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase : List[Any] = None
if self.use_input_lengths:
lowercase : Union[str, Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowercase : Optional[int] = None
if self.use_token_type_ids:
lowercase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowercase : int = None
lowercase : Optional[Any] = None
lowercase : Optional[Any] = None
if self.use_labels:
lowercase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : Tuple = ids_tensor([self.batch_size] , 2 ).float()
lowercase : Any = ids_tensor([self.batch_size] , self.num_choices )
lowercase : Union[str, Any] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __lowerCamelCase ( self ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : Optional[int] = FlaubertModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , lengths=SCREAMING_SNAKE_CASE__ , langs=SCREAMING_SNAKE_CASE__ )
lowercase : Any = model(SCREAMING_SNAKE_CASE__ , langs=SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : List[str] = FlaubertWithLMHeadModel(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Any = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : Dict = FlaubertForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Any = model(SCREAMING_SNAKE_CASE__ )
lowercase : Any = model(SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : Union[str, Any] = FlaubertForQuestionAnswering(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : List[str] = model(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = model(
SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ , cls_index=SCREAMING_SNAKE_CASE__ , is_impossible=SCREAMING_SNAKE_CASE__ , p_mask=SCREAMING_SNAKE_CASE__ , )
lowercase : int = model(
SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ , cls_index=SCREAMING_SNAKE_CASE__ , is_impossible=SCREAMING_SNAKE_CASE__ , )
((lowercase) , ) : Optional[int] = result_with_labels.to_tuple()
lowercase : Tuple = model(SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ )
((lowercase) , ) : List[str] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : Any = FlaubertForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Any = model(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : Union[str, Any] = self.num_labels
lowercase : List[Any] = FlaubertForTokenClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : Any = self.num_choices
lowercase : int = FlaubertForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : Tuple = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : Optional[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : Optional[Any] = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCamelCase ( self ):
lowercase : List[str] = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) : Tuple = config_and_inputs
lowercase : Any = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
A : Optional[int] = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
A : Optional[int] = (
{
'feature-extraction': FlaubertModel,
'fill-mask': FlaubertWithLMHeadModel,
'question-answering': FlaubertForQuestionAnsweringSimple,
'text-classification': FlaubertForSequenceClassification,
'token-classification': FlaubertForTokenClassification,
'zero-shot': FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
lowercase : Tuple = super()._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_labels=SCREAMING_SNAKE_CASE__ )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
lowercase : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ )
lowercase : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ )
return inputs_dict
def __lowerCamelCase ( self ):
lowercase : str = FlaubertModelTester(self )
lowercase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , emb_dim=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
lowercase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCamelCase ( self ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : Union[str, Any] = FlaubertModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@slow
@require_torch_gpu
def __lowerCamelCase ( self ):
lowercase , lowercase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
lowercase : Dict = True
lowercase : str = model_class(config=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = self._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = torch.jit.trace(
SCREAMING_SNAKE_CASE__ , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(SCREAMING_SNAKE_CASE__ , os.path.join(SCREAMING_SNAKE_CASE__ , '''traced_model.pt''' ) )
lowercase : Optional[Any] = torch.jit.load(os.path.join(SCREAMING_SNAKE_CASE__ , '''traced_model.pt''' ) , map_location=SCREAMING_SNAKE_CASE__ )
loaded(inputs_dict['''input_ids'''].to(SCREAMING_SNAKE_CASE__ ) , inputs_dict['''attention_mask'''].to(SCREAMING_SNAKE_CASE__ ) )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __lowerCamelCase ( self ):
lowercase : Tuple = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
lowercase : Optional[int] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
lowercase : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ )[0]
lowercase : Any = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
lowercase : str = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) )
| 337 |
import logging
import os
from .state import PartialState
class __SCREAMING_SNAKE_CASE ( logging.LoggerAdapter ):
@staticmethod
def __lowerCamelCase ( SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
lowercase : List[str] = kwargs.pop('''main_process_only''' , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = kwargs.pop('''in_order''' , SCREAMING_SNAKE_CASE__ )
if self.isEnabledFor(SCREAMING_SNAKE_CASE__ ):
if self._should_log(SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : str = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
elif in_order:
lowercase : List[Any] = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
lowercase , lowercase : Union[str, Any] = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
state.wait_for_everyone()
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->List[Any]:
"""simple docstring"""
if log_level is None:
lowercase : str = os.environ.get('''ACCELERATE_LOG_LEVEL''', _UpperCamelCase )
lowercase : str = logging.getLogger(_UpperCamelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_UpperCamelCase, {} )
| 337 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__a = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__a = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__a = {
'''sample_size''': 2_56,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__a = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.0_0_2,
'''sigma_max''': 8_0.0,
}
__a = {
'''num_train_timesteps''': 2_01,
'''sigma_min''': 0.0_0_2,
'''sigma_max''': 8_0.0,
}
__a = {
'''num_train_timesteps''': 1_51,
'''sigma_min''': 0.0_0_2,
'''sigma_max''': 8_0.0,
}
def __lowercase ( _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
if isinstance(_UpperCamelCase, _UpperCamelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError('''boolean value expected''' )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=False ) ->Any:
"""simple docstring"""
lowercase : Any = checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
lowercase : List[str] = checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
lowercase : str = checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
lowercase : Dict = checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
lowercase : Optional[int] = checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
lowercase : Dict = checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
lowercase : Optional[int] = checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
lowercase : int = checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
lowercase : Union[str, Any] = checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
lowercase : Optional[Any] = checkpoint[f"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
lowercase : int = checkpoint[f"""{old_prefix}.skip_connection.weight"""]
lowercase : List[Any] = checkpoint[f"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=None ) ->Dict:
"""simple docstring"""
lowercase , lowercase , lowercase : Optional[int] = checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3, dim=0 )
lowercase , lowercase , lowercase : str = checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3, dim=0 )
lowercase : Dict = checkpoint[f"""{old_prefix}.norm.weight"""]
lowercase : List[Any] = checkpoint[f"""{old_prefix}.norm.bias"""]
lowercase : Union[str, Any] = weight_q.squeeze(-1 ).squeeze(-1 )
lowercase : Optional[int] = bias_q.squeeze(-1 ).squeeze(-1 )
lowercase : Dict = weight_k.squeeze(-1 ).squeeze(-1 )
lowercase : Dict = bias_k.squeeze(-1 ).squeeze(-1 )
lowercase : List[Any] = weight_v.squeeze(-1 ).squeeze(-1 )
lowercase : Tuple = bias_v.squeeze(-1 ).squeeze(-1 )
lowercase : Optional[Any] = (
checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
lowercase : Tuple = checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""simple docstring"""
lowercase : Tuple = torch.load(_UpperCamelCase, map_location='''cpu''' )
lowercase : Any = {}
lowercase : Tuple = checkpoint['''time_embed.0.weight''']
lowercase : Union[str, Any] = checkpoint['''time_embed.0.bias''']
lowercase : Any = checkpoint['''time_embed.2.weight''']
lowercase : Tuple = checkpoint['''time_embed.2.bias''']
if unet_config["num_class_embeds"] is not None:
lowercase : List[str] = checkpoint['''label_emb.weight''']
lowercase : Optional[Any] = checkpoint['''input_blocks.0.0.weight''']
lowercase : Optional[Any] = checkpoint['''input_blocks.0.0.bias''']
lowercase : Dict = unet_config['''down_block_types''']
lowercase : List[str] = unet_config['''layers_per_block''']
lowercase : Dict = unet_config['''attention_head_dim''']
lowercase : str = unet_config['''block_out_channels''']
lowercase : int = 1
lowercase : Tuple = channels_list[0]
for i, layer_type in enumerate(_UpperCamelCase ):
lowercase : int = channels_list[i]
lowercase : Any = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(_UpperCamelCase ):
lowercase : Optional[int] = f"""down_blocks.{i}.resnets.{j}"""
lowercase : List[str] = f"""input_blocks.{current_layer}.0"""
lowercase : Optional[Any] = True if j == 0 and downsample_block_has_skip else False
lowercase : List[Any] = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, has_skip=_UpperCamelCase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(_UpperCamelCase ):
lowercase : Optional[int] = f"""down_blocks.{i}.resnets.{j}"""
lowercase : Optional[Any] = f"""input_blocks.{current_layer}.0"""
lowercase : Optional[int] = True if j == 0 and downsample_block_has_skip else False
lowercase : str = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, has_skip=_UpperCamelCase )
lowercase : Tuple = f"""down_blocks.{i}.attentions.{j}"""
lowercase : int = f"""input_blocks.{current_layer}.1"""
lowercase : str = convert_attention(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
current_layer += 1
if i != len(_UpperCamelCase ) - 1:
lowercase : List[Any] = f"""down_blocks.{i}.downsamplers.0"""
lowercase : List[Any] = f"""input_blocks.{current_layer}.0"""
lowercase : Optional[Any] = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
current_layer += 1
lowercase : Union[str, Any] = current_channels
# hardcoded the mid-block for now
lowercase : Dict = '''mid_block.resnets.0'''
lowercase : Tuple = '''middle_block.0'''
lowercase : List[Any] = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
lowercase : Optional[Any] = '''mid_block.attentions.0'''
lowercase : str = '''middle_block.1'''
lowercase : str = convert_attention(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
lowercase : List[str] = '''mid_block.resnets.1'''
lowercase : List[str] = '''middle_block.2'''
lowercase : Optional[int] = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
lowercase : Tuple = 0
lowercase : int = unet_config['''up_block_types''']
for i, layer_type in enumerate(_UpperCamelCase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
lowercase : Union[str, Any] = f"""up_blocks.{i}.resnets.{j}"""
lowercase : Tuple = f"""output_blocks.{current_layer}.0"""
lowercase : Dict = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, has_skip=_UpperCamelCase )
current_layer += 1
if i != len(_UpperCamelCase ) - 1:
lowercase : List[str] = f"""up_blocks.{i}.upsamplers.0"""
lowercase : int = f"""output_blocks.{current_layer-1}.1"""
lowercase : str = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
lowercase : List[str] = f"""up_blocks.{i}.resnets.{j}"""
lowercase : str = f"""output_blocks.{current_layer}.0"""
lowercase : Dict = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, has_skip=_UpperCamelCase )
lowercase : List[str] = f"""up_blocks.{i}.attentions.{j}"""
lowercase : Union[str, Any] = f"""output_blocks.{current_layer}.1"""
lowercase : Optional[int] = convert_attention(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
current_layer += 1
if i != len(_UpperCamelCase ) - 1:
lowercase : Union[str, Any] = f"""up_blocks.{i}.upsamplers.0"""
lowercase : int = f"""output_blocks.{current_layer-1}.2"""
lowercase : Optional[int] = convert_resnet(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
lowercase : int = checkpoint['''out.0.weight''']
lowercase : List[Any] = checkpoint['''out.0.bias''']
lowercase : List[str] = checkpoint['''out.2.weight''']
lowercase : Union[str, Any] = checkpoint['''out.2.bias''']
return new_checkpoint
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
__a = parser.parse_args()
__a = strabool(args.class_cond)
__a = os.path.basename(args.unet_path)
print(F'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
__a = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__a = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__a = TEST_UNET_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
__a = None
__a = con_pt_to_diffuser(args.unet_path, unet_config)
__a = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__a = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__a = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__a = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
__a = CMStochasticIterativeScheduler(**scheduler_config)
__a = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 337 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super().__init__()
lowercase : Any = model
lowercase : Optional[Any] = 2
lowercase : Optional[int] = nn.Linear(self.model.config.hidden_size , self.num_labels )
def __lowerCamelCase ( self ):
pass
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : str = LongformerModel.from_pretrained(_UpperCamelCase )
lowercase : int = LightningModel(_UpperCamelCase )
lowercase : Union[str, Any] = torch.load(_UpperCamelCase, map_location=torch.device('''cpu''' ) )
lightning_model.load_state_dict(ckpt['''state_dict'''] )
# init longformer question answering model
lowercase : List[Any] = LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_UpperCamelCase )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['''ViTFeatureExtractor''']
__a = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 |
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
__a = logging.get_logger(__name__)
__a = {
'''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Any = 'yolos'
def __init__( self , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=3072 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=[512, 864] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=100 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = hidden_size
lowercase : int = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : str = intermediate_size
lowercase : Dict = hidden_act
lowercase : int = hidden_dropout_prob
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : List[Any] = initializer_range
lowercase : Optional[int] = layer_norm_eps
lowercase : str = image_size
lowercase : Dict = patch_size
lowercase : str = num_channels
lowercase : Optional[int] = qkv_bias
lowercase : List[str] = num_detection_tokens
lowercase : List[str] = use_mid_position_embeddings
lowercase : Dict = auxiliary_loss
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : Any = bbox_cost
lowercase : int = giou_cost
# Loss coefficients
lowercase : Dict = bbox_loss_coefficient
lowercase : Optional[Any] = giou_loss_coefficient
lowercase : Tuple = eos_coefficient
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = version.parse('1.11' )
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
@property
def __lowerCamelCase ( self ):
return 12
| 337 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.17.0.dev0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')
__a = logging.getLogger(__name__)
@dataclass
class __SCREAMING_SNAKE_CASE :
A : Optional[str] = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
A : Optional[str] = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
A : int = field(
default=1024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
A : bool = field(
default=A__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
A : bool = field(
default=A__ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
A : Optional[int] = field(
default=A__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
A : Optional[int] = field(
default=A__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
A : Optional[int] = field(
default=A__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
A : Optional[str] = field(
default=A__ , metadata={'help': 'A csv or a json file containing the training data.'} )
A : Optional[str] = field(
default=A__ , metadata={'help': 'A csv or a json file containing the validation data.'} )
A : Optional[str] = field(default=A__ , metadata={'help': 'A csv or a json file containing the test data.'} )
def __lowerCamelCase ( self ):
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('''Need either a GLUE task, a training/validation file or a dataset name.''' )
else:
lowercase : Tuple = self.train_file.split('''.''' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
lowercase : List[Any] = self.validation_file.split('''.''' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __SCREAMING_SNAKE_CASE :
A : str = field(
default=A__ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
A : Optional[str] = field(
default=A__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
A : Optional[str] = field(
default=A__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
A : Optional[str] = field(
default=A__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
A : bool = field(
default=A__ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
A : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
A : bool = field(
default=A__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def __lowercase ( ) ->Any:
"""simple docstring"""
lowercase : List[Any] = 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.
lowercase , lowercase , lowercase : Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase : Dict = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', handlers=[logging.StreamHandler(sys.stdout )], )
lowercase : Optional[Any] = training_args.get_process_log_level()
logger.setLevel(_UpperCamelCase )
datasets.utils.logging.set_verbosity(_UpperCamelCase )
transformers.utils.logging.set_verbosity(_UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowercase : Optional[Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase : int = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowercase : Union[str, Any] = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
lowercase : Any = {'''train''': data_args.train_file, '''validation''': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
lowercase : Any = data_args.train_file.split('''.''' )[-1]
lowercase : List[Any] = data_args.test_file.split('''.''' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
lowercase : str = data_args.test_file
else:
raise ValueError('''Need either a GLUE task or a test file for `do_predict`.''' )
for key in data_files.keys():
logger.info(f"""load a local file for {key}: {data_files[key]}""" )
if data_args.train_file.endswith('''.csv''' ):
# Loading a dataset from local csv files
lowercase : str = load_dataset('''csv''', data_files=_UpperCamelCase, cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
lowercase : Tuple = load_dataset('''json''', data_files=_UpperCamelCase, cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
lowercase : List[Any] = raw_datasets['''train'''].features['''label'''].names
lowercase : Optional[Any] = len(_UpperCamelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=_UpperCamelCase, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
# load tapex tokenizer
lowercase : Optional[Any] = TapexTokenizer.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_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, add_prefix_space=_UpperCamelCase, )
lowercase : str = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path ), config=_UpperCamelCase, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
# Padding strategy
if data_args.pad_to_max_length:
lowercase : Dict = '''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowercase : List[str] = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
lowercase : Union[str, Any] = {'''Refused''': 0, '''Entailed''': 1}
lowercase : int = {0: '''Refused''', 1: '''Entailed'''}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
lowercase : int = min(data_args.max_seq_length, tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCamelCase ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCamelCase ):
lowercase : List[str] = [_table_row.split('''#''' ) for _table_row in _table_text.strip('''\n''' ).split('''\n''' )]
lowercase : int = pd.DataFrame.from_records(_table_content[1:], columns=_table_content[0] )
return _table_pd
lowercase : Union[str, Any] = examples['''statement''']
lowercase : Any = list(map(_convert_table_text_to_pandas, examples['''table_text'''] ) )
lowercase : str = tokenizer(_UpperCamelCase, _UpperCamelCase, padding=_UpperCamelCase, max_length=_UpperCamelCase, truncation=_UpperCamelCase )
lowercase : Dict = examples['''label''']
return result
with training_args.main_process_first(desc='''dataset map pre-processing''' ):
lowercase : int = raw_datasets.map(
_UpperCamelCase, batched=_UpperCamelCase, load_from_cache_file=not data_args.overwrite_cache, desc='''Running tokenizer on dataset''', )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
lowercase : Tuple = raw_datasets['''train''']
if data_args.max_train_samples is not None:
lowercase : Dict = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
lowercase : Tuple = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
lowercase : Dict = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('''--do_predict requires a test dataset''' )
lowercase : List[Any] = raw_datasets['''test''']
if data_args.max_predict_samples is not None:
lowercase : int = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCamelCase ) ), 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCamelCase ):
lowercase : List[str] = p.predictions[0] if isinstance(p.predictions, _UpperCamelCase ) else p.predictions
lowercase : Tuple = np.argmax(_UpperCamelCase, axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowercase : Optional[Any] = default_data_collator
elif training_args.fpaa:
lowercase : List[Any] = DataCollatorWithPadding(_UpperCamelCase, pad_to_multiple_of=8 )
else:
lowercase : int = None
# Initialize our Trainer
lowercase : int = Trainer(
model=_UpperCamelCase, args=_UpperCamelCase, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, compute_metrics=_UpperCamelCase, tokenizer=_UpperCamelCase, data_collator=_UpperCamelCase, )
# Training
if training_args.do_train:
lowercase : str = None
if training_args.resume_from_checkpoint is not None:
lowercase : int = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase : int = last_checkpoint
lowercase : Union[str, Any] = trainer.train(resume_from_checkpoint=_UpperCamelCase )
lowercase : Optional[Any] = train_result.metrics
lowercase : Optional[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCamelCase )
)
lowercase : Union[str, Any] = min(_UpperCamelCase, len(_UpperCamelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''', _UpperCamelCase )
trainer.save_metrics('''train''', _UpperCamelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase : Optional[int] = trainer.evaluate(eval_dataset=_UpperCamelCase )
lowercase : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCamelCase )
lowercase : List[Any] = min(_UpperCamelCase, len(_UpperCamelCase ) )
trainer.log_metrics('''eval''', _UpperCamelCase )
trainer.save_metrics('''eval''', _UpperCamelCase )
if training_args.do_predict:
logger.info('''*** Predict ***''' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
lowercase : Optional[Any] = predict_dataset.remove_columns('''label''' )
lowercase : List[str] = trainer.predict(_UpperCamelCase, metric_key_prefix='''predict''' ).predictions
lowercase : str = np.argmax(_UpperCamelCase, axis=1 )
lowercase : Optional[Any] = os.path.join(training_args.output_dir, '''predict_results_tabfact.txt''' )
if trainer.is_world_process_zero():
with open(_UpperCamelCase, '''w''' ) as writer:
logger.info('''***** Predict Results *****''' )
writer.write('''index\tprediction\n''' )
for index, item in enumerate(_UpperCamelCase ):
lowercase : Optional[Any] = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
lowercase : List[Any] = {'''finetuned_from''': model_args.model_name_or_path, '''tasks''': '''text-classification'''}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCamelCase )
else:
trainer.create_model_card(**_UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->Tuple:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 337 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__a = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""simple docstring"""
if got_ver is None or want_ver is None:
raise ValueError(
f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
f""" reinstalling {pkg}.""" )
if not ops[op](version.parse(_UpperCamelCase ), version.parse(_UpperCamelCase ) ):
raise ImportError(
f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->None:
"""simple docstring"""
lowercase : List[Any] = f"""\n{hint}""" if hint is not None else ''''''
# non-versioned check
if re.match(R'''^[\w_\-\d]+$''', _UpperCamelCase ):
lowercase , lowercase , lowercase : Optional[Any] = requirement, None, None
else:
lowercase : List[Any] = re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
f""" got {requirement}""" )
lowercase , lowercase : str = match[0]
lowercase : Tuple = want_full.split(''',''' ) # there could be multiple requirements
lowercase : List[Any] = {}
for w in want_range:
lowercase : str = re.findall(R'''^([\s!=<>]{1,2})(.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
f""" but got {requirement}""" )
lowercase , lowercase : Optional[int] = match[0]
lowercase : Dict = want_ver
if op not in ops:
raise ValueError(f"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )
# special case
if pkg == "python":
lowercase : int = '''.'''.join([str(_UpperCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
return
# check if any version is installed
try:
lowercase : List[str] = importlib.metadata.version(_UpperCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
f"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : Optional[int] = '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
return require_version(_UpperCamelCase, _UpperCamelCase )
| 337 | 1 |
from __future__ import annotations
from collections.abc import Callable
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 100, ) ->float:
"""simple docstring"""
lowercase : Optional[int] = x_start
lowercase : Optional[int] = fnc(_UpperCamelCase )
lowercase : List[str] = 0.0
for _ in range(_UpperCamelCase ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
lowercase : List[Any] = (x_end - x_start) / steps + xa
lowercase : str = fnc(_UpperCamelCase )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
lowercase : List[str] = xa
lowercase : Optional[Any] = fxa
return area
if __name__ == "__main__":
def __lowercase ( _UpperCamelCase ) ->Tuple:
"""simple docstring"""
return x**3 + x**2
print('''f(x) = x^3 + x^2''')
print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
__a = 10
while i <= 10_00_00:
print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
i *= 10
| 337 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'deta'
A : Dict = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=900 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=1024 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="relu" , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="sine" , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=300 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.25 , **SCREAMING_SNAKE_CASE__ , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase : Tuple = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = backbone_config.pop('''model_type''' )
lowercase : Any = CONFIG_MAPPING[backbone_model_type]
lowercase : List[Any] = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = backbone_config
lowercase : Union[str, Any] = num_queries
lowercase : Any = max_position_embeddings
lowercase : int = d_model
lowercase : Any = encoder_ffn_dim
lowercase : Optional[int] = encoder_layers
lowercase : Tuple = encoder_attention_heads
lowercase : Optional[Any] = decoder_ffn_dim
lowercase : Optional[int] = decoder_layers
lowercase : int = decoder_attention_heads
lowercase : Any = dropout
lowercase : int = attention_dropout
lowercase : Dict = activation_dropout
lowercase : int = activation_function
lowercase : Dict = init_std
lowercase : List[str] = init_xavier_std
lowercase : Optional[Any] = encoder_layerdrop
lowercase : Tuple = auxiliary_loss
lowercase : Tuple = position_embedding_type
# deformable attributes
lowercase : List[str] = num_feature_levels
lowercase : Tuple = encoder_n_points
lowercase : Optional[int] = decoder_n_points
lowercase : Tuple = two_stage
lowercase : Optional[Any] = two_stage_num_proposals
lowercase : Union[str, Any] = with_box_refine
lowercase : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : str = bbox_cost
lowercase : List[Any] = giou_cost
# Loss coefficients
lowercase : Tuple = mask_loss_coefficient
lowercase : Any = dice_loss_coefficient
lowercase : Dict = bbox_loss_coefficient
lowercase : Tuple = giou_loss_coefficient
lowercase : Union[str, Any] = eos_coefficient
lowercase : Tuple = focal_alpha
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __lowerCamelCase ( self ):
return self.encoder_attention_heads
@property
def __lowerCamelCase ( self ):
return self.d_model
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase : Any = self.backbone_config.to_dict()
lowercase : List[str] = self.__class__.model_type
return output
| 337 | 1 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=13 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=[10, 20, 30, 40] , SCREAMING_SNAKE_CASE__=[2, 2, 3, 2] , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=37 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=["stage2", "stage3", "stage4"] , SCREAMING_SNAKE_CASE__=[2, 3, 4] , SCREAMING_SNAKE_CASE__=None , ):
lowercase : List[Any] = parent
lowercase : Optional[Any] = batch_size
lowercase : Dict = image_size
lowercase : Optional[int] = num_channels
lowercase : Optional[int] = num_stages
lowercase : List[str] = hidden_sizes
lowercase : Optional[int] = depths
lowercase : Tuple = is_training
lowercase : Tuple = use_labels
lowercase : Any = intermediate_size
lowercase : Any = hidden_act
lowercase : Union[str, Any] = num_labels
lowercase : Optional[int] = initializer_range
lowercase : Union[str, Any] = out_features
lowercase : str = out_indices
lowercase : Any = scope
def __lowerCamelCase ( self ):
lowercase : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase : Optional[int] = None
if self.use_labels:
lowercase : int = ids_tensor([self.batch_size] , self.num_labels )
lowercase : str = self.get_config()
return config, pixel_values, labels
def __lowerCamelCase ( self ):
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=SCREAMING_SNAKE_CASE__ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = ConvNextModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Dict = model(SCREAMING_SNAKE_CASE__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Union[str, Any] = ConvNextForImageClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : List[str] = model(SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = ConvNextBackbone(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Any = model(SCREAMING_SNAKE_CASE__ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase : str = None
lowercase : List[str] = ConvNextBackbone(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : List[str] = model(SCREAMING_SNAKE_CASE__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : Any = config_and_inputs
lowercase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
A : Optional[int] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
A : int = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
A : Optional[int] = True
A : List[str] = False
A : int = False
A : Any = False
A : List[str] = False
def __lowerCamelCase ( self ):
lowercase : int = ConvNextModelTester(self )
lowercase : Optional[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , has_text_modality=SCREAMING_SNAKE_CASE__ , hidden_size=37 )
def __lowerCamelCase ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCamelCase ( self ):
return
@unittest.skip(reason='''ConvNext does not use inputs_embeds''' )
def __lowerCamelCase ( self ):
pass
@unittest.skip(reason='''ConvNext does not support input and output embeddings''' )
def __lowerCamelCase ( self ):
pass
@unittest.skip(reason='''ConvNext does not use feedforward chunking''' )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
lowercase , lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Any = model_class(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase : Any = [*signature.parameters.keys()]
lowercase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
def check_hidden_states_output(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = model_class(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
with torch.no_grad():
lowercase : Optional[int] = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
lowercase : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase : int = self.model_tester.num_stages
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase , lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Dict = True
check_hidden_states_output(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase : str = True
check_hidden_states_output(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCamelCase ( self ):
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : List[str] = ConvNextModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
def __lowercase ( ) ->List[str]:
"""simple docstring"""
lowercase : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __lowerCamelCase ( self ):
return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224''' ) if is_vision_available() else None
@slow
def __lowerCamelCase ( self ):
lowercase : Optional[int] = ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''' ).to(SCREAMING_SNAKE_CASE__ )
lowercase : int = self.default_image_processor
lowercase : Tuple = prepare_img()
lowercase : Optional[int] = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE__ )
# forward pass
with torch.no_grad():
lowercase : Optional[Any] = model(**SCREAMING_SNAKE_CASE__ )
# verify the logits
lowercase : Union[str, Any] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = torch.tensor([-0.0260, -0.4739, 0.1911] ).to(SCREAMING_SNAKE_CASE__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase , A__ ):
A : Union[str, Any] = (ConvNextBackbone,) if is_torch_available() else ()
A : int = ConvNextConfig
A : Dict = False
def __lowerCamelCase ( self ):
lowercase : Any = ConvNextModelTester(self )
| 337 |
def __lowercase ( ) ->List[Any]:
"""simple docstring"""
lowercase : Union[str, Any] = 0
for i in range(1, 1001 ):
total += i**i
return str(_UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 337 | 1 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : Union[str, Any] = [False] * len(_UpperCamelCase )
lowercase : Optional[int] = []
queue.append(_UpperCamelCase )
lowercase : Union[str, Any] = True
while queue:
lowercase : List[str] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_UpperCamelCase )
lowercase : Tuple = True
lowercase : Optional[Any] = u
return visited[t]
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : List[str] = [-1] * (len(_UpperCamelCase ))
lowercase : int = 0
while bfs(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : List[str] = float('''Inf''' )
lowercase : int = sink
while s != source:
# Find the minimum value in select path
lowercase : List[Any] = min(_UpperCamelCase, graph[parent[s]][s] )
lowercase : Union[str, Any] = parent[s]
max_flow += path_flow
lowercase : Optional[int] = sink
while v != source:
lowercase : Any = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowercase : Union[str, Any] = parent[v]
return max_flow
__a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__a , __a = 0, 5
print(ford_fulkerson(graph, source, sink))
| 337 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__a = 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_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__a = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self ):
lowercase : str = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
lowercase : Any = self.diffusers_dir
shutil.copy(
os.path.join(SCREAMING_SNAKE_CASE__ , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def __lowerCamelCase ( self ):
lowercase : List[Any] = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
lowercase : Tuple = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
lowercase : str = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
lowercase : Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowercase : List[Any] = black.format_str(SCREAMING_SNAKE_CASE__ , mode=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , newline='''\n''' ) as f:
f.write(SCREAMING_SNAKE_CASE__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , '''r''' ) as f:
self.assertTrue(f.read() , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Tuple = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with a really long name
lowercase : List[Any] = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , overwrite_result=re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
| 337 | 1 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : str = OmegaConf.load(_UpperCamelCase )
lowercase : Tuple = torch.load(_UpperCamelCase, map_location='''cpu''' )['''model''']
lowercase : Dict = list(state_dict.keys() )
# extract state_dict for VQVAE
lowercase : int = {}
lowercase : Any = '''first_stage_model.'''
for key in keys:
if key.startswith(_UpperCamelCase ):
lowercase : int = state_dict[key]
# extract state_dict for UNetLDM
lowercase : Union[str, Any] = {}
lowercase : Tuple = '''model.diffusion_model.'''
for key in keys:
if key.startswith(_UpperCamelCase ):
lowercase : Any = state_dict[key]
lowercase : List[Any] = config.model.params.first_stage_config.params
lowercase : List[str] = config.model.params.unet_config.params
lowercase : Optional[Any] = VQModel(**_UpperCamelCase ).eval()
vqvae.load_state_dict(_UpperCamelCase )
lowercase : Dict = UNetLDMModel(**_UpperCamelCase ).eval()
unet.load_state_dict(_UpperCamelCase )
lowercase : str = DDIMScheduler(
timesteps=config.model.params.timesteps, beta_schedule='''scaled_linear''', beta_start=config.model.params.linear_start, beta_end=config.model.params.linear_end, clip_sample=_UpperCamelCase, )
lowercase : List[str] = LDMPipeline(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
pipeline.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', type=str, required=True)
parser.add_argument('''--config_path''', type=str, required=True)
parser.add_argument('''--output_path''', type=str, required=True)
__a = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 337 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=0 ): # a graph with Node 0,1,...,N-1
lowercase : List[Any] = n
lowercase : List[Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # adjacency matrix for weight
lowercase : Union[str, Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # dp[i][j] stores minimum distance from i to j
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : int = w
def __lowerCamelCase ( self ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase : Any = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.dp[u][v]
if __name__ == "__main__":
__a = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 337 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__a = logging.get_logger(__name__)
__a = {'''vocab_file''': '''spiece.model'''}
__a = {
'''vocab_file''': {
'''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
}
}
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<sep>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<cls>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__=["<eop>", "<eod>"] , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
lowercase : Dict = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else mask_token
lowercase : Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=SCREAMING_SNAKE_CASE__ , remove_space=SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , additional_special_tokens=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , )
lowercase : Union[str, Any] = 3
lowercase : Optional[Any] = do_lower_case
lowercase : List[Any] = remove_space
lowercase : List[str] = keep_accents
lowercase : Tuple = vocab_file
lowercase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(SCREAMING_SNAKE_CASE__ )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
'''See https://pypi.org/project/jieba/ for installation.''' )
lowercase : Tuple = jieba
lowercase : Optional[Any] = str.maketrans(''' \n''' , '''\u2582\u2583''' )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def __lowerCamelCase ( self ):
return len(self.sp_model )
def __lowerCamelCase ( self ):
lowercase : int = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
lowercase : Tuple = self.__dict__.copy()
lowercase : Any = None
return state
def __setstate__( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase : int = {}
lowercase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if self.remove_space:
lowercase : int = ''' '''.join(inputs.strip().split() )
else:
lowercase : Any = inputs
lowercase : List[Any] = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
lowercase : str = unicodedata.normalize('''NFKD''' , SCREAMING_SNAKE_CASE__ )
lowercase : str = ''''''.join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE__ )] )
if self.do_lower_case:
lowercase : Union[str, Any] = outputs.lower()
return outputs
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = self.preprocess_text(SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ )
lowercase : Any = []
for piece in pieces:
if len(SCREAMING_SNAKE_CASE__ ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
lowercase : Dict = self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE__ , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
lowercase : List[str] = cur_pieces[1:]
else:
lowercase : Dict = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(SCREAMING_SNAKE_CASE__ )
else:
new_pieces.append(SCREAMING_SNAKE_CASE__ )
return new_pieces
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = ''''''.join(SCREAMING_SNAKE_CASE__ ).replace(SCREAMING_SNAKE_CASE__ , ''' ''' ).strip()
return out_string
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : int = [self.sep_token_id]
lowercase : str = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is not None:
return ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1]
return ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Union[str, Any] = [self.sep_token_id]
lowercase : Tuple = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase : Optional[Any] = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE__ , '''wb''' ) as fi:
lowercase : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,)
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = super()._decode(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
lowercase : str = text.replace(''' ''' , '''''' ).replace('''\u2582''' , ''' ''' ).replace('''\u2583''' , '''\n''' )
return text
| 337 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(_UpperCamelCase ) / len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'''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 __SCREAMING_SNAKE_CASE ( A__ ):
A : str = 'dpr'
def __init__( self , SCREAMING_SNAKE_CASE__=30522 , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=12 , 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__=0 , SCREAMING_SNAKE_CASE__="absolute" , SCREAMING_SNAKE_CASE__ = 0 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
lowercase : str = vocab_size
lowercase : Dict = hidden_size
lowercase : Dict = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : Optional[Any] = hidden_act
lowercase : Any = intermediate_size
lowercase : Any = hidden_dropout_prob
lowercase : Union[str, Any] = attention_probs_dropout_prob
lowercase : List[Any] = max_position_embeddings
lowercase : str = type_vocab_size
lowercase : Optional[Any] = initializer_range
lowercase : Optional[Any] = layer_norm_eps
lowercase : str = projection_dim
lowercase : Dict = position_embedding_type
| 337 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 337 | 1 |
import warnings
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
__a = logging.get_logger(__name__)
__a = {
'''nvidia/segformer-b0-finetuned-ade-512-512''': (
'''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : int = 'segformer'
def __init__( self , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=[2, 2, 2, 2] , SCREAMING_SNAKE_CASE__=[8, 4, 2, 1] , SCREAMING_SNAKE_CASE__=[32, 64, 160, 256] , SCREAMING_SNAKE_CASE__=[7, 3, 3, 3] , SCREAMING_SNAKE_CASE__=[4, 2, 2, 2] , SCREAMING_SNAKE_CASE__=[1, 2, 5, 8] , SCREAMING_SNAKE_CASE__=[4, 4, 4, 4] , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=1E-6 , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=255 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
'''Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'''
''' removed, as the behaviour will default to that of reshape_last_stage = True.''' , SCREAMING_SNAKE_CASE__ , )
lowercase : Any = num_channels
lowercase : List[str] = num_encoder_blocks
lowercase : Union[str, Any] = depths
lowercase : str = sr_ratios
lowercase : List[Any] = hidden_sizes
lowercase : int = patch_sizes
lowercase : int = strides
lowercase : int = mlp_ratios
lowercase : str = num_attention_heads
lowercase : str = hidden_act
lowercase : List[Any] = hidden_dropout_prob
lowercase : List[str] = attention_probs_dropout_prob
lowercase : Optional[Any] = classifier_dropout_prob
lowercase : Dict = initializer_range
lowercase : List[Any] = drop_path_rate
lowercase : str = layer_norm_eps
lowercase : List[str] = decoder_hidden_size
lowercase : Union[str, Any] = kwargs.get('''reshape_last_stage''' , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = semantic_loss_ignore_index
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Optional[int] = version.parse('1.11' )
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
@property
def __lowerCamelCase ( self ):
return 12
| 337 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase ) ->List[int]:
"""simple docstring"""
if isinstance(_UpperCamelCase, np.ndarray ):
return list(tensor.shape )
lowercase : Optional[Any] = tf.shape(_UpperCamelCase )
if tensor.shape == tf.TensorShape(_UpperCamelCase ):
return dynamic
lowercase : Tuple = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_UpperCamelCase )]
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None, _UpperCamelCase = None ) ->tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9, axis=_UpperCamelCase, name=_UpperCamelCase )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=1e-5, _UpperCamelCase=-1 ) ->int:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_UpperCamelCase, _UpperCamelCase ):
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
lowercase , lowercase : Union[str, Any] = tf.nn.moments(_UpperCamelCase, axes=[axis], keepdims=_UpperCamelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase : int = [1] * inputs.shape.rank
lowercase : Union[str, Any] = shape_list(_UpperCamelCase )[axis]
lowercase : List[str] = tf.reshape(_UpperCamelCase, _UpperCamelCase )
lowercase : Dict = tf.reshape(_UpperCamelCase, _UpperCamelCase )
# Compute layer normalization using the batch_normalization
# function.
lowercase : List[str] = tf.nn.batch_normalization(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, offset=_UpperCamelCase, scale=_UpperCamelCase, variance_epsilon=_UpperCamelCase, )
return outputs
def __lowercase ( _UpperCamelCase, _UpperCamelCase=0, _UpperCamelCase=-1 ) ->List[Any]:
"""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
lowercase : Dict = tf.shape(_UpperCamelCase )
lowercase : Optional[Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase : List[str] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]], axis=0 )
return tf.reshape(_UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->tf.Tensor:
"""simple docstring"""
if not isinstance(_UpperCamelCase, tf.Tensor ):
lowercase : Optional[Any] = tf.convert_to_tensor(_UpperCamelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase : Tuple = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase : str = (
tf.cast(1, encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = "input_ids" ) ->None:
"""simple docstring"""
tf.debugging.assert_less(
_UpperCamelCase, tf.cast(_UpperCamelCase, dtype=tensor.dtype ), message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(_UpperCamelCase )}) must be smaller than the embedding """
f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
), )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : List[Any] = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase : Optional[int] = [x for x in data if len(_UpperCamelCase ) > 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}""" )
lowercase : Any = np.asarray(_UpperCamelCase )
lowercase : List[Any] = 1
lowercase : Tuple = np.array_split(_UpperCamelCase, _UpperCamelCase )
# 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
lowercase : Dict = np.array_split(_UpperCamelCase, _UpperCamelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_UpperCamelCase ):
lowercase : Optional[int] = chunk_data
else:
lowercase : int = data
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
if name in group.attrs:
lowercase : str = [n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs[name]]
else:
lowercase : Optional[Any] = []
lowercase : List[str] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
def _expand_single_ad_tensor(_UpperCamelCase ):
if isinstance(_UpperCamelCase, tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_UpperCamelCase, axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor, _UpperCamelCase )
| 337 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
A : int = MODEL_FOR_CAUSAL_LM_MAPPING
A : Tuple = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def __lowerCamelCase ( self ):
lowercase : List[str] = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
lowercase : Tuple = text_generator('''This is a test''' , do_sample=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
lowercase : int = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
lowercase : Optional[Any] = text_generator('''This is a test''' , do_sample=SCREAMING_SNAKE_CASE__ , num_return_sequences=2 , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE__ )},
{'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE__ )},
] , )
lowercase : Any = text_generator.model.config.eos_token_id
lowercase : int = '''<pad>'''
lowercase : Optional[Any] = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=SCREAMING_SNAKE_CASE__ , num_return_sequences=2 , batch_size=2 , return_tensors=SCREAMING_SNAKE_CASE__ , )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
[
{'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE__ )},
{'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE__ )},
],
[
{'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE__ )},
{'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE__ )},
],
] , )
@require_tf
def __lowerCamelCase ( self ):
lowercase : List[Any] = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
lowercase : str = text_generator('''This is a test''' , do_sample=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
lowercase : Optional[int] = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = TextGenerationPipeline(model=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ )
return text_generator, ["This is a test", "Another test"]
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = '''Hello I believe in'''
lowercase : Optional[int] = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
lowercase : Optional[int] = text_generator(SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
lowercase : Tuple = text_generator(SCREAMING_SNAKE_CASE__ , stop_sequence=''' fe''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [{'''generated_text''': '''Hello I believe in fe'''}] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = text_generator.model
lowercase : List[Any] = text_generator.tokenizer
lowercase : Dict = text_generator('''This is a test''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
lowercase : Dict = text_generator('''This is a test''' , return_full_text=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
lowercase : Tuple = pipeline(task='''text-generation''' , model=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ , return_full_text=SCREAMING_SNAKE_CASE__ )
lowercase : Any = text_generator('''This is a test''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
lowercase : Any = text_generator('''This is a test''' , return_full_text=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
lowercase : int = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
[{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}, {'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}],
[{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}, {'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}],
] , )
if text_generator.tokenizer.pad_token is not None:
lowercase : Dict = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
[{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}, {'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}],
[{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}, {'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}],
] , )
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = text_generator('''test''' , return_full_text=SCREAMING_SNAKE_CASE__ , return_text=SCREAMING_SNAKE_CASE__ )
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = text_generator('''test''' , return_full_text=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = text_generator('''test''' , return_text=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
lowercase : Dict = text_generator('''''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [{'''generated_text''': ANY(SCREAMING_SNAKE_CASE__ )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
lowercase : Any = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
lowercase : Tuple = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 10000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
lowercase : Tuple = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def __lowerCamelCase ( self ):
import torch
# Classic `model_kwargs`
lowercase : Union[str, Any] = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
lowercase : List[str] = pipe('''This is a test''' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
lowercase : List[str] = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
lowercase : Tuple = pipe('''This is a test''' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
lowercase : str = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
lowercase : Optional[int] = pipe('''This is a test''' )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def __lowerCamelCase ( self ):
import torch
lowercase : List[str] = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def __lowerCamelCase ( self ):
import torch
lowercase : List[Any] = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=SCREAMING_SNAKE_CASE__ , top_p=0.5 )
def __lowerCamelCase ( self ):
lowercase : Dict = '''Hello world'''
lowercase : Union[str, Any] = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
lowercase : List[Any] = logging.get_logger('''transformers.generation.tf_utils''' )
else:
lowercase : Any = logging.get_logger('''transformers.generation.utils''' )
lowercase : Tuple = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
lowercase : Union[str, Any] = text_generator(SCREAMING_SNAKE_CASE__ , max_length=10 , max_new_tokens=1 )
self.assertIn(SCREAMING_SNAKE_CASE__ , cl.out )
# The user only sets one -> no warning
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
lowercase : Any = text_generator(SCREAMING_SNAKE_CASE__ , max_new_tokens=1 )
self.assertNotIn(SCREAMING_SNAKE_CASE__ , cl.out )
with CaptureLogger(SCREAMING_SNAKE_CASE__ ) as cl:
lowercase : Tuple = text_generator(SCREAMING_SNAKE_CASE__ , max_length=10 )
self.assertNotIn(SCREAMING_SNAKE_CASE__ , cl.out )
| 337 |
def __lowercase ( _UpperCamelCase = 4000000 ) ->int:
"""simple docstring"""
lowercase : int = []
lowercase , lowercase : str = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCamelCase )
lowercase , lowercase : Dict = b, a + b
return sum(_UpperCamelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 | 1 |
import math
def __lowercase ( _UpperCamelCase ) ->bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(_UpperCamelCase ) + 1 ), 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __lowercase ( _UpperCamelCase = 10001 ) ->int:
"""simple docstring"""
try:
lowercase : List[Any] = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('''Parameter nth must be int or castable to int.''' ) from None
if nth <= 0:
raise ValueError('''Parameter nth must be greater than or equal to one.''' )
lowercase : list[int] = []
lowercase : Dict = 2
while len(_UpperCamelCase ) < nth:
if is_prime(_UpperCamelCase ):
primes.append(_UpperCamelCase )
num += 1
else:
num += 1
return primes[len(_UpperCamelCase ) - 1]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
__a = logging.get_logger(__name__)
__a = {
'''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'perceiver'
def __init__( self , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=1280 , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=26 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="kv" , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=262 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=56 , SCREAMING_SNAKE_CASE__=[368, 496] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=1920 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=[1, 16, 224, 224] , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Any = num_latents
lowercase : Union[str, Any] = d_latents
lowercase : str = d_model
lowercase : int = num_blocks
lowercase : str = num_self_attends_per_block
lowercase : List[str] = num_self_attention_heads
lowercase : List[str] = num_cross_attention_heads
lowercase : int = qk_channels
lowercase : List[Any] = v_channels
lowercase : int = cross_attention_shape_for_attention
lowercase : Tuple = self_attention_widening_factor
lowercase : Dict = cross_attention_widening_factor
lowercase : Any = hidden_act
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : Union[str, Any] = initializer_range
lowercase : Any = layer_norm_eps
lowercase : Any = use_query_residual
# masked language modeling attributes
lowercase : List[str] = vocab_size
lowercase : Dict = max_position_embeddings
# image classification attributes
lowercase : int = image_size
# flow attributes
lowercase : List[Any] = train_size
# multimodal autoencoding attributes
lowercase : List[Any] = num_frames
lowercase : Union[str, Any] = audio_samples_per_frame
lowercase : int = samples_per_patch
lowercase : Optional[int] = output_shape
class __SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCamelCase ( self ):
if self.task == "multiple-choice":
lowercase : Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase : Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''inputs''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 3 , SCREAMING_SNAKE_CASE__ = 40 , SCREAMING_SNAKE_CASE__ = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase : str = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase : Union[str, Any] = preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=SCREAMING_SNAKE_CASE__ )
# Generate dummy inputs according to compute batch and sequence
lowercase : Optional[Any] = [''' '''.join(['''a'''] ) * seq_length] * batch_size
lowercase : Any = dict(preprocessor(SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = inputs.pop('''input_ids''' )
return inputs
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase : List[str] = compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_batch )
lowercase : List[str] = self._generate_dummy_images(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = dict(preprocessor(images=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = inputs.pop('''pixel_values''' )
return inputs
else:
raise ValueError(
'''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''' )
| 337 | 1 |
from collections.abc import Generator
from math import sin
def __lowercase ( _UpperCamelCase ) ->bytes:
"""simple docstring"""
if len(_UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
lowercase : Any = B''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def __lowercase ( _UpperCamelCase ) ->bytes:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
lowercase : Any = format(_UpperCamelCase, '''08x''' )[-8:]
lowercase : Optional[int] = B''''''
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8''' )
return little_endian_hex
def __lowercase ( _UpperCamelCase ) ->bytes:
"""simple docstring"""
lowercase : Optional[int] = B''''''
for char in message:
bit_string += format(_UpperCamelCase, '''08b''' ).encode('''utf-8''' )
lowercase : Any = format(len(_UpperCamelCase ), '''064b''' ).encode('''utf-8''' )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(_UpperCamelCase ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def __lowercase ( _UpperCamelCase ) ->Generator[list[int], None, None]:
"""simple docstring"""
if len(_UpperCamelCase ) % 512 != 0:
raise ValueError('''Input must have length that\'s a multiple of 512''' )
for pos in range(0, len(_UpperCamelCase ), 512 ):
lowercase : Union[str, Any] = bit_string[pos : pos + 512]
lowercase : Dict = []
for i in range(0, 512, 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ), 2 ) )
yield block_words
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
lowercase : Dict = format(_UpperCamelCase, '''032b''' )
lowercase : Tuple = ''''''
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(_UpperCamelCase, 2 )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
return (a + b) % 2**32
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
if shift < 0:
raise ValueError('''Shift must be non-negative''' )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def __lowercase ( _UpperCamelCase ) ->bytes:
"""simple docstring"""
lowercase : Optional[int] = preprocess(_UpperCamelCase )
lowercase : List[Any] = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
lowercase : Tuple = 0X67_452_301
lowercase : Tuple = 0Xef_cda_b89
lowercase : Tuple = 0X98_bad_cfe
lowercase : Optional[int] = 0X10_325_476
lowercase : List[Any] = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(_UpperCamelCase ):
lowercase : Tuple = aa
lowercase : List[Any] = ba
lowercase : Dict = ca
lowercase : Dict = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowercase : int = d ^ (b & (c ^ d))
lowercase : Optional[Any] = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowercase : List[str] = c ^ (d & (b ^ c))
lowercase : List[str] = (5 * i + 1) % 16
elif i <= 47:
lowercase : str = b ^ c ^ d
lowercase : Optional[int] = (3 * i + 5) % 16
else:
lowercase : Tuple = c ^ (b | not_aa(_UpperCamelCase ))
lowercase : str = (7 * i) % 16
lowercase : Optional[int] = (f + a + added_consts[i] + block_words[g]) % 2**32
lowercase : Optional[Any] = d
lowercase : Union[str, Any] = c
lowercase : List[str] = b
lowercase : str = sum_aa(_UpperCamelCase, left_rotate_aa(_UpperCamelCase, shift_amounts[i] ) )
# Add hashed chunk to running total
lowercase : List[Any] = sum_aa(_UpperCamelCase, _UpperCamelCase )
lowercase : Tuple = sum_aa(_UpperCamelCase, _UpperCamelCase )
lowercase : str = sum_aa(_UpperCamelCase, _UpperCamelCase )
lowercase : Any = sum_aa(_UpperCamelCase, _UpperCamelCase )
lowercase : List[Any] = reformat_hex(_UpperCamelCase ) + reformat_hex(_UpperCamelCase ) + reformat_hex(_UpperCamelCase ) + reformat_hex(_UpperCamelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowercase ( _UpperCamelCase = 8 ) ->str:
"""simple docstring"""
lowercase : List[str] = ascii_letters + digits + punctuation
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
i -= len(_UpperCamelCase )
lowercase : Dict = i // 3
lowercase : List[str] = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowercase : Union[str, Any] = (
chars_incl
+ random(_UpperCamelCase, quotient + remainder )
+ random(_UpperCamelCase, _UpperCamelCase )
+ random(_UpperCamelCase, _UpperCamelCase )
)
lowercase : Union[str, Any] = list(_UpperCamelCase )
shuffle(_UpperCamelCase )
return "".join(_UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Dict:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[Any]:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase = 8 ) ->bool:
"""simple docstring"""
if len(_UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowercase : str = any(char in ascii_uppercase for char in password )
lowercase : List[str] = any(char in ascii_lowercase for char in password )
lowercase : Dict = any(char in digits for char in password )
lowercase : Tuple = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def __lowercase ( ) ->Dict:
"""simple docstring"""
lowercase : Union[str, Any] = int(input('''Please indicate the max length of your password: ''' ).strip() )
lowercase : Optional[Any] = input(
'''Please indicate the characters that must be in your password: ''' ).strip()
print('''Password generated:''', password_generator(_UpperCamelCase ) )
print(
'''Alternative Password generated:''', alternative_password_generator(_UpperCamelCase, _UpperCamelCase ), )
print('''[If you are thinking of using this passsword, You better save it.]''' )
if __name__ == "__main__":
main()
| 337 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a = 10
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
for i in range(_UpperCamelCase, _UpperCamelCase ):
if array[i] == target:
return i
return -1
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : Optional[Any] = 0
lowercase : List[Any] = len(_UpperCamelCase )
while left <= right:
if right - left < precision:
return lin_search(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
lowercase : Union[str, Any] = (left + right) // 3 + 1
lowercase : Tuple = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
lowercase : Dict = one_third - 1
elif array[two_third] < target:
lowercase : Union[str, Any] = two_third + 1
else:
lowercase : Union[str, Any] = one_third + 1
lowercase : Optional[int] = two_third - 1
else:
return -1
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
if left < right:
if right - left < precision:
return lin_search(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
lowercase : Optional[int] = (left + right) // 3 + 1
lowercase : List[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_UpperCamelCase, one_third - 1, _UpperCamelCase, _UpperCamelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
else:
return rec_ternary_search(one_third + 1, two_third - 1, _UpperCamelCase, _UpperCamelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
__a = input('''Enter numbers separated by comma:\n''').strip()
__a = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
__a = int(input('''Enter the number to be found in the list:\n''').strip())
__a = ite_ternary_search(collection, target)
__a = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print('''Not found''')
| 337 |
from __future__ import annotations
__a = []
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_UpperCamelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, -1, -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, len(_UpperCamelCase ) ) ):
if board[i][j] == 1:
return False
return True
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
if row >= len(_UpperCamelCase ):
solution.append(_UpperCamelCase )
printboard(_UpperCamelCase )
print()
return True
for i in range(len(_UpperCamelCase ) ):
if is_safe(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : int = 1
solve(_UpperCamelCase, row + 1 )
lowercase : Tuple = 0
return False
def __lowercase ( _UpperCamelCase ) ->None:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
for j in range(len(_UpperCamelCase ) ):
if board[i][j] == 1:
print('''Q''', end=''' ''' )
else:
print('''.''', end=''' ''' )
print()
# n=int(input("The no. of queens"))
__a = 8
__a = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 337 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
__a = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''
__a = '''
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results[\'matthews_correlation\'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results[\'matthews_correlation\'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results[\'matthews_correlation\'], 2))
-0.25
'''
__a = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def __lowerCamelCase ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
] , )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
return {
"matthews_correlation": float(matthews_corrcoef(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , sample_weight=SCREAMING_SNAKE_CASE__ ) ),
}
| 337 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CTRLForSequenceClassification''',
'''CTRLLMHeadModel''',
'''CTRLModel''',
'''CTRLPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCTRLForSequenceClassification''',
'''TFCTRLLMHeadModel''',
'''TFCTRLModel''',
'''TFCTRLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase, _UpperCamelCase=False ) ->Tuple:
"""simple docstring"""
lowercase : Any = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('''cls_token''', '''vit.embeddings.cls_token'''),
('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowercase : Any = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=False ) ->Optional[Any]:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
lowercase : Optional[int] = ''''''
else:
lowercase : Optional[Any] = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase : Union[str, Any] = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
lowercase : Any = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase : List[str] = in_proj_weight[
: config.hidden_size, :
]
lowercase : List[str] = in_proj_bias[: config.hidden_size]
lowercase : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase : Union[str, Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase : Any = in_proj_weight[
-config.hidden_size :, :
]
lowercase : Dict = in_proj_bias[-config.hidden_size :]
def __lowercase ( _UpperCamelCase ) ->Any:
"""simple docstring"""
lowercase : Tuple = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(_UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""simple docstring"""
lowercase : Tuple = dct.pop(_UpperCamelCase )
lowercase : List[Any] = val
def __lowercase ( ) ->int:
"""simple docstring"""
lowercase : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase : str = Image.open(requests.get(_UpperCamelCase, stream=_UpperCamelCase ).raw )
return im
@torch.no_grad()
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=True ) ->Any:
"""simple docstring"""
lowercase : Dict = ViTConfig()
# patch_size
if model_name[-1] == "8":
lowercase : Optional[int] = 8
# set labels if required
if not base_model:
lowercase : Union[str, Any] = 1000
lowercase : str = '''huggingface/label-files'''
lowercase : Tuple = '''imagenet-1k-id2label.json'''
lowercase : List[str] = json.load(open(hf_hub_download(_UpperCamelCase, _UpperCamelCase, repo_type='''dataset''' ), '''r''' ) )
lowercase : Tuple = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
lowercase : List[Any] = idalabel
lowercase : Tuple = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
lowercase : List[Any] = 384
lowercase : List[Any] = 1536
lowercase : str = 12
lowercase : Union[str, Any] = 6
# load original model from torch hub
lowercase : List[Any] = torch.hub.load('''facebookresearch/dino:main''', _UpperCamelCase )
original_model.eval()
# load state_dict of original model, remove and rename some keys
lowercase : Dict = original_model.state_dict()
if base_model:
remove_classification_head_(_UpperCamelCase )
lowercase : Optional[int] = create_rename_keys(_UpperCamelCase, base_model=_UpperCamelCase )
for src, dest in rename_keys:
rename_key(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
read_in_q_k_v(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
# load HuggingFace model
if base_model:
lowercase : Optional[Any] = ViTModel(_UpperCamelCase, add_pooling_layer=_UpperCamelCase ).eval()
else:
lowercase : Any = ViTForImageClassification(_UpperCamelCase ).eval()
model.load_state_dict(_UpperCamelCase )
# Check outputs on an image, prepared by ViTImageProcessor
lowercase : List[str] = ViTImageProcessor()
lowercase : Optional[int] = image_processor(images=prepare_img(), return_tensors='''pt''' )
lowercase : Union[str, Any] = encoding['''pixel_values''']
lowercase : int = model(_UpperCamelCase )
if base_model:
lowercase : int = original_model(_UpperCamelCase )
assert torch.allclose(_UpperCamelCase, outputs.last_hidden_state[:, 0, :], atol=1e-1 )
else:
lowercase : Union[str, Any] = original_model(_UpperCamelCase )
assert logits.shape == outputs.logits.shape
assert torch.allclose(_UpperCamelCase, outputs.logits, atol=1e-3 )
Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_UpperCamelCase )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''dino_vitb16''',
type=str,
help='''Name of the model trained with DINO you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--base_model''',
action='''store_true''',
help='''Whether to only convert the base model (no projection head weights).''',
)
parser.set_defaults(base_model=True)
__a = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 337 |
from collections.abc import Callable
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
# Stores actual heap items.
lowercase : list = []
# Stores indexes of each item for supporting updates and deletion.
lowercase : dict = {}
# Stores current size of heap.
lowercase : str = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowercase : Tuple = key or (lambda SCREAMING_SNAKE_CASE__ : x)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return int((i - 1) / 2 ) if i > 0 else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : Dict = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowercase , lowercase : int = self.arr[j], self.arr[i]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.arr[i][1] < self.arr[j][1]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : int = self._left(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = self._right(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = i
if left is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = left
if right is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = right
return valid_parent
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = self._parent(SCREAMING_SNAKE_CASE__ )
while parent is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : Optional[int] = parent, self._parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
while valid_parent != index:
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : str = valid_parent, self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : str = self.pos_map[item]
lowercase : Optional[int] = [item, self.key(SCREAMING_SNAKE_CASE__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : List[str] = self.pos_map[item]
del self.pos_map[item]
lowercase : Optional[int] = self.arr[self.size - 1]
lowercase : int = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(SCREAMING_SNAKE_CASE__ )] )
else:
lowercase : int = [item, self.key(SCREAMING_SNAKE_CASE__ )]
lowercase : str = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowerCamelCase ( self ):
return self.arr[0] if self.size else None
def __lowerCamelCase ( self ):
lowercase : str = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def __lowercase ( ) ->None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
def is_in_circle(_UpperCamelCase, _UpperCamelCase ) -> bool:
lowercase : List[str] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
lowercase : List[str] = mean(
int(is_in_circle(uniform(-1.0, 1.0 ), uniform(-1.0, 1.0 ) ) )
for _ in range(_UpperCamelCase ) )
# The ratio of the area for circle to square is pi/4.
lowercase : str = proportion * 4
print(f"""The estimated value of pi is {pi_estimate}""" )
print(f"""The numpy value of pi is {pi}""" )
print(f"""The total error is {abs(pi - pi_estimate )}""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = 0.0, _UpperCamelCase = 1.0, ) ->float:
"""simple docstring"""
return mean(
function_to_integrate(uniform(_UpperCamelCase, _UpperCamelCase ) ) for _ in range(_UpperCamelCase ) ) * (max_value - min_value)
def __lowercase ( _UpperCamelCase, _UpperCamelCase = 0.0, _UpperCamelCase = 1.0 ) ->None:
"""simple docstring"""
def identity_function(_UpperCamelCase ) -> float:
return x
lowercase : Union[str, Any] = area_under_curve_estimator(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
lowercase : Tuple = (max_value * max_value - min_value * min_value) / 2
print('''******************''' )
print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {expected_value}""" )
print(f"""Total error is {abs(estimated_value - expected_value )}""" )
print('''******************''' )
def __lowercase ( _UpperCamelCase ) ->None:
"""simple docstring"""
def function_to_integrate(_UpperCamelCase ) -> float:
return sqrt(4.0 - x * x )
lowercase : int = area_under_curve_estimator(
_UpperCamelCase, _UpperCamelCase, 0.0, 2.0 )
print('''******************''' )
print('''Estimating pi using area_under_curve_estimator''' )
print(f"""Estimated value is {estimated_value}""" )
print(f"""Expected value is {pi}""" )
print(f"""Total error is {abs(estimated_value - pi )}""" )
print('''******************''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[List[np.ndarray], torch.FloatTensor]
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 .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 337 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class __SCREAMING_SNAKE_CASE :
def __init__( self ):
lowercase : int = {}
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Union[str, Any] = {}
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if nodea not in self.connections:
self.add_node(SCREAMING_SNAKE_CASE__ )
if nodea not in self.connections:
self.add_node(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = probability
def __lowerCamelCase ( self ):
return list(self.connections )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = 0
lowercase : Optional[int] = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->dict[str, int]:
"""simple docstring"""
lowercase : Any = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
lowercase : Any = Counter(graph.get_nodes() )
lowercase : List[str] = start
for _ in range(_UpperCamelCase ):
lowercase : Optional[Any] = graph.transition(_UpperCamelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__a = logging.get_logger(__name__)
__a = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__a = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__a = {'''facebook/blenderbot-3B''': 1_28}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Dict = VOCAB_FILES_NAMES
A : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Optional[int] = ['input_ids', 'attention_mask']
A : str = BlenderbotTokenizer
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="replace" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , tokenizer_file=SCREAMING_SNAKE_CASE__ , errors=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ , trim_offsets=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
lowercase : str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : List[Any] = getattr(SCREAMING_SNAKE_CASE__ , pre_tok_state.pop('''type''' ) )
lowercase : str = add_prefix_space
lowercase : List[Any] = pre_tok_class(**SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = add_prefix_space
lowercase : str = '''post_processor'''
lowercase : str = getattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if tokenizer_component_instance:
lowercase : Optional[int] = 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:
lowercase : Tuple = tuple(state['''sep'''] )
if "cls" in state:
lowercase : Union[str, Any] = tuple(state['''cls'''] )
lowercase : Optional[int] = False
if state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : Any = add_prefix_space
lowercase : Tuple = True
if state.get('''trim_offsets''' , SCREAMING_SNAKE_CASE__ ) != trim_offsets:
lowercase : List[str] = trim_offsets
lowercase : Optional[int] = True
if changes_to_apply:
lowercase : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE__ , state.pop('''type''' ) )
lowercase : Union[str, Any] = component_class(**SCREAMING_SNAKE_CASE__ )
setattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def __lowerCamelCase ( self ):
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else value
lowercase : Any = value
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
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(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Any = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
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(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : int = self._tokenizer.model.save(SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
return tuple(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Tuple = [self.sep_token_id]
lowercase : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
return token_ids_a + [self.eos_token_id]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = ''' '''.join(SCREAMING_SNAKE_CASE__ )
lowercase : Any = self.encode(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > self.model_max_length:
lowercase : Tuple = input_ids[-self.model_max_length :]
logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 337 | 1 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__a = logging.get_logger(__name__)
__a = '''T5Config'''
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Optional[Any] = 'mt5'
A : str = MTaConfig
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Optional[int] = 'mt5'
A : str = MTaConfig
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Tuple = 'mt5'
A : Dict = MTaConfig
| 337 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ) ->int:
"""simple docstring"""
lowercase : Tuple = HfArgumentParser(_UpperCamelCase )
lowercase : List[str] = parser.parse_args_into_dataclasses()[0]
lowercase : Optional[int] = TensorFlowBenchmark(args=_UpperCamelCase )
try:
lowercase : Any = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase : Optional[int] = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
lowercase : Any = ''' '''.join(str(_UpperCamelCase ).split(''' ''' )[:-1] )
lowercase : Any = ''''''
lowercase : str = eval(str(_UpperCamelCase ).split(''' ''' )[-1] )
lowercase : List[str] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
lowercase : Union[str, Any] = full_error_msg + begin_error_msg + str(_UpperCamelCase )
raise ValueError(_UpperCamelCase )
benchmark.run()
if __name__ == "__main__":
main()
| 337 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__a = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
datarow=DataRow('''''', '''|''', '''|'''),
padding=1,
with_header_hide=None,
)
__a = []
__a = []
__a = {'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}
__a = [
{
'''type''': '''header''',
'''text''': {
'''type''': '''plain_text''',
'''text''': F'''🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results''',
'''emoji''': True,
},
}
]
__a = 0
for log in Path().glob('''*.log'''):
__a = 0
with open(log, '''r''') as f:
for line in f:
__a = json.loads(line)
if line.get('''nodeid''', '''''') != "":
__a = line['''nodeid''']
if line.get('''duration''', None) is not None:
__a = F'''{line["duration"]:.4f}'''
if line.get('''outcome''', '''''') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('''_''')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
__a = []
log.unlink()
__a = ''''''
__a = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
__a = []
__a = {}
for test in failed_tests:
__a = test[0].split('''::''')
__a = data[0].split('''/''')[-1]
if data[0] not in filesafailed:
__a = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
__a = [test[0] for test in failed_table]
__a = list(set(files))
# Count number of instances in failed_tests
__a = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
__a = tabulate(
table,
headers=['''Test Location''', '''Num Failed'''],
tablefmt=hf_table_format,
stralign='''right''',
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 30_00:
__a = '''Too many failed tests, please see the full report in the Action results.'''
__a = len(err) + 10
__a = message[: 30_00 - offset] + F'''\n...\n```\n{err}'''
print(F'''### {message}''')
else:
__a = '''No failed tests! 🤗'''
print(F'''## {message}''')
payload.append(no_error_payload)
if os.environ.get('''TEST_TYPE''', '''''') != "":
from slack_sdk import WebClient
__a = WebClient(token=os.environ['''SLACK_API_TOKEN'''])
if message != "No failed tests! 🤗":
__a = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': message,
},
}
payload.append(md_report)
__a = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': '''*For more details:*''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''Check Action results''',
'''emoji''': True,
},
'''url''': F'''https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
},
}
payload.append(action_button)
__a = {
'''type''': '''context''',
'''elements''': [
{
'''type''': '''plain_text''',
'''text''': F'''Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}''',
}
],
}
payload.append(date_report)
__a = client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
__a = response.data['''ts''']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
__a = ''''''
for i, row in enumerate(test_failures):
if row[0] != test_class:
__a = row[0]
else:
__a = ''''''
__a = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```''',
},
}
client.chat_postMessage(
channel='''#accelerate-ci-daily''',
thread_ts=ts,
blocks=[payload],
)
| 337 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : Union[str, Any] = [False] * len(_UpperCamelCase )
lowercase : Optional[int] = []
queue.append(_UpperCamelCase )
lowercase : Union[str, Any] = True
while queue:
lowercase : List[str] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_UpperCamelCase )
lowercase : Tuple = True
lowercase : Optional[Any] = u
return visited[t]
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : List[str] = [-1] * (len(_UpperCamelCase ))
lowercase : int = 0
while bfs(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : List[str] = float('''Inf''' )
lowercase : int = sink
while s != source:
# Find the minimum value in select path
lowercase : List[Any] = min(_UpperCamelCase, graph[parent[s]][s] )
lowercase : Union[str, Any] = parent[s]
max_flow += path_flow
lowercase : Optional[int] = sink
while v != source:
lowercase : Any = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowercase : Union[str, Any] = parent[v]
return max_flow
__a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__a , __a = 0, 5
print(ford_fulkerson(graph, source, sink))
| 337 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def __lowercase ( _UpperCamelCase ) ->Tuple:
"""simple docstring"""
lowercase : List[str] = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class __SCREAMING_SNAKE_CASE ( A__ , A__ , A__ , unittest.TestCase ):
A : int = StableDiffusionLatentUpscalePipeline
A : List[str] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'height',
'width',
'cross_attention_kwargs',
'negative_prompt_embeds',
'prompt_embeds',
}
A : int = PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
A : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
A : List[str] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A : Union[str, Any] = frozenset([] )
A : List[str] = True
@property
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = 1
lowercase : List[str] = 4
lowercase : List[Any] = (16, 16)
lowercase : Union[str, Any] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
return image
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
lowercase : Dict = UNetaDConditionModel(
act_fn='''gelu''' , attention_head_dim=8 , norm_num_groups=SCREAMING_SNAKE_CASE__ , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
'''KDownBlock2D''',
'''KCrossAttnDownBlock2D''',
'''KCrossAttnDownBlock2D''',
'''KCrossAttnDownBlock2D''',
) , in_channels=8 , mid_block_type=SCREAMING_SNAKE_CASE__ , only_cross_attention=SCREAMING_SNAKE_CASE__ , out_channels=5 , resnet_time_scale_shift='''scale_shift''' , time_embedding_type='''fourier''' , timestep_post_act='''gelu''' , up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''') , )
lowercase : List[str] = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
'''DownEncoderBlock2D''',
'''DownEncoderBlock2D''',
'''DownEncoderBlock2D''',
'''DownEncoderBlock2D''',
] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
lowercase : List[Any] = EulerDiscreteScheduler(prediction_type='''sample''' )
lowercase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''quick_gelu''' , projection_dim=512 , )
lowercase : List[str] = CLIPTextModel(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowercase : Any = {
'''unet''': model.eval(),
'''vae''': vae.eval(),
'''scheduler''': scheduler,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ):
if str(SCREAMING_SNAKE_CASE__ ).startswith('''mps''' ):
lowercase : Dict = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
lowercase : str = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': self.dummy_image.cpu(),
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def __lowerCamelCase ( self ):
lowercase : Any = '''cpu'''
lowercase : Any = self.get_dummy_components()
lowercase : Tuple = self.pipeline_class(**SCREAMING_SNAKE_CASE__ )
pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
lowercase : Dict = pipe(**SCREAMING_SNAKE_CASE__ ).images
lowercase : List[str] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
lowercase : Tuple = np.array(
[0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055] )
lowercase : Optional[int] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE__ , 1E-3 )
def __lowerCamelCase ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )
def __lowerCamelCase ( self ):
super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )
def __lowerCamelCase ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def __lowerCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=7E-3 )
def __lowerCamelCase ( self ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )
def __lowerCamelCase ( self ):
super().test_save_load_local(expected_max_difference=3E-3 )
def __lowerCamelCase ( self ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def __lowerCamelCase ( self ):
lowercase : List[str] = [
'''DDIMScheduler''',
'''DDPMScheduler''',
'''PNDMScheduler''',
'''HeunDiscreteScheduler''',
'''EulerAncestralDiscreteScheduler''',
'''KDPM2DiscreteScheduler''',
'''KDPM2AncestralDiscreteScheduler''',
'''DPMSolverSDEScheduler''',
]
lowercase : Dict = self.get_dummy_components()
lowercase : Optional[Any] = self.pipeline_class(**SCREAMING_SNAKE_CASE__ )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=SCREAMING_SNAKE_CASE__ )
pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
lowercase : Any = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
lowercase : int = 2
lowercase : int = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
lowercase : Any = getattr(SCREAMING_SNAKE_CASE__ , scheduler_enum.name )
lowercase : Any = scheduler_cls.from_config(pipe.scheduler.config )
lowercase : Optional[Any] = pipe(**SCREAMING_SNAKE_CASE__ )[0]
outputs.append(SCREAMING_SNAKE_CASE__ )
assert check_same_shape(SCREAMING_SNAKE_CASE__ )
@require_torch_gpu
@slow
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCamelCase ( self ):
lowercase : Any = torch.manual_seed(33 )
lowercase : str = StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' , torch_dtype=torch.floataa )
pipe.to('''cuda''' )
lowercase : Any = StableDiffusionLatentUpscalePipeline.from_pretrained(
'''stabilityai/sd-x2-latent-upscaler''' , torch_dtype=torch.floataa )
upscaler.to('''cuda''' )
lowercase : List[Any] = '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''
lowercase : Dict = pipe(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , output_type='''latent''' ).images
lowercase : List[Any] = upscaler(
prompt=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , num_inference_steps=20 , guidance_scale=0 , generator=SCREAMING_SNAKE_CASE__ , output_type='''np''' , ).images[0]
lowercase : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy''' )
assert np.abs((expected_image - image).mean() ) < 5E-2
def __lowerCamelCase ( self ):
lowercase : int = torch.manual_seed(33 )
lowercase : str = StableDiffusionLatentUpscalePipeline.from_pretrained(
'''stabilityai/sd-x2-latent-upscaler''' , torch_dtype=torch.floataa )
upscaler.to('''cuda''' )
lowercase : Optional[int] = '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''
lowercase : int = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png''' )
lowercase : str = upscaler(
prompt=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , num_inference_steps=20 , guidance_scale=0 , generator=SCREAMING_SNAKE_CASE__ , output_type='''np''' , ).images[0]
lowercase : int = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy''' )
assert np.abs((expected_image - image).max() ) < 5E-2
| 337 |
from typing import List
from .keymap import KEYMAP, get_character
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : str = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += [key]
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
def __lowercase ( *_UpperCamelCase ) ->Any:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : List[Any] = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += keys
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
class __SCREAMING_SNAKE_CASE ( A__ ):
def __new__( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = super().__new__(cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not hasattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' ):
setattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' , {} )
setattr(SCREAMING_SNAKE_CASE__ , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
lowercase : Dict = getattr(SCREAMING_SNAKE_CASE__ , '''handle_key''' , [] )
for key in handled_keys:
lowercase : List[Any] = value
return new_cls
@staticmethod
def __lowerCamelCase ( cls ):
lowercase : Dict = get_character()
if char != KEYMAP["undefined"]:
lowercase : Optional[int] = ord(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = cls.key_handler.get(SCREAMING_SNAKE_CASE__ )
if handler:
lowercase : Tuple = char
return handler(cls )
else:
return None
def __lowercase ( cls ) ->Any:
"""simple docstring"""
return KeyHandler(cls.__name__, cls.__bases__, cls.__dict__.copy() )
| 337 | 1 |
from __future__ import annotations
import math
def __lowercase ( _UpperCamelCase ) ->bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(_UpperCamelCase ) + 1 ), 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
__a = [num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def __lowercase ( _UpperCamelCase ) ->list[int]:
"""simple docstring"""
if not isinstance(_UpperCamelCase, _UpperCamelCase ):
raise ValueError('''n must be an integer''' )
if n <= 0:
raise ValueError('''n must be >= 0''' )
lowercase : str = []
for num in range(len(_UpperCamelCase ) ):
lowercase : Optional[Any] = 0
while 2 * i * i <= odd_composites[num]:
lowercase : List[str] = odd_composites[num] - 2 * i * i
if is_prime(_UpperCamelCase ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(_UpperCamelCase ) == n:
return list_nums
return []
def __lowercase ( ) ->int:
"""simple docstring"""
return compute_nums(1 )[0]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 |
import logging
import os
from .state import PartialState
class __SCREAMING_SNAKE_CASE ( logging.LoggerAdapter ):
@staticmethod
def __lowerCamelCase ( SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
lowercase : List[str] = kwargs.pop('''main_process_only''' , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = kwargs.pop('''in_order''' , SCREAMING_SNAKE_CASE__ )
if self.isEnabledFor(SCREAMING_SNAKE_CASE__ ):
if self._should_log(SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : str = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
elif in_order:
lowercase : List[Any] = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
lowercase , lowercase : Union[str, Any] = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
state.wait_for_everyone()
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->List[Any]:
"""simple docstring"""
if log_level is None:
lowercase : str = os.environ.get('''ACCELERATE_LOG_LEVEL''', _UpperCamelCase )
lowercase : str = logging.getLogger(_UpperCamelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_UpperCamelCase, {} )
| 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['''RemBertTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['''RemBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RemBertForCausalLM''',
'''RemBertForMaskedLM''',
'''RemBertForMultipleChoice''',
'''RemBertForQuestionAnswering''',
'''RemBertForSequenceClassification''',
'''RemBertForTokenClassification''',
'''RemBertLayer''',
'''RemBertModel''',
'''RemBertPreTrainedModel''',
'''load_tf_weights_in_rembert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRemBertForCausalLM''',
'''TFRemBertForMaskedLM''',
'''TFRemBertForMultipleChoice''',
'''TFRemBertForQuestionAnswering''',
'''TFRemBertForSequenceClassification''',
'''TFRemBertForTokenClassification''',
'''TFRemBertLayer''',
'''TFRemBertModel''',
'''TFRemBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super().__init__()
lowercase : Any = model
lowercase : Optional[Any] = 2
lowercase : Optional[int] = nn.Linear(self.model.config.hidden_size , self.num_labels )
def __lowerCamelCase ( self ):
pass
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : str = LongformerModel.from_pretrained(_UpperCamelCase )
lowercase : int = LightningModel(_UpperCamelCase )
lowercase : Union[str, Any] = torch.load(_UpperCamelCase, map_location=torch.device('''cpu''' ) )
lightning_model.load_state_dict(ckpt['''state_dict'''] )
# init longformer question answering model
lowercase : List[Any] = LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_UpperCamelCase )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['''ReformerTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['''ReformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ReformerAttention''',
'''ReformerForMaskedLM''',
'''ReformerForQuestionAnswering''',
'''ReformerForSequenceClassification''',
'''ReformerLayer''',
'''ReformerModel''',
'''ReformerModelWithLMHead''',
'''ReformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 |
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
__a = logging.get_logger(__name__)
__a = {
'''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Any = 'yolos'
def __init__( self , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=3072 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=[512, 864] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=100 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = hidden_size
lowercase : int = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : str = intermediate_size
lowercase : Dict = hidden_act
lowercase : int = hidden_dropout_prob
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : List[Any] = initializer_range
lowercase : Optional[int] = layer_norm_eps
lowercase : str = image_size
lowercase : Dict = patch_size
lowercase : str = num_channels
lowercase : Optional[int] = qkv_bias
lowercase : List[str] = num_detection_tokens
lowercase : List[str] = use_mid_position_embeddings
lowercase : Dict = auxiliary_loss
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : Any = bbox_cost
lowercase : int = giou_cost
# Loss coefficients
lowercase : Dict = bbox_loss_coefficient
lowercase : Optional[Any] = giou_loss_coefficient
lowercase : Tuple = eos_coefficient
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = version.parse('1.11' )
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
@property
def __lowerCamelCase ( self ):
return 12
| 337 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__a = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', '''weight'''),
('''beta''', '''bias'''),
('''gamma''', '''weight'''),
('''pegasus''', '''model'''),
]
__a = [
('''.output.dense''', '''.fc2'''),
('''intermediate.LayerNorm''', '''final_layer_norm'''),
('''intermediate.dense''', '''fc1'''),
]
__a = (
INIT_COMMON
+ [
('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
('''attention.output.dense''', '''self_attn.out_proj'''),
('''attention.self''', '''self_attn'''),
('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
('''attention.encdec''', '''encoder_attn'''),
('''key''', '''k_proj'''),
('''value''', '''v_proj'''),
('''query''', '''q_proj'''),
('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
]
+ END_COMMON
)
__a = (
INIT_COMMON
+ [
('''embeddings.word_embeddings''', '''shared.weight'''),
('''embeddings.position_embeddings''', '''embed_positions.weight'''),
('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
('''attention.output.dense''', '''self_attn.output'''),
('''attention.self''', '''self_attn.self'''),
('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
]
+ END_COMMON
)
__a = [
'''encdec/key/bias''',
'''encdec/query/bias''',
'''encdec/value/bias''',
'''self/key/bias''',
'''self/query/bias''',
'''self/value/bias''',
'''encdec_output/dense/bias''',
'''attention/output/dense/bias''',
]
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
for tf_name, hf_name in patterns:
lowercase : Tuple = k.replace(_UpperCamelCase, _UpperCamelCase )
return k
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->BigBirdPegasusForConditionalGeneration:
"""simple docstring"""
lowercase : List[Any] = BigBirdPegasusConfig(**_UpperCamelCase )
lowercase : Optional[Any] = BigBirdPegasusForConditionalGeneration(_UpperCamelCase )
lowercase : List[Any] = torch_model.state_dict()
lowercase : List[Any] = {}
# separating decoder weights
lowercase : List[str] = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
lowercase : List[Any] = {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items(), '''tf -> hf conversion''' ):
lowercase : Optional[Any] = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
lowercase : List[Any] = DECODER_PATTERNS
lowercase : int = rename_state_dict_key(_UpperCamelCase, _UpperCamelCase )
if new_k not in state_dict:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
lowercase : List[str] = v.T
lowercase : List[Any] = torch.from_numpy(_UpperCamelCase )
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items(), '''tf -> hf conversion''' ):
lowercase : Union[str, Any] = [k.endswith(_UpperCamelCase ) for ending in KEYS_TO_IGNORE]
if any(_UpperCamelCase ):
continue
lowercase : List[str] = REMAINING_PATTERNS
lowercase : Dict = rename_state_dict_key(_UpperCamelCase, _UpperCamelCase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
lowercase : Dict = v.T
lowercase : str = torch.from_numpy(_UpperCamelCase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
lowercase : List[str] = mapping['''model.embed_positions.weight''']
lowercase : Tuple = mapping.pop('''model.embed_positions.weight''' )
lowercase , lowercase : Optional[Any] = torch_model.load_state_dict(_UpperCamelCase, strict=_UpperCamelCase )
lowercase : List[Any] = [
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def __lowercase ( _UpperCamelCase ) ->Dict:
"""simple docstring"""
lowercase : Optional[Any] = tf.train.list_variables(_UpperCamelCase )
lowercase : Any = {}
lowercase : Optional[int] = ['''global_step''']
for name, shape in tqdm(_UpperCamelCase, desc='''converting tf checkpoint to dict''' ):
lowercase : List[str] = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowercase : List[Any] = tf.train.load_variable(_UpperCamelCase, _UpperCamelCase )
lowercase : Dict = array
return tf_weights
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Any:
"""simple docstring"""
lowercase : Optional[Any] = get_tf_weights_as_numpy(_UpperCamelCase )
lowercase : Tuple = convert_bigbird_pegasus(_UpperCamelCase, _UpperCamelCase )
torch_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
__a = parser.parse_args()
__a = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 337 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__a = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""simple docstring"""
if got_ver is None or want_ver is None:
raise ValueError(
f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
f""" reinstalling {pkg}.""" )
if not ops[op](version.parse(_UpperCamelCase ), version.parse(_UpperCamelCase ) ):
raise ImportError(
f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->None:
"""simple docstring"""
lowercase : List[Any] = f"""\n{hint}""" if hint is not None else ''''''
# non-versioned check
if re.match(R'''^[\w_\-\d]+$''', _UpperCamelCase ):
lowercase , lowercase , lowercase : Optional[Any] = requirement, None, None
else:
lowercase : List[Any] = re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
f""" got {requirement}""" )
lowercase , lowercase : str = match[0]
lowercase : Tuple = want_full.split(''',''' ) # there could be multiple requirements
lowercase : List[Any] = {}
for w in want_range:
lowercase : str = re.findall(R'''^([\s!=<>]{1,2})(.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
f""" but got {requirement}""" )
lowercase , lowercase : Optional[int] = match[0]
lowercase : Dict = want_ver
if op not in ops:
raise ValueError(f"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )
# special case
if pkg == "python":
lowercase : int = '''.'''.join([str(_UpperCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
return
# check if any version is installed
try:
lowercase : List[str] = importlib.metadata.version(_UpperCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
f"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : Optional[int] = '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
return require_version(_UpperCamelCase, _UpperCamelCase )
| 337 | 1 |
from __future__ import annotations
import math
import random
from typing import Any
class __SCREAMING_SNAKE_CASE :
def __init__( self ):
lowercase : list[Any] = []
lowercase : int = 0
lowercase : int = 0
def __lowerCamelCase ( self ):
return self.head == self.tail
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
self.data.append(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = self.tail + 1
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.data[self.head]
lowercase : Optional[int] = self.head + 1
return ret
def __lowerCamelCase ( self ):
return self.tail - self.head
def __lowerCamelCase ( self ):
print(self.data )
print('''**************''' )
print(self.data[self.head : self.tail] )
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = data
lowercase : MyNode | None = None
lowercase : MyNode | None = None
lowercase : int = 1
def __lowerCamelCase ( self ):
return self.data
def __lowerCamelCase ( self ):
return self.left
def __lowerCamelCase ( self ):
return self.right
def __lowerCamelCase ( self ):
return self.height
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = data
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Union[str, Any] = node
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = node
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = height
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
if node is None:
return 0
return node.get_height()
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
if a > b:
return a
return b
def __lowercase ( _UpperCamelCase ) ->MyNode:
"""simple docstring"""
print('''left rotation node:''', node.get_data() )
lowercase : List[str] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(_UpperCamelCase )
lowercase : Union[str, Any] = my_max(get_height(node.get_right() ), get_height(node.get_left() ) ) + 1
node.set_height(_UpperCamelCase )
lowercase : Optional[int] = my_max(get_height(ret.get_right() ), get_height(ret.get_left() ) ) + 1
ret.set_height(_UpperCamelCase )
return ret
def __lowercase ( _UpperCamelCase ) ->MyNode:
"""simple docstring"""
print('''right rotation node:''', node.get_data() )
lowercase : Union[str, Any] = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(_UpperCamelCase )
lowercase : Union[str, Any] = my_max(get_height(node.get_right() ), get_height(node.get_left() ) ) + 1
node.set_height(_UpperCamelCase )
lowercase : Dict = my_max(get_height(ret.get_right() ), get_height(ret.get_left() ) ) + 1
ret.set_height(_UpperCamelCase )
return ret
def __lowercase ( _UpperCamelCase ) ->MyNode:
"""simple docstring"""
lowercase : Any = node.get_left()
assert left_child is not None
node.set_left(left_rotation(_UpperCamelCase ) )
return right_rotation(_UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->MyNode:
"""simple docstring"""
lowercase : List[Any] = node.get_right()
assert right_child is not None
node.set_right(right_rotation(_UpperCamelCase ) )
return left_rotation(_UpperCamelCase )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->MyNode | None:
"""simple docstring"""
if node is None:
return MyNode(_UpperCamelCase )
if data < node.get_data():
node.set_left(insert_node(node.get_left(), _UpperCamelCase ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
lowercase : Optional[Any] = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
lowercase : Tuple = right_rotation(_UpperCamelCase )
else:
lowercase : Optional[Any] = lr_rotation(_UpperCamelCase )
else:
node.set_right(insert_node(node.get_right(), _UpperCamelCase ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
lowercase : str = node.get_right()
assert right_child is not None
if data < right_child.get_data():
lowercase : Tuple = rl_rotation(_UpperCamelCase )
else:
lowercase : Optional[int] = left_rotation(_UpperCamelCase )
lowercase : Optional[Any] = my_max(get_height(node.get_right() ), get_height(node.get_left() ) ) + 1
node.set_height(_UpperCamelCase )
return node
def __lowercase ( _UpperCamelCase ) ->Any:
"""simple docstring"""
while True:
lowercase : Union[str, Any] = root.get_right()
if right_child is None:
break
lowercase : str = right_child
return root.get_data()
def __lowercase ( _UpperCamelCase ) ->Any:
"""simple docstring"""
while True:
lowercase : str = root.get_left()
if left_child is None:
break
lowercase : Optional[Any] = left_child
return root.get_data()
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->MyNode | None:
"""simple docstring"""
lowercase : str = root.get_left()
lowercase : str = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
lowercase : Union[str, Any] = get_left_most(_UpperCamelCase )
root.set_data(_UpperCamelCase )
root.set_right(del_node(_UpperCamelCase, _UpperCamelCase ) )
elif left_child is not None:
lowercase : int = left_child
elif right_child is not None:
lowercase : Tuple = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('''No such data''' )
return root
else:
root.set_left(del_node(_UpperCamelCase, _UpperCamelCase ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(_UpperCamelCase, _UpperCamelCase ) )
if get_height(_UpperCamelCase ) - get_height(_UpperCamelCase ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
lowercase : List[str] = left_rotation(_UpperCamelCase )
else:
lowercase : int = rl_rotation(_UpperCamelCase )
elif get_height(_UpperCamelCase ) - get_height(_UpperCamelCase ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
lowercase : List[Any] = right_rotation(_UpperCamelCase )
else:
lowercase : List[Any] = lr_rotation(_UpperCamelCase )
lowercase : Any = my_max(get_height(root.get_right() ), get_height(root.get_left() ) ) + 1
root.set_height(_UpperCamelCase )
return root
class __SCREAMING_SNAKE_CASE :
def __init__( self ):
lowercase : MyNode | None = None
def __lowerCamelCase ( self ):
return get_height(self.root )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
print('''insert:''' + str(SCREAMING_SNAKE_CASE__ ) )
lowercase : Optional[Any] = insert_node(self.root , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
print('''delete:''' + str(SCREAMING_SNAKE_CASE__ ) )
if self.root is None:
print('''Tree is empty!''' )
return
lowercase : Dict = del_node(self.root , SCREAMING_SNAKE_CASE__ )
def __str__( self , ): # a level traversale, gives a more intuitive look on the tree
lowercase : List[Any] = ''''''
lowercase : Optional[Any] = MyQueue()
q.push(self.root )
lowercase : int = self.get_height()
if layer == 0:
return output
lowercase : List[str] = 0
while not q.is_empty():
lowercase : Tuple = q.pop()
lowercase : Optional[int] = ''' ''' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(SCREAMING_SNAKE_CASE__ )
q.push(SCREAMING_SNAKE_CASE__ )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
lowercase : Tuple = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , SCREAMING_SNAKE_CASE__ ) - 1:
lowercase : int = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def __lowercase ( ) ->None:
"""simple docstring"""
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
__a = AVLtree()
__a = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 337 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'deta'
A : Dict = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=900 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=1024 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="relu" , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="sine" , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=300 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.25 , **SCREAMING_SNAKE_CASE__ , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase : Tuple = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = backbone_config.pop('''model_type''' )
lowercase : Any = CONFIG_MAPPING[backbone_model_type]
lowercase : List[Any] = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = backbone_config
lowercase : Union[str, Any] = num_queries
lowercase : Any = max_position_embeddings
lowercase : int = d_model
lowercase : Any = encoder_ffn_dim
lowercase : Optional[int] = encoder_layers
lowercase : Tuple = encoder_attention_heads
lowercase : Optional[Any] = decoder_ffn_dim
lowercase : Optional[int] = decoder_layers
lowercase : int = decoder_attention_heads
lowercase : Any = dropout
lowercase : int = attention_dropout
lowercase : Dict = activation_dropout
lowercase : int = activation_function
lowercase : Dict = init_std
lowercase : List[str] = init_xavier_std
lowercase : Optional[Any] = encoder_layerdrop
lowercase : Tuple = auxiliary_loss
lowercase : Tuple = position_embedding_type
# deformable attributes
lowercase : List[str] = num_feature_levels
lowercase : Tuple = encoder_n_points
lowercase : Optional[int] = decoder_n_points
lowercase : Tuple = two_stage
lowercase : Optional[Any] = two_stage_num_proposals
lowercase : Union[str, Any] = with_box_refine
lowercase : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : str = bbox_cost
lowercase : List[Any] = giou_cost
# Loss coefficients
lowercase : Tuple = mask_loss_coefficient
lowercase : Any = dice_loss_coefficient
lowercase : Dict = bbox_loss_coefficient
lowercase : Tuple = giou_loss_coefficient
lowercase : Union[str, Any] = eos_coefficient
lowercase : Tuple = focal_alpha
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __lowerCamelCase ( self ):
return self.encoder_attention_heads
@property
def __lowerCamelCase ( self ):
return self.d_model
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase : Any = self.backbone_config.to_dict()
lowercase : List[str] = self.__class__.model_type
return output
| 337 | 1 |
from torch import nn
def __lowercase ( _UpperCamelCase ) ->Any:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f"""Unsupported activation function: {act_fn}""" )
| 337 |
def __lowercase ( ) ->List[Any]:
"""simple docstring"""
lowercase : Union[str, Any] = 0
for i in range(1, 1001 ):
total += i**i
return str(_UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 337 | 1 |
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 = logging.getLogger(__name__)
@dataclass
class __SCREAMING_SNAKE_CASE :
A : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
A : Optional[str] = field(
default=A__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
A : Optional[str] = field(
default='NER' , metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
A : Optional[str] = field(
default=A__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
A : bool = field(default=A__ , metadata={'help': 'Set this flag to use fast tokenization.'} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
A : Optional[str] = field(
default=A__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
A : str = field(
metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
A : Optional[str] = field(
default=A__ , metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'} , )
A : 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.'
)
} , )
A : bool = field(
default=A__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def __lowercase ( ) ->str:
"""simple docstring"""
lowercase : Any = 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.
lowercase , lowercase , lowercase : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase : Union[str, 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.''' )
lowercase : List[Any] = import_module('''tasks''' )
try:
lowercase : Tuple = getattr(_UpperCamelCase, model_args.task_type )
lowercase : 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''', _UpperCamelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowercase : Optional[Any] = token_classification_task.get_labels(data_args.labels )
lowercase : Dict[int, str] = dict(enumerate(_UpperCamelCase ) )
lowercase : int = len(_UpperCamelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase : Union[str, Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=_UpperCamelCase, idalabel=_UpperCamelCase, labelaid={label: i for i, label in enumerate(_UpperCamelCase )}, cache_dir=model_args.cache_dir, )
lowercase : 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, )
lowercase : int = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path ), config=_UpperCamelCase, cache_dir=model_args.cache_dir, )
# Get datasets
lowercase : List[Any] = (
TokenClassificationDataset(
token_classification_task=_UpperCamelCase, data_dir=data_args.data_dir, tokenizer=_UpperCamelCase, labels=_UpperCamelCase, 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
)
lowercase : Optional[Any] = (
TokenClassificationDataset(
token_classification_task=_UpperCamelCase, data_dir=data_args.data_dir, tokenizer=_UpperCamelCase, labels=_UpperCamelCase, 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(_UpperCamelCase, _UpperCamelCase ) -> Tuple[List[int], List[int]]:
lowercase : List[Any] = np.argmax(_UpperCamelCase, axis=2 )
lowercase , lowercase : Union[str, Any] = preds.shape
lowercase : Optional[Any] = [[] for _ in range(_UpperCamelCase )]
lowercase : List[str] = [[] for _ in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
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(_UpperCamelCase ) -> Dict:
lowercase , lowercase : Union[str, Any] = align_predictions(p.predictions, p.label_ids )
return {
"accuracy_score": accuracy_score(_UpperCamelCase, _UpperCamelCase ),
"precision": precision_score(_UpperCamelCase, _UpperCamelCase ),
"recall": recall_score(_UpperCamelCase, _UpperCamelCase ),
"f1": fa_score(_UpperCamelCase, _UpperCamelCase ),
}
# Data collator
lowercase : List[str] = DataCollatorWithPadding(_UpperCamelCase, pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase : Dict = Trainer(
model=_UpperCamelCase, args=_UpperCamelCase, train_dataset=_UpperCamelCase, eval_dataset=_UpperCamelCase, compute_metrics=_UpperCamelCase, data_collator=_UpperCamelCase, )
# 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
lowercase : Tuple = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase : Dict = trainer.evaluate()
lowercase : Optional[int] = os.path.join(training_args.output_dir, '''eval_results.txt''' )
if trainer.is_world_process_zero():
with open(_UpperCamelCase, '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''', _UpperCamelCase, _UpperCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
results.update(_UpperCamelCase )
# Predict
if training_args.do_predict:
lowercase : Tuple = TokenClassificationDataset(
token_classification_task=_UpperCamelCase, data_dir=data_args.data_dir, tokenizer=_UpperCamelCase, labels=_UpperCamelCase, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.test, )
lowercase , lowercase , lowercase : Dict = trainer.predict(_UpperCamelCase )
lowercase , lowercase : List[str] = align_predictions(_UpperCamelCase, _UpperCamelCase )
lowercase : List[Any] = os.path.join(training_args.output_dir, '''test_results.txt''' )
if trainer.is_world_process_zero():
with open(_UpperCamelCase, '''w''' ) as writer:
for key, value in metrics.items():
logger.info(''' %s = %s''', _UpperCamelCase, _UpperCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
# Save predictions
lowercase : Optional[int] = os.path.join(training_args.output_dir, '''test_predictions.txt''' )
if trainer.is_world_process_zero():
with open(_UpperCamelCase, '''w''' ) as writer:
with open(os.path.join(data_args.data_dir, '''test.txt''' ), '''r''' ) as f:
token_classification_task.write_predictions_to_file(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
return results
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 337 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__a = 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_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__a = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self ):
lowercase : str = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
lowercase : Any = self.diffusers_dir
shutil.copy(
os.path.join(SCREAMING_SNAKE_CASE__ , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def __lowerCamelCase ( self ):
lowercase : List[Any] = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
lowercase : Tuple = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
lowercase : str = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
lowercase : Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowercase : List[Any] = black.format_str(SCREAMING_SNAKE_CASE__ , mode=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , newline='''\n''' ) as f:
f.write(SCREAMING_SNAKE_CASE__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , '''r''' ) as f:
self.assertTrue(f.read() , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Tuple = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with a really long name
lowercase : List[Any] = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , overwrite_result=re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
| 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'''configuration_blenderbot_small''': [
'''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotSmallConfig''',
'''BlenderbotSmallOnnxConfig''',
],
'''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = ['''BlenderbotSmallTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotSmallForCausalLM''',
'''BlenderbotSmallForConditionalGeneration''',
'''BlenderbotSmallModel''',
'''BlenderbotSmallPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TFBlenderbotSmallForConditionalGeneration''',
'''TFBlenderbotSmallModel''',
'''TFBlenderbotSmallPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''FlaxBlenderbotSmallForConditionalGeneration''',
'''FlaxBlenderbotSmallModel''',
'''FlaxBlenderbotSmallPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=0 ): # a graph with Node 0,1,...,N-1
lowercase : List[Any] = n
lowercase : List[Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # adjacency matrix for weight
lowercase : Union[str, Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # dp[i][j] stores minimum distance from i to j
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : int = w
def __lowerCamelCase ( self ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase : Any = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.dp[u][v]
if __name__ == "__main__":
__a = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 337 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[str, Any] = 'unispeech-sat'
def __init__( self , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=12 , 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__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__="group" , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 512, 512, 512) , SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2) , SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2) , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=128 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.05 , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=320 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=100 , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__="mean" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 1500) , SCREAMING_SNAKE_CASE__=(5, 3, 3, 1, 1) , SCREAMING_SNAKE_CASE__=(1, 2, 3, 1, 1) , SCREAMING_SNAKE_CASE__=512 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=504 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ , pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = hidden_size
lowercase : Optional[int] = feat_extract_norm
lowercase : Dict = feat_extract_activation
lowercase : int = list(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = list(SCREAMING_SNAKE_CASE__ )
lowercase : Dict = list(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = conv_bias
lowercase : Any = num_conv_pos_embeddings
lowercase : Any = num_conv_pos_embedding_groups
lowercase : int = len(self.conv_dim )
lowercase : Union[str, Any] = num_hidden_layers
lowercase : int = intermediate_size
lowercase : Optional[int] = hidden_act
lowercase : List[str] = num_attention_heads
lowercase : Any = hidden_dropout
lowercase : List[Any] = attention_dropout
lowercase : Any = activation_dropout
lowercase : int = feat_proj_dropout
lowercase : str = final_dropout
lowercase : Optional[Any] = layerdrop
lowercase : Optional[Any] = layer_norm_eps
lowercase : Union[str, Any] = initializer_range
lowercase : List[str] = vocab_size
lowercase : List[Any] = num_clusters
lowercase : Union[str, Any] = do_stable_layer_norm
lowercase : List[str] = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase : Union[str, Any] = apply_spec_augment
lowercase : Optional[int] = mask_time_prob
lowercase : Any = mask_time_length
lowercase : Dict = mask_time_min_masks
lowercase : List[str] = mask_feature_prob
lowercase : List[str] = mask_feature_length
lowercase : Union[str, Any] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowercase : List[str] = num_codevectors_per_group
lowercase : str = num_codevector_groups
lowercase : List[Any] = contrastive_logits_temperature
lowercase : Union[str, Any] = feat_quantizer_dropout
lowercase : str = num_negatives
lowercase : Dict = codevector_dim
lowercase : Tuple = proj_codevector_dim
lowercase : Optional[int] = diversity_loss_weight
# ctc loss
lowercase : Any = ctc_loss_reduction
lowercase : Any = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase : Dict = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase : Optional[Any] = list(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = list(SCREAMING_SNAKE_CASE__ )
lowercase : int = list(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = xvector_output_dim
@property
def __lowerCamelCase ( self ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 337 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(_UpperCamelCase ) / len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : Any = prime_factors(_UpperCamelCase )
if is_square_free(_UpperCamelCase ):
return -1 if len(_UpperCamelCase ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 337 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__a = get_tests_dir('''fixtures/spiece.model''')
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
A : Tuple = DebertaVaTokenizer
A : int = DebertaVaTokenizerFast
A : Optional[Any] = True
A : List[str] = True
def __lowerCamelCase ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowercase : Optional[Any] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = '''this is a test'''
lowercase : str = '''this is a test'''
return input_text, output_text
def __lowerCamelCase ( self ):
lowercase : List[Any] = '''<pad>'''
lowercase : Dict = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 30001 )
def __lowerCamelCase ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 30000 )
def __lowerCamelCase ( self ):
# fmt: off
lowercase : List[str] = ''' \tHeLLo!how \n Are yoU? '''
lowercase : Optional[int] = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
lowercase : Tuple = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : str = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def __lowerCamelCase ( self ):
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
# fmt: off
lowercase : List[str] = '''I was born in 92000, and this is falsé.'''
lowercase : Dict = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
lowercase : Optional[int] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : int = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# fmt: off
lowercase : Optional[Any] = '''I was born in 92000, and this is falsé.'''
lowercase : int = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
lowercase : List[Any] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# fmt: off
lowercase : List[str] = '''I was born in 92000, and this is falsé.'''
lowercase : Tuple = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
lowercase : Optional[Any] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# fmt: off
lowercase : Dict = '''I was born in 92000, and this is falsé.'''
lowercase : Tuple = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
lowercase : Optional[int] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# fmt: off
lowercase : Any = ''' \tHeLLo!how \n Are yoU? '''
lowercase : Union[str, Any] = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
lowercase : Any = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Dict = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.get_tokenizer()
lowercase : int = self.get_rust_tokenizer()
lowercase : Tuple = '''I was born in 92000, and this is falsé.'''
lowercase : int = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
lowercase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = self.get_rust_tokenizer()
lowercase : str = tokenizer.encode(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Any = '''This is a test'''
lowercase : Dict = [13, 1, 4398, 25, 21, 1289]
lowercase : Optional[Any] = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
lowercase : str = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
lowercase : Union[str, Any] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Dict = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : int = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# fmt: off
lowercase : Optional[int] = '''I was born in 92000, and this is falsé.'''
lowercase : str = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
lowercase : List[str] = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
lowercase : Dict = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
lowercase : Union[str, Any] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Any = rust_tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Any = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = tokenizer.encode('''sequence builders''' )
lowercase : str = tokenizer.encode('''multi-sequence build''' )
lowercase : Optional[int] = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ )
lowercase : str = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , SCREAMING_SNAKE_CASE__ )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , SCREAMING_SNAKE_CASE__ , )
@slow
def __lowerCamelCase ( self ):
# fmt: off
lowercase : str = {'''input_ids''': [[1, 39867, 36, 19390, 486, 27, 35052, 81436, 18, 60685, 1225, 7, 35052, 81436, 18, 9367, 16899, 18, 15937, 53, 594, 773, 18, 16287, 30465, 36, 15937, 6, 41139, 38, 36979, 60763, 191, 6, 34132, 99, 6, 50538, 390, 43230, 6, 34132, 2779, 20850, 14, 699, 1072, 1194, 36, 382, 10901, 53, 7, 699, 1072, 2084, 36, 20422, 630, 53, 19, 105, 3049, 1896, 1053, 16899, 1506, 11, 37978, 4243, 7, 1237, 31869, 200, 16566, 654, 6, 35052, 81436, 7, 55630, 13593, 4, 2], [1, 26, 15011, 13, 667, 8, 1053, 18, 23611, 1237, 72356, 12820, 34, 104134, 1209, 35, 13313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 5, 1232, 2864, 15785, 14951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE__ , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 337 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase ) ->List[int]:
"""simple docstring"""
if isinstance(_UpperCamelCase, np.ndarray ):
return list(tensor.shape )
lowercase : Optional[Any] = tf.shape(_UpperCamelCase )
if tensor.shape == tf.TensorShape(_UpperCamelCase ):
return dynamic
lowercase : Tuple = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_UpperCamelCase )]
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None, _UpperCamelCase = None ) ->tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9, axis=_UpperCamelCase, name=_UpperCamelCase )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=1e-5, _UpperCamelCase=-1 ) ->int:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_UpperCamelCase, _UpperCamelCase ):
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
lowercase , lowercase : Union[str, Any] = tf.nn.moments(_UpperCamelCase, axes=[axis], keepdims=_UpperCamelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase : int = [1] * inputs.shape.rank
lowercase : Union[str, Any] = shape_list(_UpperCamelCase )[axis]
lowercase : List[str] = tf.reshape(_UpperCamelCase, _UpperCamelCase )
lowercase : Dict = tf.reshape(_UpperCamelCase, _UpperCamelCase )
# Compute layer normalization using the batch_normalization
# function.
lowercase : List[str] = tf.nn.batch_normalization(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, offset=_UpperCamelCase, scale=_UpperCamelCase, variance_epsilon=_UpperCamelCase, )
return outputs
def __lowercase ( _UpperCamelCase, _UpperCamelCase=0, _UpperCamelCase=-1 ) ->List[Any]:
"""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
lowercase : Dict = tf.shape(_UpperCamelCase )
lowercase : Optional[Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase : List[str] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]], axis=0 )
return tf.reshape(_UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->tf.Tensor:
"""simple docstring"""
if not isinstance(_UpperCamelCase, tf.Tensor ):
lowercase : Optional[Any] = tf.convert_to_tensor(_UpperCamelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase : Tuple = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase : str = (
tf.cast(1, encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = "input_ids" ) ->None:
"""simple docstring"""
tf.debugging.assert_less(
_UpperCamelCase, tf.cast(_UpperCamelCase, dtype=tensor.dtype ), message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(_UpperCamelCase )}) must be smaller than the embedding """
f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
), )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : List[Any] = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase : Optional[int] = [x for x in data if len(_UpperCamelCase ) > 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}""" )
lowercase : Any = np.asarray(_UpperCamelCase )
lowercase : List[Any] = 1
lowercase : Tuple = np.array_split(_UpperCamelCase, _UpperCamelCase )
# 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
lowercase : Dict = np.array_split(_UpperCamelCase, _UpperCamelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_UpperCamelCase ):
lowercase : Optional[int] = chunk_data
else:
lowercase : int = data
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
if name in group.attrs:
lowercase : str = [n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs[name]]
else:
lowercase : Optional[Any] = []
lowercase : List[str] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
def _expand_single_ad_tensor(_UpperCamelCase ):
if isinstance(_UpperCamelCase, tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_UpperCamelCase, axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor, _UpperCamelCase )
| 337 | 1 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__a = logging.get_logger(__name__)
__a = {
'''tensor(bool)''': np.bool_,
'''tensor(int8)''': np.inta,
'''tensor(uint8)''': np.uinta,
'''tensor(int16)''': np.intaa,
'''tensor(uint16)''': np.uintaa,
'''tensor(int32)''': np.intaa,
'''tensor(uint32)''': np.uintaa,
'''tensor(int64)''': np.intaa,
'''tensor(uint64)''': np.uintaa,
'''tensor(float16)''': np.floataa,
'''tensor(float)''': np.floataa,
'''tensor(double)''': np.floataa,
}
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ ):
logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.''' )
lowercase : int = model
lowercase : List[str] = kwargs.get('''model_save_dir''' , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = kwargs.get('''latest_model_name''' , SCREAMING_SNAKE_CASE__ )
def __call__( self , **SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = {k: np.array(SCREAMING_SNAKE_CASE__ ) for k, v in kwargs.items()}
return self.model.run(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@staticmethod
def __lowerCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None ):
if provider is None:
logger.info('''No onnxruntime provider specified, using CPUExecutionProvider''' )
lowercase : Optional[Any] = '''CPUExecutionProvider'''
return ort.InferenceSession(SCREAMING_SNAKE_CASE__ , providers=[provider] , sess_options=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = file_name if file_name is not None else ONNX_WEIGHTS_NAME
lowercase : Union[str, Any] = self.model_save_dir.joinpath(self.latest_model_name )
lowercase : Any = Path(SCREAMING_SNAKE_CASE__ ).joinpath(SCREAMING_SNAKE_CASE__ )
try:
shutil.copyfile(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
lowercase : int = self.model_save_dir.joinpath(SCREAMING_SNAKE_CASE__ )
if src_path.exists():
lowercase : int = Path(SCREAMING_SNAKE_CASE__ ).joinpath(SCREAMING_SNAKE_CASE__ )
try:
shutil.copyfile(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
except shutil.SameFileError:
pass
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , ):
if os.path.isfile(SCREAMING_SNAKE_CASE__ ):
logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
return
os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__ )
# saving model weights/files
self._save_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@classmethod
def __lowerCamelCase ( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
lowercase : Any = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = OnnxRuntimeModel.load_model(
os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , provider=SCREAMING_SNAKE_CASE__ , sess_options=SCREAMING_SNAKE_CASE__ )
lowercase : int = Path(SCREAMING_SNAKE_CASE__ )
# load model from hub
else:
# download model
lowercase : int = hf_hub_download(
repo_id=SCREAMING_SNAKE_CASE__ , filename=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , revision=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ , force_download=SCREAMING_SNAKE_CASE__ , )
lowercase : str = Path(SCREAMING_SNAKE_CASE__ ).parent
lowercase : Optional[int] = Path(SCREAMING_SNAKE_CASE__ ).name
lowercase : Dict = OnnxRuntimeModel.load_model(SCREAMING_SNAKE_CASE__ , provider=SCREAMING_SNAKE_CASE__ , sess_options=SCREAMING_SNAKE_CASE__ )
return cls(model=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@classmethod
def __lowerCamelCase ( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
lowercase : int = None
if len(str(SCREAMING_SNAKE_CASE__ ).split('''@''' ) ) == 2:
lowercase , lowercase : List[Any] = model_id.split('''@''' )
return cls._from_pretrained(
model_id=SCREAMING_SNAKE_CASE__ , revision=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ , force_download=SCREAMING_SNAKE_CASE__ , use_auth_token=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
| 337 |
def __lowercase ( _UpperCamelCase = 4000000 ) ->int:
"""simple docstring"""
lowercase : int = []
lowercase , lowercase : str = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCamelCase )
lowercase , lowercase : Dict = b, a + b
return sum(_UpperCamelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 | 1 |
import os
def __lowercase ( _UpperCamelCase = "input.txt" ) ->int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(_UpperCamelCase ), _UpperCamelCase ) ) as input_file:
lowercase : List[str] = [
[int(_UpperCamelCase ) for element in line.split(''',''' )]
for line in input_file.readlines()
]
lowercase : List[str] = len(_UpperCamelCase )
lowercase : List[str] = len(matrix[0] )
lowercase : List[str] = [[-1 for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
lowercase : Optional[int] = matrix[i][0]
for j in range(1, _UpperCamelCase ):
for i in range(_UpperCamelCase ):
lowercase : Dict = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1, _UpperCamelCase ):
lowercase : Optional[int] = min(
minimal_path_sums[i][j], minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2, -1, -1 ):
lowercase : int = min(
minimal_path_sums[i][j], minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
__a = logging.get_logger(__name__)
__a = {
'''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'perceiver'
def __init__( self , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=1280 , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=26 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="kv" , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=262 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=56 , SCREAMING_SNAKE_CASE__=[368, 496] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=1920 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=[1, 16, 224, 224] , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Any = num_latents
lowercase : Union[str, Any] = d_latents
lowercase : str = d_model
lowercase : int = num_blocks
lowercase : str = num_self_attends_per_block
lowercase : List[str] = num_self_attention_heads
lowercase : List[str] = num_cross_attention_heads
lowercase : int = qk_channels
lowercase : List[Any] = v_channels
lowercase : int = cross_attention_shape_for_attention
lowercase : Tuple = self_attention_widening_factor
lowercase : Dict = cross_attention_widening_factor
lowercase : Any = hidden_act
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : Union[str, Any] = initializer_range
lowercase : Any = layer_norm_eps
lowercase : Any = use_query_residual
# masked language modeling attributes
lowercase : List[str] = vocab_size
lowercase : Dict = max_position_embeddings
# image classification attributes
lowercase : int = image_size
# flow attributes
lowercase : List[Any] = train_size
# multimodal autoencoding attributes
lowercase : List[Any] = num_frames
lowercase : Union[str, Any] = audio_samples_per_frame
lowercase : int = samples_per_patch
lowercase : Optional[int] = output_shape
class __SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCamelCase ( self ):
if self.task == "multiple-choice":
lowercase : Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase : Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''inputs''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = -1 , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 3 , SCREAMING_SNAKE_CASE__ = 40 , SCREAMING_SNAKE_CASE__ = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase : str = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase : Union[str, Any] = preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = compute_effective_axis_dimension(
SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=SCREAMING_SNAKE_CASE__ )
# Generate dummy inputs according to compute batch and sequence
lowercase : Optional[Any] = [''' '''.join(['''a'''] ) * seq_length] * batch_size
lowercase : Any = dict(preprocessor(SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = inputs.pop('''input_ids''' )
return inputs
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase : List[str] = compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__ , fixed_dimension=OnnxConfig.default_fixed_batch )
lowercase : List[str] = self._generate_dummy_images(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = dict(preprocessor(images=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = inputs.pop('''pixel_values''' )
return inputs
else:
raise ValueError(
'''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''' )
| 337 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase=None, _UpperCamelCase=None ) ->List[Any]:
"""simple docstring"""
return field(default_factory=lambda: default, metadata=_UpperCamelCase )
@dataclass
class __SCREAMING_SNAKE_CASE :
A : List[str] = list_field(
default=[] , metadata={
'help': (
'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
' of all available models'
)
} , )
A : List[int] = list_field(
default=[8] , metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'} )
A : List[int] = list_field(
default=[8, 32, 128, 512] , metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'} , )
A : bool = field(
default=A__ , metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'} , )
A : bool = field(
default=A__ , metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'} , )
A : bool = field(
default=A__ , metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'} )
A : bool = field(default=A__ , metadata={'help': 'Use FP16 to accelerate inference.'} )
A : bool = field(default=A__ , metadata={'help': 'Benchmark training of model'} )
A : bool = field(default=A__ , metadata={'help': 'Verbose memory tracing'} )
A : bool = field(
default=A__ , metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'} , )
A : bool = field(
default=A__ , metadata={
'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
} , )
A : bool = field(default=A__ , metadata={'help': 'Trace memory line by line'} )
A : bool = field(default=A__ , metadata={'help': 'Save result to a CSV file'} )
A : bool = field(default=A__ , metadata={'help': 'Save all print statements in a log file'} )
A : bool = field(default=A__ , metadata={'help': 'Whether to print environment information'} )
A : bool = field(
default=A__ , metadata={
'help': (
'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
' for debugging / testing and on TPU.'
)
} , )
A : str = field(
default=F'inference_time_{round(time() )}.csv' , metadata={'help': 'CSV filename used if saving time results to csv.'} , )
A : str = field(
default=F'inference_memory_{round(time() )}.csv' , metadata={'help': 'CSV filename used if saving memory results to csv.'} , )
A : str = field(
default=F'train_time_{round(time() )}.csv' , metadata={'help': 'CSV filename used if saving time results to csv for training.'} , )
A : str = field(
default=F'train_memory_{round(time() )}.csv' , metadata={'help': 'CSV filename used if saving memory results to csv for training.'} , )
A : str = field(
default=F'env_info_{round(time() )}.csv' , metadata={'help': 'CSV filename used if saving environment information.'} , )
A : str = field(
default=F'log_{round(time() )}.csv' , metadata={'help': 'Log filename used if print statements are saved in log.'} , )
A : int = field(default=3 , metadata={'help': 'Times an experiment will be run.'} )
A : bool = field(
default=A__ , metadata={
'help': (
'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
' model weights.'
)
} , )
def __lowerCamelCase ( self ):
warnings.warn(
f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , SCREAMING_SNAKE_CASE__ , )
def __lowerCamelCase ( self ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def __lowerCamelCase ( self ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def __lowerCamelCase ( self ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 337 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowercase ( _UpperCamelCase = 8 ) ->str:
"""simple docstring"""
lowercase : List[str] = ascii_letters + digits + punctuation
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
i -= len(_UpperCamelCase )
lowercase : Dict = i // 3
lowercase : List[str] = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowercase : Union[str, Any] = (
chars_incl
+ random(_UpperCamelCase, quotient + remainder )
+ random(_UpperCamelCase, _UpperCamelCase )
+ random(_UpperCamelCase, _UpperCamelCase )
)
lowercase : Union[str, Any] = list(_UpperCamelCase )
shuffle(_UpperCamelCase )
return "".join(_UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->str:
"""simple docstring"""
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Dict:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[Any]:
"""simple docstring"""
pass # Put your code here...
def __lowercase ( _UpperCamelCase, _UpperCamelCase = 8 ) ->bool:
"""simple docstring"""
if len(_UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowercase : str = any(char in ascii_uppercase for char in password )
lowercase : List[str] = any(char in ascii_lowercase for char in password )
lowercase : Dict = any(char in digits for char in password )
lowercase : Tuple = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def __lowercase ( ) ->Dict:
"""simple docstring"""
lowercase : Union[str, Any] = int(input('''Please indicate the max length of your password: ''' ).strip() )
lowercase : Optional[Any] = input(
'''Please indicate the characters that must be in your password: ''' ).strip()
print('''Password generated:''', password_generator(_UpperCamelCase ) )
print(
'''Alternative Password generated:''', alternative_password_generator(_UpperCamelCase, _UpperCamelCase ), )
print('''[If you are thinking of using this passsword, You better save it.]''' )
if __name__ == "__main__":
main()
| 337 | 1 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'''vocab_file''': '''vocab.json'''}
__a = {
'''vocab_file''': {
'''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
}
}
__a = {'''mgp-str''': 27}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Optional[int] = VOCAB_FILES_NAMES
A : Dict = PRETRAINED_VOCAB_FILES_MAP
A : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="[GO]" , SCREAMING_SNAKE_CASE__="[GO]" , SCREAMING_SNAKE_CASE__="[s]" , SCREAMING_SNAKE_CASE__="[GO]" , **SCREAMING_SNAKE_CASE__ ):
super().__init__(
unk_token=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
with open(SCREAMING_SNAKE_CASE__ , encoding='''utf-8''' ) as vocab_handle:
lowercase : Dict = json.load(SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = {v: k for k, v in self.vocab.items()}
@property
def __lowerCamelCase ( self ):
return len(self.vocab )
def __lowerCamelCase ( self ):
return dict(self.vocab , **self.added_tokens_encoder )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = []
for s in text:
char_tokens.extend(SCREAMING_SNAKE_CASE__ )
return char_tokens
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self.vocab.get(SCREAMING_SNAKE_CASE__ , self.vocab.get(self.unk_token ) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self.decoder.get(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE__ ) )
return
lowercase : str = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ ) + '''\n''' )
return (vocab_file,)
| 337 |
from __future__ import annotations
__a = []
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_UpperCamelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, -1, -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(_UpperCamelCase, -1, -1 ), range(_UpperCamelCase, len(_UpperCamelCase ) ) ):
if board[i][j] == 1:
return False
return True
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
if row >= len(_UpperCamelCase ):
solution.append(_UpperCamelCase )
printboard(_UpperCamelCase )
print()
return True
for i in range(len(_UpperCamelCase ) ):
if is_safe(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : int = 1
solve(_UpperCamelCase, row + 1 )
lowercase : Tuple = 0
return False
def __lowercase ( _UpperCamelCase ) ->None:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
for j in range(len(_UpperCamelCase ) ):
if board[i][j] == 1:
print('''Q''', end=''' ''' )
else:
print('''.''', end=''' ''' )
print()
# n=int(input("The no. of queens"))
__a = 8
__a = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 337 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__a = '''src/diffusers'''
__a = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
__a = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
__a = spec.loader.load_module()
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[Any]:
"""simple docstring"""
return line.startswith(_UpperCamelCase ) or len(_UpperCamelCase ) <= 1 or re.search(R'''^\s*\)(\s*->.*:|:)\s*$''', _UpperCamelCase ) is not None
def __lowercase ( _UpperCamelCase ) ->Optional[Any]:
"""simple docstring"""
lowercase : List[str] = object_name.split('''.''' )
lowercase : List[str] = 0
# First let's find the module where our object lives.
lowercase : Dict = parts[i]
while i < len(_UpperCamelCase ) and not os.path.isfile(os.path.join(_UpperCamelCase, f"""{module}.py""" ) ):
i += 1
if i < len(_UpperCamelCase ):
lowercase : int = os.path.join(_UpperCamelCase, parts[i] )
if i >= len(_UpperCamelCase ):
raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(_UpperCamelCase, f"""{module}.py""" ), '''r''', encoding='''utf-8''', newline='''\n''' ) as f:
lowercase : List[Any] = f.readlines()
# Now let's find the class / func in the code!
lowercase : Optional[Any] = ''''''
lowercase : Optional[Any] = 0
for name in parts[i + 1 :]:
while (
line_index < len(_UpperCamelCase ) and re.search(Rf"""^{indent}(class|def)\s+{name}(\(|\:)""", lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(_UpperCamelCase ):
raise ValueError(f""" {object_name} does not match any function or class in {module}.""" )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
lowercase : Tuple = line_index
while line_index < len(_UpperCamelCase ) and _should_continue(lines[line_index], _UpperCamelCase ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
lowercase : str = lines[start_index:line_index]
return "".join(_UpperCamelCase )
__a = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
__a = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
__a = re.compile(r'''<FILL\s+[^>]*>''')
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : str = code.split('''\n''' )
lowercase : str = 0
while idx < len(_UpperCamelCase ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(_UpperCamelCase ):
return re.search(R'''^(\s*)\S''', lines[idx] ).groups()[0]
return ""
def __lowercase ( _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : List[Any] = len(get_indent(_UpperCamelCase ) ) > 0
if has_indent:
lowercase : Dict = f"""class Bla:\n{code}"""
lowercase : Dict = black.Mode(target_versions={black.TargetVersion.PYaa}, line_length=119, preview=_UpperCamelCase )
lowercase : List[Any] = black.format_str(_UpperCamelCase, mode=_UpperCamelCase )
lowercase , lowercase : str = style_docstrings_in_code(_UpperCamelCase )
return result[len('''class Bla:\n''' ) :] if has_indent else result
def __lowercase ( _UpperCamelCase, _UpperCamelCase=False ) ->Optional[Any]:
"""simple docstring"""
with open(_UpperCamelCase, '''r''', encoding='''utf-8''', newline='''\n''' ) as f:
lowercase : List[Any] = f.readlines()
lowercase : Tuple = []
lowercase : List[Any] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(_UpperCamelCase ):
lowercase : Dict = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
lowercase , lowercase , lowercase : Any = search.groups()
lowercase : Any = find_code_in_diffusers(_UpperCamelCase )
lowercase : Dict = get_indent(_UpperCamelCase )
lowercase : Optional[int] = line_index + 1 if indent == theoretical_indent else line_index + 2
lowercase : int = theoretical_indent
lowercase : Union[str, Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
lowercase : Optional[Any] = True
while line_index < len(_UpperCamelCase ) and should_continue:
line_index += 1
if line_index >= len(_UpperCamelCase ):
break
lowercase : List[Any] = lines[line_index]
lowercase : str = _should_continue(_UpperCamelCase, _UpperCamelCase ) and re.search(f"""^{indent}# End copy""", _UpperCamelCase ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
lowercase : List[str] = lines[start_index:line_index]
lowercase : Tuple = ''''''.join(_UpperCamelCase )
# Remove any nested `Copied from` comments to avoid circular copies
lowercase : Dict = [line for line in theoretical_code.split('''\n''' ) if _re_copy_warning.search(_UpperCamelCase ) is None]
lowercase : Dict = '''\n'''.join(_UpperCamelCase )
# Before comparing, use the `replace_pattern` on the original code.
if len(_UpperCamelCase ) > 0:
lowercase : Any = replace_pattern.replace('''with''', '''''' ).split(''',''' )
lowercase : Dict = [_re_replace_pattern.search(_UpperCamelCase ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
lowercase , lowercase , lowercase : Any = pattern.groups()
lowercase : int = re.sub(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
if option.strip() == "all-casing":
lowercase : List[str] = re.sub(obja.lower(), obja.lower(), _UpperCamelCase )
lowercase : Optional[int] = re.sub(obja.upper(), obja.upper(), _UpperCamelCase )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
lowercase : List[Any] = blackify(lines[start_index - 1] + theoretical_code )
lowercase : List[str] = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
lowercase : Optional[int] = lines[:start_index] + [theoretical_code] + lines[line_index:]
lowercase : Optional[Any] = start_index + 1
if overwrite and len(_UpperCamelCase ) > 0:
# Warn the user a file has been modified.
print(f"""Detected changes, rewriting {filename}.""" )
with open(_UpperCamelCase, '''w''', encoding='''utf-8''', newline='''\n''' ) as f:
f.writelines(_UpperCamelCase )
return diffs
def __lowercase ( _UpperCamelCase = False ) ->Any:
"""simple docstring"""
lowercase : Dict = glob.glob(os.path.join(_UpperCamelCase, '''**/*.py''' ), recursive=_UpperCamelCase )
lowercase : Dict = []
for filename in all_files:
lowercase : Any = is_copy_consistent(_UpperCamelCase, _UpperCamelCase )
diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(_UpperCamelCase ) > 0:
lowercase : Tuple = '''\n'''.join(_UpperCamelCase )
raise Exception(
'''Found the following copy inconsistencies:\n'''
+ diff
+ '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.''' )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
__a = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 337 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CTRLForSequenceClassification''',
'''CTRLLMHeadModel''',
'''CTRLModel''',
'''CTRLPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCTRLForSequenceClassification''',
'''TFCTRLLMHeadModel''',
'''TFCTRLModel''',
'''TFCTRLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__a = datasets.load_iris()
__a = np.array(data['''data'''])
__a = np.array(data['''target'''])
__a = data['''target_names''']
__a , __a , __a , __a = train_test_split(X, y)
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Optional[Any]:
"""simple docstring"""
return np.linalg.norm(np.array(_UpperCamelCase ) - np.array(_UpperCamelCase ) )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=5 ) ->str:
"""simple docstring"""
lowercase : Optional[int] = zip(_UpperCamelCase, _UpperCamelCase )
# List of distances of all points from the point to be classified
lowercase : List[str] = []
for data_point in data:
lowercase : List[Any] = euclidean_distance(data_point[0], _UpperCamelCase )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
lowercase : Optional[int] = [i[1] for i in sorted(_UpperCamelCase )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
lowercase : Dict = Counter(_UpperCamelCase ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 337 |
from collections.abc import Callable
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
# Stores actual heap items.
lowercase : list = []
# Stores indexes of each item for supporting updates and deletion.
lowercase : dict = {}
# Stores current size of heap.
lowercase : str = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowercase : Tuple = key or (lambda SCREAMING_SNAKE_CASE__ : x)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return int((i - 1) / 2 ) if i > 0 else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : Dict = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowercase , lowercase : int = self.arr[j], self.arr[i]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.arr[i][1] < self.arr[j][1]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : int = self._left(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = self._right(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = i
if left is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = left
if right is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : List[str] = right
return valid_parent
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = self._parent(SCREAMING_SNAKE_CASE__ )
while parent is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : Optional[int] = parent, self._parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
while valid_parent != index:
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase , lowercase : str = valid_parent, self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : str = self.pos_map[item]
lowercase : Optional[int] = [item, self.key(SCREAMING_SNAKE_CASE__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if item not in self.pos_map:
return
lowercase : List[str] = self.pos_map[item]
del self.pos_map[item]
lowercase : Optional[int] = self.arr[self.size - 1]
lowercase : int = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(SCREAMING_SNAKE_CASE__ )] )
else:
lowercase : int = [item, self.key(SCREAMING_SNAKE_CASE__ )]
lowercase : str = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowerCamelCase ( self ):
return self.arr[0] if self.size else None
def __lowerCamelCase ( self ):
lowercase : str = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def __lowercase ( ) ->None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : str = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += [key]
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
def __lowercase ( *_UpperCamelCase ) ->Any:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : List[Any] = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += keys
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
class __SCREAMING_SNAKE_CASE ( A__ ):
def __new__( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = super().__new__(cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not hasattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' ):
setattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' , {} )
setattr(SCREAMING_SNAKE_CASE__ , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
lowercase : Dict = getattr(SCREAMING_SNAKE_CASE__ , '''handle_key''' , [] )
for key in handled_keys:
lowercase : List[Any] = value
return new_cls
@staticmethod
def __lowerCamelCase ( cls ):
lowercase : Dict = get_character()
if char != KEYMAP["undefined"]:
lowercase : Optional[int] = ord(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = cls.key_handler.get(SCREAMING_SNAKE_CASE__ )
if handler:
lowercase : Tuple = char
return handler(cls )
else:
return None
def __lowercase ( cls ) ->Any:
"""simple docstring"""
return KeyHandler(cls.__name__, cls.__bases__, cls.__dict__.copy() )
| 337 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[List[np.ndarray], torch.FloatTensor]
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 .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 337 | 1 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__a = '''▁'''
__a = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class __SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
A : List[Any] = BertGenerationTokenizer
A : Tuple = False
A : List[str] = True
def __lowerCamelCase ( self ):
super().setUp()
lowercase : str = BertGenerationTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCamelCase ( self ):
lowercase : int = '''<s>'''
lowercase : Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 1002 )
def __lowerCamelCase ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def __lowerCamelCase ( self ):
lowercase : List[Any] = BertGenerationTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [285, 46, 10, 170, 382] , )
lowercase : Optional[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
lowercase : List[Any] = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
lowercase : Optional[Any] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def __lowerCamelCase ( self ):
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def __lowerCamelCase ( self ):
lowercase : int = '''Hello World!'''
lowercase : Optional[Any] = [18536, 2260, 101]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__ ) )
@slow
def __lowerCamelCase ( self ):
lowercase : Dict = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
lowercase : Any = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
34324,
497,
391,
408,
11342,
1244,
385,
100,
938,
985,
456,
574,
362,
12597,
3200,
3129,
1172,
]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__ ) )
@require_torch
@slow
def __lowerCamelCase ( self ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
lowercase : List[Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
lowercase : List[str] = ''' '''.join(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' , return_token_type_ids=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = BertGenerationConfig()
lowercase : Union[str, Any] = BertGenerationEncoder(SCREAMING_SNAKE_CASE__ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**SCREAMING_SNAKE_CASE__ )
model(**SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCamelCase ( self ):
# fmt: off
lowercase : Optional[int] = {'''input_ids''': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE__ , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 337 |
import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__a = logging.get_logger(__name__)
__a = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__a = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__a = {'''facebook/blenderbot-3B''': 1_28}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Dict = VOCAB_FILES_NAMES
A : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Optional[int] = ['input_ids', 'attention_mask']
A : str = BlenderbotTokenizer
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="replace" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , tokenizer_file=SCREAMING_SNAKE_CASE__ , errors=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ , trim_offsets=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
lowercase : str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : List[Any] = getattr(SCREAMING_SNAKE_CASE__ , pre_tok_state.pop('''type''' ) )
lowercase : str = add_prefix_space
lowercase : List[Any] = pre_tok_class(**SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = add_prefix_space
lowercase : str = '''post_processor'''
lowercase : str = getattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if tokenizer_component_instance:
lowercase : Optional[int] = 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:
lowercase : Tuple = tuple(state['''sep'''] )
if "cls" in state:
lowercase : Union[str, Any] = tuple(state['''cls'''] )
lowercase : Optional[int] = False
if state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : Any = add_prefix_space
lowercase : Tuple = True
if state.get('''trim_offsets''' , SCREAMING_SNAKE_CASE__ ) != trim_offsets:
lowercase : List[str] = trim_offsets
lowercase : Optional[int] = True
if changes_to_apply:
lowercase : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE__ , state.pop('''type''' ) )
lowercase : Union[str, Any] = component_class(**SCREAMING_SNAKE_CASE__ )
setattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def __lowerCamelCase ( self ):
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else value
lowercase : Any = value
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
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(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Any = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
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(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : int = self._tokenizer.model.save(SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
return tuple(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Tuple = [self.sep_token_id]
lowercase : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
return token_ids_a + [self.eos_token_id]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = ''' '''.join(SCREAMING_SNAKE_CASE__ )
lowercase : Any = self.encode(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > self.model_max_length:
lowercase : Tuple = input_ids[-self.model_max_length :]
logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 337 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CTRLForSequenceClassification''',
'''CTRLLMHeadModel''',
'''CTRLModel''',
'''CTRLPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCTRLForSequenceClassification''',
'''TFCTRLLMHeadModel''',
'''TFCTRLModel''',
'''TFCTRLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 337 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ) ->int:
"""simple docstring"""
lowercase : Tuple = HfArgumentParser(_UpperCamelCase )
lowercase : List[str] = parser.parse_args_into_dataclasses()[0]
lowercase : Optional[int] = TensorFlowBenchmark(args=_UpperCamelCase )
try:
lowercase : Any = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase : Optional[int] = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
lowercase : Any = ''' '''.join(str(_UpperCamelCase ).split(''' ''' )[:-1] )
lowercase : Any = ''''''
lowercase : str = eval(str(_UpperCamelCase ).split(''' ''' )[-1] )
lowercase : List[str] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
lowercase : Union[str, Any] = full_error_msg + begin_error_msg + str(_UpperCamelCase )
raise ValueError(_UpperCamelCase )
benchmark.run()
if __name__ == "__main__":
main()
| 337 | 1 |
import math
def __lowercase ( _UpperCamelCase ) ->bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(_UpperCamelCase ) + 1 ), 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __lowercase ( _UpperCamelCase = 0.1 ) ->int:
"""simple docstring"""
lowercase : int = 3
lowercase : int = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1, (j + 2) * (j + 2), j + 1 ):
primes += is_prime(_UpperCamelCase )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : Union[str, Any] = [False] * len(_UpperCamelCase )
lowercase : Optional[int] = []
queue.append(_UpperCamelCase )
lowercase : Union[str, Any] = True
while queue:
lowercase : List[str] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_UpperCamelCase )
lowercase : Tuple = True
lowercase : Optional[Any] = u
return visited[t]
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : List[str] = [-1] * (len(_UpperCamelCase ))
lowercase : int = 0
while bfs(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ):
lowercase : List[str] = float('''Inf''' )
lowercase : int = sink
while s != source:
# Find the minimum value in select path
lowercase : List[Any] = min(_UpperCamelCase, graph[parent[s]][s] )
lowercase : Union[str, Any] = parent[s]
max_flow += path_flow
lowercase : Optional[int] = sink
while v != source:
lowercase : Any = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowercase : Union[str, Any] = parent[v]
return max_flow
__a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__a , __a = 0, 5
print(ford_fulkerson(graph, source, sink))
| 337 | 1 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__a = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
booktitle = {},
year = {2002},
pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
author = "Lin, Chin-Yew and
Och, Franz Josef",
booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
month = "aug 23{--}aug 27",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://www.aclweb.org/anthology/C04-1072",
pages = "501--507",
}
'''
__a = '''\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,
the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.
Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].
BLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
'''
__a = '''
Computes BLEU score of translated segments against one or more references.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
\'bleu\': bleu score,
\'precisions\': geometric mean of n-gram precisions,
\'brevity_penalty\': brevity penalty,
\'length_ratio\': ratio of lengths,
\'translation_length\': translation_length,
\'reference_length\': reference_length
Examples:
>>> predictions = [
... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample
... ["foo", "bar", "foobar"] # tokenized prediction of the second sample
... ]
>>> references = [
... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)
... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)
... ]
>>> bleu = datasets.load_metric("bleu")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results["bleu"])
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def __lowerCamelCase ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=False ):
lowercase : Optional[int] = compute_bleu(
reference_corpus=SCREAMING_SNAKE_CASE__ , translation_corpus=SCREAMING_SNAKE_CASE__ , max_order=SCREAMING_SNAKE_CASE__ , smooth=SCREAMING_SNAKE_CASE__ )
((lowercase) , (lowercase) , (lowercase) , (lowercase) , (lowercase) , (lowercase)) : List[Any] = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 337 |
from typing import List
from .keymap import KEYMAP, get_character
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : str = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += [key]
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
def __lowercase ( *_UpperCamelCase ) ->Any:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : List[Any] = getattr(_UpperCamelCase, '''handle_key''', [] )
handle += keys
setattr(_UpperCamelCase, '''handle_key''', _UpperCamelCase )
return func
return decorator
class __SCREAMING_SNAKE_CASE ( A__ ):
def __new__( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : str = super().__new__(cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not hasattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' ):
setattr(SCREAMING_SNAKE_CASE__ , '''key_handler''' , {} )
setattr(SCREAMING_SNAKE_CASE__ , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
lowercase : Dict = getattr(SCREAMING_SNAKE_CASE__ , '''handle_key''' , [] )
for key in handled_keys:
lowercase : List[Any] = value
return new_cls
@staticmethod
def __lowerCamelCase ( cls ):
lowercase : Dict = get_character()
if char != KEYMAP["undefined"]:
lowercase : Optional[int] = ord(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = cls.key_handler.get(SCREAMING_SNAKE_CASE__ )
if handler:
lowercase : Tuple = char
return handler(cls )
else:
return None
def __lowercase ( cls ) ->Any:
"""simple docstring"""
return KeyHandler(cls.__name__, cls.__bases__, cls.__dict__.copy() )
| 337 | 1 |
from __future__ import annotations
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = TypeError(
'''Matrices must be formed from a list of zero or more lists containing at '''
'''least one and the same number of values, each of which must be of type '''
'''int or float.''' )
if len(SCREAMING_SNAKE_CASE__ ) != 0:
lowercase : int = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(SCREAMING_SNAKE_CASE__ ) != cols:
raise error
for value in row:
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise error
lowercase : List[Any] = rows
else:
lowercase : List[str] = []
def __lowerCamelCase ( self ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def __lowerCamelCase ( self ):
return len(self.rows )
@property
def __lowerCamelCase ( self ):
return len(self.rows[0] )
@property
def __lowerCamelCase ( self ):
return (self.num_rows, self.num_columns)
@property
def __lowerCamelCase ( self ):
return self.order[0] == self.order[1]
def __lowerCamelCase ( self ):
lowercase : Dict = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def __lowerCamelCase ( self ):
return bool(self.determinant() )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(SCREAMING_SNAKE_CASE__ ).determinant()
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if (row + column) % 2 == 0:
return self.get_minor(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return -1 * self.get_minor(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
return Matrix(
[
[self.get_minor(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def __lowerCamelCase ( self ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def __lowerCamelCase ( self ):
lowercase : int = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Dict = self.determinant()
if not determinant:
raise TypeError('''Only matrices with a non-zero determinant have an inverse''' )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
return str(self.rows )
def __str__( self ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'''[''' + '''. '''.join([str(SCREAMING_SNAKE_CASE__ ) for value in row] ) + '''.]'''
for row in self.rows
] )
+ "]"
)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : str = TypeError('''Row must be a list containing all ints and/or floats''' )
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise type_error
for value in row:
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise type_error
if len(SCREAMING_SNAKE_CASE__ ) != self.num_columns:
raise ValueError(
'''Row must be equal in length to the other rows in the matrix''' )
if position is None:
self.rows.append(SCREAMING_SNAKE_CASE__ )
else:
lowercase : Union[str, Any] = self.rows[0:position] + [row] + self.rows[position:]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Dict = TypeError(
'''Column must be a list containing all ints and/or floats''' )
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise type_error
for value in column:
if not isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
raise type_error
if len(SCREAMING_SNAKE_CASE__ ) != self.num_rows:
raise ValueError(
'''Column must be equal in length to the other columns in the matrix''' )
if position is None:
lowercase : Any = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowercase : Optional[Any] = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , SCREAMING_SNAKE_CASE__ ):
return not self == other
def __neg__( self ):
return self * -1
def __add__( self , SCREAMING_SNAKE_CASE__ ):
if self.order != other.order:
raise ValueError('''Addition requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , SCREAMING_SNAKE_CASE__ ):
if self.order != other.order:
raise ValueError('''Subtraction requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , SCREAMING_SNAKE_CASE__ ):
if isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if self.num_columns != other.num_rows:
raise ValueError(
'''The number of columns in the first matrix must '''
'''be equal to the number of rows in the second''' )
return Matrix(
[
[Matrix.dot_product(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'''A Matrix can only be multiplied by an int, float, or another matrix''' )
def __pow__( self , SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('''A Matrix can only be raised to the power of an int''' )
if not self.is_square:
raise ValueError('''Only square matrices can be raised to a power''' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'''Only invertable matrices can be raised to a negative power''' )
lowercase : int = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def __lowerCamelCase ( cls , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return sum(row[i] * column[i] for i in range(len(SCREAMING_SNAKE_CASE__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 |
import logging
import os
from .state import PartialState
class __SCREAMING_SNAKE_CASE ( logging.LoggerAdapter ):
@staticmethod
def __lowerCamelCase ( SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
lowercase : List[str] = kwargs.pop('''main_process_only''' , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = kwargs.pop('''in_order''' , SCREAMING_SNAKE_CASE__ )
if self.isEnabledFor(SCREAMING_SNAKE_CASE__ ):
if self._should_log(SCREAMING_SNAKE_CASE__ ):
lowercase , lowercase : str = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
elif in_order:
lowercase : List[Any] = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
lowercase , lowercase : Union[str, Any] = self.process(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.logger.log(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
state.wait_for_everyone()
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->List[Any]:
"""simple docstring"""
if log_level is None:
lowercase : str = os.environ.get('''ACCELERATE_LOG_LEVEL''', _UpperCamelCase )
lowercase : str = logging.getLogger(_UpperCamelCase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(_UpperCamelCase, {} )
| 337 | 1 |
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 __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = "arrow" , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(
split=SCREAMING_SNAKE_CASE__ , features=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ , keep_in_memory=SCREAMING_SNAKE_CASE__ , streaming=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
lowercase : Union[str, Any] = load_from_cache_file
lowercase : Optional[Any] = file_format
lowercase : int = Spark(
df=SCREAMING_SNAKE_CASE__ , features=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ , working_dir=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
def __lowerCamelCase ( self ):
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowercase : Optional[Any] = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=SCREAMING_SNAKE_CASE__ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 337 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super().__init__()
lowercase : Any = model
lowercase : Optional[Any] = 2
lowercase : Optional[int] = nn.Linear(self.model.config.hidden_size , self.num_labels )
def __lowerCamelCase ( self ):
pass
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : str = LongformerModel.from_pretrained(_UpperCamelCase )
lowercase : int = LightningModel(_UpperCamelCase )
lowercase : Union[str, Any] = torch.load(_UpperCamelCase, map_location=torch.device('''cpu''' ) )
lightning_model.load_state_dict(ckpt['''state_dict'''] )
# init longformer question answering model
lowercase : List[Any] = LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(_UpperCamelCase )
print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--longformer_model''',
default=None,
type=str,
required=True,
help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
)
parser.add_argument(
'''--longformer_question_answering_ckpt_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch Lightning Checkpoint.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__a = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 337 | 1 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
while a != 0:
lowercase , lowercase : int = b % a, a
return b
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
if gcd(_UpperCamelCase, _UpperCamelCase ) != 1:
lowercase : Tuple = f"""mod inverse of {a!r} and {m!r} does not exist"""
raise ValueError(_UpperCamelCase )
lowercase , lowercase , lowercase : Union[str, Any] = 1, 0, a
lowercase , lowercase , lowercase : int = 0, 1, m
while va != 0:
lowercase : Optional[Any] = ua // va
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase : Optional[int] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 337 |
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
__a = logging.get_logger(__name__)
__a = {
'''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Any = 'yolos'
def __init__( self , SCREAMING_SNAKE_CASE__=768 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=3072 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=[512, 864] , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=100 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = hidden_size
lowercase : int = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : str = intermediate_size
lowercase : Dict = hidden_act
lowercase : int = hidden_dropout_prob
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : List[Any] = initializer_range
lowercase : Optional[int] = layer_norm_eps
lowercase : str = image_size
lowercase : Dict = patch_size
lowercase : str = num_channels
lowercase : Optional[int] = qkv_bias
lowercase : List[str] = num_detection_tokens
lowercase : List[str] = use_mid_position_embeddings
lowercase : Dict = auxiliary_loss
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : Any = bbox_cost
lowercase : int = giou_cost
# Loss coefficients
lowercase : Dict = bbox_loss_coefficient
lowercase : Optional[Any] = giou_loss_coefficient
lowercase : Tuple = eos_coefficient
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = version.parse('1.11' )
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
return 1E-4
@property
def __lowerCamelCase ( self ):
return 12
| 337 | 1 |
def __lowercase ( _UpperCamelCase = 100 ) ->int:
"""simple docstring"""
lowercase : List[Any] = (n * (n + 1) // 2) ** 2
lowercase : Any = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__a = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Optional[int]:
"""simple docstring"""
if got_ver is None or want_ver is None:
raise ValueError(
f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
f""" reinstalling {pkg}.""" )
if not ops[op](version.parse(_UpperCamelCase ), version.parse(_UpperCamelCase ) ):
raise ImportError(
f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None ) ->None:
"""simple docstring"""
lowercase : List[Any] = f"""\n{hint}""" if hint is not None else ''''''
# non-versioned check
if re.match(R'''^[\w_\-\d]+$''', _UpperCamelCase ):
lowercase , lowercase , lowercase : Optional[Any] = requirement, None, None
else:
lowercase : List[Any] = re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
f""" got {requirement}""" )
lowercase , lowercase : str = match[0]
lowercase : Tuple = want_full.split(''',''' ) # there could be multiple requirements
lowercase : List[Any] = {}
for w in want_range:
lowercase : str = re.findall(R'''^([\s!=<>]{1,2})(.+)''', _UpperCamelCase )
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
f""" but got {requirement}""" )
lowercase , lowercase : Optional[int] = match[0]
lowercase : Dict = want_ver
if op not in ops:
raise ValueError(f"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )
# special case
if pkg == "python":
lowercase : int = '''.'''.join([str(_UpperCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
return
# check if any version is installed
try:
lowercase : List[str] = importlib.metadata.version(_UpperCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
f"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : Optional[int] = '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
return require_version(_UpperCamelCase, _UpperCamelCase )
| 337 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__a = 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_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__a = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self ):
lowercase : str = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
lowercase : Any = self.diffusers_dir
shutil.copy(
os.path.join(SCREAMING_SNAKE_CASE__ , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def __lowerCamelCase ( self ):
lowercase : List[Any] = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
lowercase : Tuple = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
lowercase : str = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
lowercase : Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowercase : List[Any] = black.format_str(SCREAMING_SNAKE_CASE__ , mode=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , newline='''\n''' ) as f:
f.write(SCREAMING_SNAKE_CASE__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , '''r''' ) as f:
self.assertTrue(f.read() , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Tuple = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with a really long name
lowercase : List[Any] = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , overwrite_result=re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
| 337 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'deta'
A : Dict = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=900 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=1024 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="relu" , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="sine" , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=300 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.25 , **SCREAMING_SNAKE_CASE__ , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase : Tuple = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = backbone_config.pop('''model_type''' )
lowercase : Any = CONFIG_MAPPING[backbone_model_type]
lowercase : List[Any] = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = backbone_config
lowercase : Union[str, Any] = num_queries
lowercase : Any = max_position_embeddings
lowercase : int = d_model
lowercase : Any = encoder_ffn_dim
lowercase : Optional[int] = encoder_layers
lowercase : Tuple = encoder_attention_heads
lowercase : Optional[Any] = decoder_ffn_dim
lowercase : Optional[int] = decoder_layers
lowercase : int = decoder_attention_heads
lowercase : Any = dropout
lowercase : int = attention_dropout
lowercase : Dict = activation_dropout
lowercase : int = activation_function
lowercase : Dict = init_std
lowercase : List[str] = init_xavier_std
lowercase : Optional[Any] = encoder_layerdrop
lowercase : Tuple = auxiliary_loss
lowercase : Tuple = position_embedding_type
# deformable attributes
lowercase : List[str] = num_feature_levels
lowercase : Tuple = encoder_n_points
lowercase : Optional[int] = decoder_n_points
lowercase : Tuple = two_stage
lowercase : Optional[Any] = two_stage_num_proposals
lowercase : Union[str, Any] = with_box_refine
lowercase : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : str = bbox_cost
lowercase : List[Any] = giou_cost
# Loss coefficients
lowercase : Tuple = mask_loss_coefficient
lowercase : Any = dice_loss_coefficient
lowercase : Dict = bbox_loss_coefficient
lowercase : Tuple = giou_loss_coefficient
lowercase : Union[str, Any] = eos_coefficient
lowercase : Tuple = focal_alpha
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __lowerCamelCase ( self ):
return self.encoder_attention_heads
@property
def __lowerCamelCase ( self ):
return self.d_model
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase : Any = self.backbone_config.to_dict()
lowercase : List[str] = self.__class__.model_type
return output
| 337 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : List[str] = 'deta'
A : Dict = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=900 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=2048 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=1024 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="relu" , SCREAMING_SNAKE_CASE__=256 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="sine" , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=300 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.25 , **SCREAMING_SNAKE_CASE__ , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase : Tuple = CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
else:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = backbone_config.pop('''model_type''' )
lowercase : Any = CONFIG_MAPPING[backbone_model_type]
lowercase : List[Any] = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = backbone_config
lowercase : Union[str, Any] = num_queries
lowercase : Any = max_position_embeddings
lowercase : int = d_model
lowercase : Any = encoder_ffn_dim
lowercase : Optional[int] = encoder_layers
lowercase : Tuple = encoder_attention_heads
lowercase : Optional[Any] = decoder_ffn_dim
lowercase : Optional[int] = decoder_layers
lowercase : int = decoder_attention_heads
lowercase : Any = dropout
lowercase : int = attention_dropout
lowercase : Dict = activation_dropout
lowercase : int = activation_function
lowercase : Dict = init_std
lowercase : List[str] = init_xavier_std
lowercase : Optional[Any] = encoder_layerdrop
lowercase : Tuple = auxiliary_loss
lowercase : Tuple = position_embedding_type
# deformable attributes
lowercase : List[str] = num_feature_levels
lowercase : Tuple = encoder_n_points
lowercase : Optional[int] = decoder_n_points
lowercase : Tuple = two_stage
lowercase : Optional[Any] = two_stage_num_proposals
lowercase : Union[str, Any] = with_box_refine
lowercase : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
lowercase : Optional[Any] = class_cost
lowercase : str = bbox_cost
lowercase : List[Any] = giou_cost
# Loss coefficients
lowercase : Tuple = mask_loss_coefficient
lowercase : Any = dice_loss_coefficient
lowercase : Dict = bbox_loss_coefficient
lowercase : Tuple = giou_loss_coefficient
lowercase : Union[str, Any] = eos_coefficient
lowercase : Tuple = focal_alpha
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __lowerCamelCase ( self ):
return self.encoder_attention_heads
@property
def __lowerCamelCase ( self ):
return self.d_model
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase : Any = self.backbone_config.to_dict()
lowercase : List[str] = self.__class__.model_type
return output
| 337 |
def __lowercase ( ) ->List[Any]:
"""simple docstring"""
lowercase : Union[str, Any] = 0
for i in range(1, 1001 ):
total += i**i
return str(_UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 337 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__a = {
'''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
'''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
'''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
'''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
'''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
'''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
'''mask_downscaling.0''': '''mask_embed.conv1''',
'''mask_downscaling.1''': '''mask_embed.layer_norm1''',
'''mask_downscaling.3''': '''mask_embed.conv2''',
'''mask_downscaling.4''': '''mask_embed.layer_norm2''',
'''mask_downscaling.6''': '''mask_embed.conv3''',
'''point_embeddings''': '''point_embed''',
'''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
'''image_encoder''': '''vision_encoder''',
'''neck.0''': '''neck.conv1''',
'''neck.1''': '''neck.layer_norm1''',
'''neck.2''': '''neck.conv2''',
'''neck.3''': '''neck.layer_norm2''',
'''patch_embed.proj''': '''patch_embed.projection''',
'''.norm''': '''.layer_norm''',
'''blocks''': '''layers''',
}
def __lowercase ( _UpperCamelCase ) ->Tuple:
"""simple docstring"""
lowercase : List[str] = {}
state_dict.pop('''pixel_mean''', _UpperCamelCase )
state_dict.pop('''pixel_std''', _UpperCamelCase )
lowercase : List[Any] = R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
lowercase : Any = key.replace(_UpperCamelCase, _UpperCamelCase )
if re.match(_UpperCamelCase, _UpperCamelCase ):
lowercase : Dict = int(re.match(_UpperCamelCase, _UpperCamelCase ).group(2 ) )
if layer_nb == 0:
lowercase : List[Any] = key.replace('''layers.0''', '''proj_in''' )
elif layer_nb == 1:
lowercase : List[Any] = key.replace('''layers.1''', '''layers.0''' )
elif layer_nb == 2:
lowercase : Optional[Any] = key.replace('''layers.2''', '''proj_out''' )
lowercase : Optional[Any] = value
lowercase : Dict = model_state_dict[
'''prompt_encoder.shared_embedding.positional_embedding'''
]
return model_state_dict
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase="ybelkada/segment-anything" ) ->Optional[Any]:
"""simple docstring"""
lowercase : List[Any] = hf_hub_download(_UpperCamelCase, f"""checkpoints/{model_name}.pth""" )
if "sam_vit_b" in model_name:
lowercase : Dict = SamConfig()
elif "sam_vit_l" in model_name:
lowercase : Optional[int] = SamVisionConfig(
hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, global_attn_indexes=[5, 11, 17, 23], )
lowercase : Any = SamConfig(
vision_config=_UpperCamelCase, )
elif "sam_vit_h" in model_name:
lowercase : Union[str, Any] = SamVisionConfig(
hidden_size=1280, num_hidden_layers=32, num_attention_heads=16, global_attn_indexes=[7, 15, 23, 31], )
lowercase : List[str] = SamConfig(
vision_config=_UpperCamelCase, )
lowercase : Dict = torch.load(_UpperCamelCase, map_location='''cpu''' )
lowercase : List[str] = replace_keys(_UpperCamelCase )
lowercase : Tuple = SamImageProcessor()
lowercase : int = SamProcessor(image_processor=_UpperCamelCase )
lowercase : Optional[Any] = SamModel(_UpperCamelCase )
hf_model.load_state_dict(_UpperCamelCase )
lowercase : Dict = hf_model.to('''cuda''' )
lowercase : str = '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
lowercase : int = Image.open(requests.get(_UpperCamelCase, stream=_UpperCamelCase ).raw ).convert('''RGB''' )
lowercase : Union[str, Any] = [[[400, 650]]]
lowercase : List[Any] = [[1]]
lowercase : List[str] = processor(images=np.array(_UpperCamelCase ), return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
lowercase : List[str] = hf_model(**_UpperCamelCase )
lowercase : Any = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8
lowercase : int = processor(
images=np.array(_UpperCamelCase ), input_points=_UpperCamelCase, input_labels=_UpperCamelCase, return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
lowercase : str = hf_model(**_UpperCamelCase )
lowercase : int = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4
lowercase : Tuple = ((75, 275, 1725, 850),)
lowercase : Dict = processor(images=np.array(_UpperCamelCase ), input_boxes=_UpperCamelCase, return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
lowercase : int = hf_model(**_UpperCamelCase )
lowercase : Dict = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4
# Test with 2 points and 1 image.
lowercase : Optional[Any] = [[[400, 650], [800, 650]]]
lowercase : int = [[1, 1]]
lowercase : Dict = processor(
images=np.array(_UpperCamelCase ), input_points=_UpperCamelCase, input_labels=_UpperCamelCase, return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
lowercase : Optional[int] = hf_model(**_UpperCamelCase )
lowercase : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2
if __name__ == "__main__":
__a = argparse.ArgumentParser()
__a = ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
parser.add_argument(
'''--model_name''',
default='''sam_vit_h_4b8939''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
parser.add_argument(
'''--model_hub_id''',
default='''ybelkada/segment-anything''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
__a = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 337 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__a = 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_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__a = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self ):
lowercase : str = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
lowercase : Any = self.diffusers_dir
shutil.copy(
os.path.join(SCREAMING_SNAKE_CASE__ , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def __lowerCamelCase ( self ):
lowercase : List[Any] = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
lowercase : Tuple = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
lowercase : str = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
lowercase : Any = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowercase : List[Any] = black.format_str(SCREAMING_SNAKE_CASE__ , mode=SCREAMING_SNAKE_CASE__ )
lowercase : Dict = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(SCREAMING_SNAKE_CASE__ , '''w''' , newline='''\n''' ) as f:
f.write(SCREAMING_SNAKE_CASE__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , '''r''' ) as f:
self.assertTrue(f.read() , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Tuple = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with a really long name
lowercase : List[Any] = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , SCREAMING_SNAKE_CASE__ , overwrite_result=re.sub('''DDPM''' , '''Test''' , SCREAMING_SNAKE_CASE__ ) , )
| 337 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__ , ):
lowercase : Optional[Any] = parent
lowercase : Any = 13
lowercase : List[str] = 7
lowercase : Union[str, Any] = True
lowercase : int = True
lowercase : int = True
lowercase : int = 99
lowercase : Optional[Any] = 32
lowercase : List[Any] = 2
lowercase : Any = 4
lowercase : List[str] = 37
lowercase : Any = '''gelu'''
lowercase : Optional[Any] = 0.1
lowercase : Optional[Any] = 0.1
lowercase : List[str] = 512
lowercase : List[str] = 16
lowercase : List[Any] = 2
lowercase : Tuple = 0.02
lowercase : List[Any] = 3
lowercase : str = 4
lowercase : Dict = None
def __lowerCamelCase ( self ):
lowercase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Union[str, Any] = None
if self.use_input_mask:
lowercase : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowercase : Any = None
lowercase : Tuple = None
lowercase : List[Any] = None
if self.use_labels:
lowercase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : int = ids_tensor([self.batch_size] , self.num_choices )
lowercase : Dict = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self ):
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) : str = self.prepare_config_and_inputs()
lowercase : List[str] = True
lowercase : str = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = TFEsmModel(config=SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = [input_ids, input_mask]
lowercase : List[str] = model(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
lowercase : List[str] = True
lowercase : List[Any] = TFEsmModel(config=SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''encoder_hidden_states''': encoder_hidden_states,
'''encoder_attention_mask''': encoder_attention_mask,
}
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = [input_ids, input_mask]
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ )
# Also check the case where encoder outputs are not passed
lowercase : Optional[int] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = TFEsmForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
lowercase : int = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = self.num_labels
lowercase : List[str] = TFEsmForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
lowercase : List[Any] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) : List[str] = config_and_inputs
lowercase : List[str] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
A : Any = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
A : List[str] = (
{
'feature-extraction': TFEsmModel,
'fill-mask': TFEsmForMaskedLM,
'text-classification': TFEsmForSequenceClassification,
'token-classification': TFEsmForTokenClassification,
'zero-shot': TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
A : Optional[Any] = False
A : Optional[Any] = False
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = TFEsmModelTester(self )
lowercase : int = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , hidden_size=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : int = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCamelCase ( self ):
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : str = TFEsmModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@unittest.skip('''Protein models do not support embedding resizing.''' )
def __lowerCamelCase ( self ):
pass
@unittest.skip('''Protein models do not support embedding resizing.''' )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
lowercase , lowercase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Tuple = model_class(SCREAMING_SNAKE_CASE__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
lowercase : Optional[Any] = model.get_bias()
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
for k, v in name.items():
assert isinstance(SCREAMING_SNAKE_CASE__ , tf.Variable )
else:
lowercase : Optional[Any] = model.get_output_embeddings()
assert x is None
lowercase : Tuple = model.get_bias()
assert name is None
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
lowercase : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase : int = model(SCREAMING_SNAKE_CASE__ )[0]
lowercase : int = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , SCREAMING_SNAKE_CASE__ )
# compare the actual values for a slice.
lowercase : Optional[Any] = tf.constant(
[
[
[8.921518, -10.589814, -6.4671307],
[-6.3967156, -13.911377, -1.1211915],
[-7.781247, -13.951557, -3.740592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2 ) )
@slow
def __lowerCamelCase ( self ):
lowercase : Dict = TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
lowercase : Any = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowercase : List[Any] = model(SCREAMING_SNAKE_CASE__ )[0]
# compare the actual values for a slice.
lowercase : int = tf.constant(
[
[
[0.14443092, 0.54125327, 0.3247739],
[0.30340484, 0.00526676, 0.31077722],
[0.32278043, -0.24987096, 0.3414628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 337 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=0 ): # a graph with Node 0,1,...,N-1
lowercase : List[Any] = n
lowercase : List[Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # adjacency matrix for weight
lowercase : Union[str, Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # dp[i][j] stores minimum distance from i to j
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : int = w
def __lowerCamelCase ( self ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase : Any = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.dp[u][v]
if __name__ == "__main__":
__a = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 337 | 1 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Any:
"""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 __lowercase ( _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))
| 337 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(_UpperCamelCase ) / len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->int:
"""simple docstring"""
assert x is not None
assert y is not None
lowercase : str = len(_UpperCamelCase )
lowercase : Optional[Any] = len(_UpperCamelCase )
# declaring the array for storing the dp values
lowercase : Any = [[0] * (n + 1) for _ in range(m + 1 )] # noqa: E741
for i in range(1, m + 1 ):
for j in range(1, n + 1 ):
lowercase : str = 1 if x[i - 1] == y[j - 1] else 0
lowercase : Optional[Any] = max(l[i - 1][j], l[i][j - 1], l[i - 1][j - 1] + match )
lowercase : Optional[Any] = ''''''
lowercase , lowercase : str = m, n
while i > 0 and j > 0:
lowercase : List[str] = 1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
lowercase : Optional[Any] = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
__a = '''AGGTAB'''
__a = '''GXTXAYB'''
__a = 4
__a = '''GTAB'''
__a , __a = longest_common_subsequence(a, b)
print('''len =''', ln, ''', sub-sequence =''', subseq)
import doctest
doctest.testmod()
| 337 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 337 | 1 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self , SCREAMING_SNAKE_CASE__=0 ): # a graph with Node 0,1,...,N-1
lowercase : List[Any] = n
lowercase : List[Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # adjacency matrix for weight
lowercase : Union[str, Any] = [
[math.inf for j in range(0 , SCREAMING_SNAKE_CASE__ )] for i in range(0 , SCREAMING_SNAKE_CASE__ )
] # dp[i][j] stores minimum distance from i to j
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : int = w
def __lowerCamelCase ( self ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowercase : Any = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return self.dp[u][v]
if __name__ == "__main__":
__a = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 337 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase ) ->List[int]:
"""simple docstring"""
if isinstance(_UpperCamelCase, np.ndarray ):
return list(tensor.shape )
lowercase : Optional[Any] = tf.shape(_UpperCamelCase )
if tensor.shape == tf.TensorShape(_UpperCamelCase ):
return dynamic
lowercase : Tuple = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_UpperCamelCase )]
def __lowercase ( _UpperCamelCase, _UpperCamelCase = None, _UpperCamelCase = None ) ->tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9, axis=_UpperCamelCase, name=_UpperCamelCase )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase=1e-5, _UpperCamelCase=-1 ) ->int:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_UpperCamelCase, _UpperCamelCase ):
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
lowercase , lowercase : Union[str, Any] = tf.nn.moments(_UpperCamelCase, axes=[axis], keepdims=_UpperCamelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase : int = [1] * inputs.shape.rank
lowercase : Union[str, Any] = shape_list(_UpperCamelCase )[axis]
lowercase : List[str] = tf.reshape(_UpperCamelCase, _UpperCamelCase )
lowercase : Dict = tf.reshape(_UpperCamelCase, _UpperCamelCase )
# Compute layer normalization using the batch_normalization
# function.
lowercase : List[str] = tf.nn.batch_normalization(
_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, offset=_UpperCamelCase, scale=_UpperCamelCase, variance_epsilon=_UpperCamelCase, )
return outputs
def __lowercase ( _UpperCamelCase, _UpperCamelCase=0, _UpperCamelCase=-1 ) ->List[Any]:
"""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
lowercase : Dict = tf.shape(_UpperCamelCase )
lowercase : Optional[Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase : List[str] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]], axis=0 )
return tf.reshape(_UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->tf.Tensor:
"""simple docstring"""
if not isinstance(_UpperCamelCase, tf.Tensor ):
lowercase : Optional[Any] = tf.convert_to_tensor(_UpperCamelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase : Tuple = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase : str = (
tf.cast(1, encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase = "input_ids" ) ->None:
"""simple docstring"""
tf.debugging.assert_less(
_UpperCamelCase, tf.cast(_UpperCamelCase, dtype=tensor.dtype ), message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(_UpperCamelCase )}) must be smaller than the embedding """
f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
), )
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]:
"""simple docstring"""
lowercase : List[Any] = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase : Optional[int] = [x for x in data if len(_UpperCamelCase ) > 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}""" )
lowercase : Any = np.asarray(_UpperCamelCase )
lowercase : List[Any] = 1
lowercase : Tuple = np.array_split(_UpperCamelCase, _UpperCamelCase )
# 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
lowercase : Dict = np.array_split(_UpperCamelCase, _UpperCamelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_UpperCamelCase ):
lowercase : Optional[int] = chunk_data
else:
lowercase : int = data
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->List[str]:
"""simple docstring"""
if name in group.attrs:
lowercase : str = [n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs[name]]
else:
lowercase : Optional[Any] = []
lowercase : List[str] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(_UpperCamelCase, '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
def _expand_single_ad_tensor(_UpperCamelCase ):
if isinstance(_UpperCamelCase, tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_UpperCamelCase, axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor, _UpperCamelCase )
| 337 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class __SCREAMING_SNAKE_CASE :
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__=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__=0.02 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=None , ):
lowercase : Optional[Any] = parent
lowercase : Optional[int] = batch_size
lowercase : List[str] = seq_length
lowercase : Dict = is_training
lowercase : str = use_token_type_ids
lowercase : str = use_labels
lowercase : Any = vocab_size
lowercase : int = hidden_size
lowercase : Tuple = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : List[Any] = intermediate_size
lowercase : Tuple = hidden_act
lowercase : List[Any] = hidden_dropout_prob
lowercase : Optional[int] = attention_probs_dropout_prob
lowercase : Any = max_position_embeddings
lowercase : Dict = type_vocab_size
lowercase : Union[str, Any] = type_sequence_label_size
lowercase : Optional[Any] = initializer_range
lowercase : Any = num_labels
lowercase : Optional[Any] = num_choices
lowercase : List[Any] = scope
lowercase : List[Any] = self.vocab_size - 1
def __lowerCamelCase ( self ):
lowercase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Optional[Any] = None
if self.use_token_type_ids:
lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase : Union[str, Any] = None
lowercase : Dict = None
lowercase : List[Any] = None
if self.use_labels:
lowercase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
lowercase : Optional[Any] = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowercase : Optional[int] = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = OpenAIGPTModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , head_mask=SCREAMING_SNAKE_CASE__ )
lowercase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ )
lowercase : Any = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = OpenAIGPTLMHeadModel(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Tuple = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ ):
lowercase : int = OpenAIGPTDoubleHeadsModel(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Any = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ ):
lowercase : List[Any] = self.num_labels
lowercase : Tuple = OpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
lowercase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : Dict = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) : Tuple = config_and_inputs
lowercase : Tuple = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( A__ , A__ , A__ , unittest.TestCase ):
A : Union[str, Any] = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
A : Dict = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
A : List[str] = (
{
'feature-extraction': OpenAIGPTModel,
'text-classification': OpenAIGPTForSequenceClassification,
'text-generation': OpenAIGPTLMHeadModel,
'zero-shot': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
lowercase : Optional[Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_labels=SCREAMING_SNAKE_CASE__ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowercase : str = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ , )
lowercase : Dict = inputs_dict['''labels''']
lowercase : List[Any] = inputs_dict['''labels''']
lowercase : Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ , )
lowercase : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ )
return inputs_dict
def __lowerCamelCase ( self ):
lowercase : Tuple = OpenAIGPTModelTester(self )
lowercase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , n_embd=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self ):
lowercase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCamelCase ( self ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : Dict = OpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __lowerCamelCase ( self ):
lowercase : Dict = OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
model.to(SCREAMING_SNAKE_CASE__ )
lowercase : Dict = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ ) # the president is
lowercase : Dict = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowercase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE__ , do_sample=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(output_ids[0].tolist() , SCREAMING_SNAKE_CASE__ )
| 337 |
def __lowercase ( _UpperCamelCase = 4000000 ) ->int:
"""simple docstring"""
lowercase : int = []
lowercase , lowercase : str = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCamelCase )
lowercase , lowercase : Dict = b, a + b
return sum(_UpperCamelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 337 | 1 |
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