code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : int = int(_lowercase )
if decimal in (0, 1): # Exit cases for the recursion
return str(_lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = divmod(_lowercase , 2 )
return binary_recursive(_lowercase ) + str(_lowercase )
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : Optional[int] = str(_lowercase ).strip()
if not number:
raise ValueError('''No input value was provided''' )
SCREAMING_SNAKE_CASE : str = '''-''' if number.startswith('''-''' ) else ''''''
SCREAMING_SNAKE_CASE : Union[str, Any] = number.lstrip('''-''' )
if not number.isnumeric():
raise ValueError('''Input value is not an integer''' )
return f"""{negative}0b{binary_recursive(int(_lowercase ) )}"""
if __name__ == "__main__":
from doctest import testmod
testmod()
| 248 | import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__UpperCamelCase : List[Any] = (
'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)
def A ( _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
return (preds == labels).mean()
def A ( _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
SCREAMING_SNAKE_CASE : int = simple_accuracy(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE : Tuple = fa_score(y_true=_lowercase , y_pred=_lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def A ( _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
SCREAMING_SNAKE_CASE : str = pearsonr(_lowercase , _lowercase )[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = spearmanr(_lowercase , _lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def A ( _lowercase , _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
assert len(_lowercase ) == len(_lowercase ), f"""Predictions and labels have mismatched lengths {len(_lowercase )} and {len(_lowercase )}"""
if task_name == "cola":
return {"mcc": matthews_corrcoef(_lowercase , _lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "mrpc":
return acc_and_fa(_lowercase , _lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(_lowercase , _lowercase )
elif task_name == "qqp":
return acc_and_fa(_lowercase , _lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
else:
raise KeyError(_lowercase )
def A ( _lowercase , _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
if len(_lowercase ) != len(_lowercase ):
raise ValueError(f"""Predictions and labels have mismatched lengths {len(_lowercase )} and {len(_lowercase )}""" )
if task_name == "xnli":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
else:
raise KeyError(_lowercase )
| 248 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
__snake_case : List[Any] = logging.getLogger(__name__)
@dataclass
class A :
__UpperCAmelCase : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__UpperCAmelCase : Optional[str] = field(
default=a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__UpperCAmelCase : Optional[str] = field(
default=a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__UpperCAmelCase : Optional[str] = field(
default=a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__UpperCAmelCase : bool = field(
default=a , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__UpperCAmelCase : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__UpperCAmelCase : bool = field(
default=a , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class A :
__UpperCAmelCase : Optional[str] = field(default=a , metadata={"""help""": """The input training data file (a text file)."""} )
__UpperCAmelCase : Optional[str] = field(
default=a , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__UpperCAmelCase : bool = field(
default=a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__UpperCAmelCase : Optional[int] = field(
default=a , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__UpperCAmelCase : Optional[int] = field(
default=a , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__UpperCAmelCase : bool = field(
default=a , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
__UpperCAmelCase : Optional[int] = field(
default=a , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__UpperCAmelCase : Optional[int] = field(
default=a , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def __lowerCAmelCase ( self ) -> Optional[Any]:
if self.train_file is not None:
_a = self.train_file.split("." )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_a = self.validation_file.split("." )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class A :
__UpperCAmelCase : PreTrainedTokenizerBase
__UpperCAmelCase : Union[bool, str, PaddingStrategy] = True
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Optional[int] = None
def __call__( self , snake_case_ ) -> Dict:
_a = "label" if "label" in features[0].keys() else "labels"
_a = [feature.pop(snake_case_ ) for feature in features]
_a = len(snake_case_ )
_a = len(features[0]["input_ids"] )
_a = [
[{k: v[i] for k, v in feature.items()} for i in range(snake_case_ )] for feature in features
]
_a = list(chain(*snake_case_ ) )
_a = self.tokenizer.pad(
snake_case_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
_a = {k: v.view(snake_case_ , snake_case_ , -1 ) for k, v in batch.items()}
# Add back labels
_a = torch.tensor(snake_case_ , dtype=torch.intaa )
return batch
def _lowercase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_a = 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.
_a , _a , _a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_a , _a , _a = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_swag", lowerCamelCase__, lowerCamelCase__ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout )], )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_a = training_args.get_process_log_level()
logger.setLevel(lowerCamelCase__ )
datasets.utils.logging.set_verbosity(lowerCamelCase__ )
transformers.utils.logging.set_verbosity(lowerCamelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_a = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_a = 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/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. 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.train_file is not None or data_args.validation_file is not None:
_a = {}
if data_args.train_file is not None:
_a = data_args.train_file
if data_args.validation_file is not None:
_a = data_args.validation_file
_a = data_args.train_file.split("." )[-1]
_a = load_dataset(
lowerCamelCase__, data_files=lowerCamelCase__, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
else:
# Downloading and loading the swag dataset from the hub.
_a = load_dataset(
"swag", "regular", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_a = 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_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
_a = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path ), config=lowerCamelCase__, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_a = [F'''ending{i}''' for i in range(4 )]
_a = "sent1"
_a = "sent2"
if data_args.max_seq_length is None:
_a = tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`." )
_a = 1_024
else:
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}.''' )
_a = min(data_args.max_seq_length, tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowerCamelCase__ : Tuple ):
_a = [[context] * 4 for context in examples[context_name]]
_a = examples[question_header_name]
_a = [
[F'''{header} {examples[end][i]}''' for end in ending_names] for i, header in enumerate(lowerCamelCase__ )
]
# Flatten out
_a = list(chain(*lowerCamelCase__ ) )
_a = list(chain(*lowerCamelCase__ ) )
# Tokenize
_a = tokenizer(
lowerCamelCase__, lowerCamelCase__, truncation=lowerCamelCase__, max_length=lowerCamelCase__, padding="max_length" if data_args.pad_to_max_length else False, )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0, len(lowerCamelCase__ ), 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset" )
_a = raw_datasets["train"]
if data_args.max_train_samples is not None:
_a = min(len(lowerCamelCase__ ), data_args.max_train_samples )
_a = train_dataset.select(range(lowerCamelCase__ ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
_a = train_dataset.map(
lowerCamelCase__, batched=lowerCamelCase__, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset" )
_a = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
_a = min(len(lowerCamelCase__ ), data_args.max_eval_samples )
_a = eval_dataset.select(range(lowerCamelCase__ ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
_a = eval_dataset.map(
lowerCamelCase__, batched=lowerCamelCase__, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, )
# Data collator
_a = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowerCamelCase__, pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowerCamelCase__ : int ):
_a , _a = eval_predictions
_a = np.argmax(lowerCamelCase__, axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_a = Trainer(
model=lowerCamelCase__, args=lowerCamelCase__, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=lowerCamelCase__, data_collator=lowerCamelCase__, compute_metrics=lowerCamelCase__, )
# Training
if training_args.do_train:
_a = None
if training_args.resume_from_checkpoint is not None:
_a = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_a = last_checkpoint
_a = trainer.train(resume_from_checkpoint=lowerCamelCase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
_a = train_result.metrics
_a = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase__ )
)
_a = min(lowerCamelCase__, len(lowerCamelCase__ ) )
trainer.log_metrics("train", lowerCamelCase__ )
trainer.save_metrics("train", lowerCamelCase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_a = trainer.evaluate()
_a = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase__ )
_a = min(lowerCamelCase__, len(lowerCamelCase__ ) )
trainer.log_metrics("eval", lowerCamelCase__ )
trainer.save_metrics("eval", lowerCamelCase__ )
_a = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCamelCase__ )
else:
trainer.create_model_card(**lowerCamelCase__ )
def _lowercase ( lowerCamelCase__ : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 691 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
__snake_case : List[Any] = [
"EAGER",
"AOT_EAGER",
"INDUCTOR",
"NVFUSER",
"AOT_NVFUSER",
"AOT_CUDAGRAPHS",
"OFI",
"FX2TRT",
"ONNXRT",
"IPEX",
]
def _lowercase ( lowerCamelCase__ : Union[str, Any], lowerCamelCase__ : Union[str, Any]=None, lowerCamelCase__ : Dict=None, lowerCamelCase__ : Optional[int]=None ):
_a = True
while ask_again:
_a = input(lowerCamelCase__ )
try:
if default is not None and len(lowerCamelCase__ ) == 0:
return default
return convert_value(lowerCamelCase__ ) if convert_value is not None else result
except Exception:
if error_message is not None:
print(lowerCamelCase__ )
def _lowercase ( lowerCamelCase__ : Optional[Any], lowerCamelCase__ : Dict=[], lowerCamelCase__ : int=None, lowerCamelCase__ : Union[str, Any]=0 ):
_a = BulletMenu(lowerCamelCase__, lowerCamelCase__ )
_a = menu.run(default_choice=lowerCamelCase__ )
return convert_value(lowerCamelCase__ ) if convert_value is not None else result
def _lowercase ( lowerCamelCase__ : str ):
_a = int(lowerCamelCase__ )
return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value] )
def _lowercase ( lowerCamelCase__ : str ):
_a = int(lowerCamelCase__ )
return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value] )
def _lowercase ( lowerCamelCase__ : Dict ):
_a = int(lowerCamelCase__ )
return DynamoBackend(DYNAMO_BACKENDS[value] ).value
def _lowercase ( lowerCamelCase__ : List[Any] ):
_a = int(lowerCamelCase__ )
return PrecisionType(["no", "fp16", "bf16", "fp8"][value] )
def _lowercase ( lowerCamelCase__ : str ):
_a = int(lowerCamelCase__ )
return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value] )
def _lowercase ( lowerCamelCase__ : str ):
return {"yes": True, "no": False}[value.lower()]
class A ( argparse.RawDescriptionHelpFormatter ):
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ) -> int:
_a = super()._format_usage(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
_a = usage.replace("<command> [<args>] " , "" )
return usage
| 691 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=7 , __a=6 , __a=17 , __a=23 , __a=11 , __a=True , ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = act_dim
_UpperCamelCase = state_dim
_UpperCamelCase = hidden_size
_UpperCamelCase = max_length
_UpperCamelCase = is_training
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = floats_tensor((self.batch_size, self.seq_length, self.state_dim))
_UpperCamelCase = floats_tensor((self.batch_size, self.seq_length, self.act_dim))
_UpperCamelCase = floats_tensor((self.batch_size, self.seq_length, 1))
_UpperCamelCase = floats_tensor((self.batch_size, self.seq_length, 1))
_UpperCamelCase = ids_tensor((self.batch_size, self.seq_length) , vocab_size=10_00)
_UpperCamelCase = random_attention_mask((self.batch_size, self.seq_length))
_UpperCamelCase = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a , ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = DecisionTransformerModel(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a , __a , __a , __a , __a , __a)
self.parent.assertEqual(result.state_preds.shape , states.shape)
self.parent.assertEqual(result.action_preds.shape , actions.shape)
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size)) # seq length *3 as there are 3 modelities: states, returns and actions
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (DecisionTransformerModel,) if is_torch_available() else ()
lowercase__ = ()
lowercase__ = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
lowercase__ = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = DecisionTransformerModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
@slow
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = DecisionTransformerModel.from_pretrained(__a)
self.assertIsNotNone(__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(__a)] , __a)
@require_torch
class _UpperCAmelCase( unittest.TestCase ):
@slow
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = 2 # number of steps of autoregressive prediction we will perform
_UpperCamelCase = 10 # defined by the RL environment, may be normalized
_UpperCamelCase = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''')
_UpperCamelCase = model.to(__a)
_UpperCamelCase = model.config
torch.manual_seed(0)
_UpperCamelCase = torch.randn(1 , 1 , config.state_dim).to(device=__a , dtype=torch.floataa) # env.reset()
_UpperCamelCase = torch.tensor(
[[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]] , device=__a)
_UpperCamelCase = torch.tensor(__a , device=__a , dtype=torch.floataa).reshape(1 , 1 , 1)
_UpperCamelCase = state
_UpperCamelCase = torch.zeros(1 , 0 , config.act_dim , device=__a , dtype=torch.floataa)
_UpperCamelCase = torch.zeros(1 , 0 , device=__a , dtype=torch.floataa)
_UpperCamelCase = torch.tensor(0 , device=__a , dtype=torch.long).reshape(1 , 1)
for step in range(__a):
_UpperCamelCase = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=__a)] , dim=1)
_UpperCamelCase = torch.cat([rewards, torch.zeros(1 , 1 , device=__a)] , dim=1)
_UpperCamelCase = torch.ones(1 , states.shape[1]).to(dtype=torch.long , device=states.device)
with torch.no_grad():
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = model(
states=__a , actions=__a , rewards=__a , returns_to_go=__a , timesteps=__a , attention_mask=__a , return_dict=__a , )
self.assertEqual(action_pred.shape , actions.shape)
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1e-4))
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim).to(device=__a , dtype=torch.floataa),
1.0,
False,
{},
)
_UpperCamelCase = action_pred[0, -1]
_UpperCamelCase = torch.cat([states, state] , dim=1)
_UpperCamelCase = returns_to_go[0, -1] - reward
_UpperCamelCase = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1)] , dim=1)
_UpperCamelCase = torch.cat(
[timesteps, torch.ones((1, 1) , device=__a , dtype=torch.long) * (step + 1)] , dim=1)
| 19 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
"""processing_trocr""": ["""TrOCRProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TrOCRForCausalLM""",
"""TrOCRPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 | 0 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_snake_case : Dict = get_logger(__name__)
class A ( enum.Enum ):
lowercase_ = 'all_checks'
lowercase_ = 'basic_checks'
lowercase_ = 'no_checks'
class A ( _a ):
pass
class A ( _a ):
pass
class A ( _a ):
pass
class A ( _a ):
pass
def snake_case_ (UpperCamelCase : Optional[dict] , UpperCamelCase : dict , UpperCamelCase : List[str]=None ):
'''simple docstring'''
if expected_checksums is None:
logger.info('''Unable to verify checksums.''' )
return
if len(set(UpperCamelCase ) - set(UpperCamelCase ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(UpperCamelCase ) - set(UpperCamelCase ) ) )
if len(set(UpperCamelCase ) - set(UpperCamelCase ) ) > 0:
raise UnexpectedDownloadedFile(str(set(UpperCamelCase ) - set(UpperCamelCase ) ) )
_a = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
_a = ''' for ''' + verification_name if verification_name is not None else ''''''
if len(UpperCamelCase ) > 0:
raise NonMatchingChecksumError(
f'Checksums didn\'t match{for_verification_name}:\n'
f'{bad_urls}\n'
'''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error''' )
logger.info('''All the checksums matched successfully''' + for_verification_name )
class A ( _a ):
pass
class A ( _a ):
pass
class A ( _a ):
pass
class A ( _a ):
pass
def snake_case_ (UpperCamelCase : Optional[dict] , UpperCamelCase : dict ):
'''simple docstring'''
if expected_splits is None:
logger.info('''Unable to verify splits sizes.''' )
return
if len(set(UpperCamelCase ) - set(UpperCamelCase ) ) > 0:
raise ExpectedMoreSplits(str(set(UpperCamelCase ) - set(UpperCamelCase ) ) )
if len(set(UpperCamelCase ) - set(UpperCamelCase ) ) > 0:
raise UnexpectedSplits(str(set(UpperCamelCase ) - set(UpperCamelCase ) ) )
_a = [
{'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(UpperCamelCase ) > 0:
raise NonMatchingSplitsSizesError(str(UpperCamelCase ) )
logger.info('''All the splits matched successfully.''' )
def snake_case_ (UpperCamelCase : str , UpperCamelCase : bool = True ):
'''simple docstring'''
if record_checksum:
_a = shaaaa()
with open(UpperCamelCase , '''rb''' ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , B'''''' ):
m.update(UpperCamelCase )
_a = m.hexdigest()
else:
_a = None
return {"num_bytes": os.path.getsize(UpperCamelCase ), "checksum": checksum}
def snake_case_ (UpperCamelCase : List[Any] ):
'''simple docstring'''
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 377 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case : str = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_snake_case : Union[str, Any] = 256047
_snake_case : Tuple = 256145
@require_sentencepiece
@require_tokenizers
class A ( _a ,unittest.TestCase ):
lowercase_ = NllbTokenizer
lowercase_ = NllbTokenizerFast
lowercase_ = True
lowercase_ = True
lowercase_ = {}
def __lowerCAmelCase ( self : List[Any] ) -> int:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_a = NllbTokenizer(lowerCAmelCase_ , keep_accents=lowerCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_a = NllbTokenizer(lowerCAmelCase_ , keep_accents=lowerCAmelCase_ )
_a = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
_a = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_a = tokenizer.convert_tokens_to_ids(lowerCAmelCase_ )
self.assertListEqual(
lowerCAmelCase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_a = tokenizer.convert_ids_to_tokens(lowerCAmelCase_ )
self.assertListEqual(
lowerCAmelCase_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def __lowerCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
_a = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_a = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
_a = self.tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
_a = tempfile.mkdtemp()
_a = tokenizer_r.save_pretrained(lowerCAmelCase_ )
_a = tokenizer_p.save_pretrained(lowerCAmelCase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
_a = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# Checks everything loads correctly in the same way
_a = tokenizer_r.from_pretrained(lowerCAmelCase_ )
_a = tokenizer_p.from_pretrained(lowerCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase_ , lowerCAmelCase_ ) )
shutil.rmtree(lowerCAmelCase_ )
# Save tokenizer rust, legacy_format=True
_a = tempfile.mkdtemp()
_a = tokenizer_r.save_pretrained(lowerCAmelCase_ , legacy_format=lowerCAmelCase_ )
_a = tokenizer_p.save_pretrained(lowerCAmelCase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# Checks everything loads correctly in the same way
_a = tokenizer_r.from_pretrained(lowerCAmelCase_ )
_a = tokenizer_p.from_pretrained(lowerCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase_ , lowerCAmelCase_ ) )
shutil.rmtree(lowerCAmelCase_ )
# Save tokenizer rust, legacy_format=False
_a = tempfile.mkdtemp()
_a = tokenizer_r.save_pretrained(lowerCAmelCase_ , legacy_format=lowerCAmelCase_ )
_a = tokenizer_p.save_pretrained(lowerCAmelCase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_a = tokenizer_r.from_pretrained(lowerCAmelCase_ )
_a = tokenizer_p.from_pretrained(lowerCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase_ , lowerCAmelCase_ ) )
shutil.rmtree(lowerCAmelCase_ )
@require_torch
def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
if not self.test_seqaseq:
return
_a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
_a = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
''' will only worsen the violence and misery for millions of people.''',
]
_a = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
try:
_a = tokenizer.prepare_seqaseq_batch(
src_texts=lowerCAmelCase_ , tgt_texts=lowerCAmelCase_ , max_length=3 , max_target_length=10 , return_tensors='''pt''' , src_lang='''eng_Latn''' , tgt_lang='''ron_Latn''' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
_a = tokenizer.prepare_seqaseq_batch(
lowerCAmelCase_ , tgt_texts=lowerCAmelCase_ , max_length=3 , return_tensors='''pt''' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_a = tokenizer.prepare_seqaseq_batch(
src_texts=lowerCAmelCase_ , max_length=3 , max_target_length=10 , return_tensors='''pt''' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('''decoder_input_ids''' , lowerCAmelCase_ )
@unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.''' )
def __lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_a = [AddedToken('''<special>''' , lstrip=lowerCAmelCase_ )]
_a = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase_ , additional_special_tokens=lowerCAmelCase_ , **lowerCAmelCase_ )
_a = tokenizer_r.encode('''Hey this is a <special> token''' )
_a = tokenizer_r.encode('''<special>''' , add_special_tokens=lowerCAmelCase_ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_a = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase_ , additional_special_tokens=lowerCAmelCase_ , **lowerCAmelCase_ , )
_a = self.tokenizer_class.from_pretrained(
lowerCAmelCase_ , additional_special_tokens=lowerCAmelCase_ , **lowerCAmelCase_ )
_a = tokenizer_p.encode('''Hey this is a <special> token''' )
_a = tokenizer_cr.encode('''Hey this is a <special> token''' )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class A ( unittest.TestCase ):
lowercase_ = 'facebook/nllb-200-distilled-600M'
lowercase_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
lowercase_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
lowercase_ = [
25_6047,
1_6297,
13_4408,
8165,
24_8066,
1_4734,
950,
1135,
10_5721,
3573,
83,
2_7352,
108,
4_9486,
2,
]
@classmethod
def __lowerCAmelCase ( cls : Tuple ) -> Optional[int]:
"""simple docstring"""
_a = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''eng_Latn''' , tgt_lang='''ron_Latn''' )
_a = 1
return cls
def __lowerCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab'''] , 25_60_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn'''] , 25_60_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn'''] , 25_60_57 )
def __lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
_a = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase_ )
def __lowerCAmelCase ( self : int ) -> List[Any]:
"""simple docstring"""
self.assertIn(lowerCAmelCase_ , self.tokenizer.all_special_ids )
# fmt: off
_a = [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
# fmt: on
_a = self.tokenizer.decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_ )
_a = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertNotIn(self.tokenizer.eos_token , lowerCAmelCase_ )
def __lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
_a = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , lowerCAmelCase_ )
_a = 10
_a = self.tokenizer(lowerCAmelCase_ , max_length=lowerCAmelCase_ , truncation=lowerCAmelCase_ ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , lowerCAmelCase_ )
self.assertEqual(len(lowerCAmelCase_ ) , lowerCAmelCase_ )
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [25_62_03, 3] )
def __lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
_a = tempfile.mkdtemp()
_a = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowerCAmelCase_ )
_a = NllbTokenizer.from_pretrained(lowerCAmelCase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowerCAmelCase_ )
@require_torch
def __lowerCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
_a = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
_a = shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['''ron_Latn'''] )
self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
_a = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def __lowerCAmelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
_a = self.tokenizer(self.src_text , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=3 , return_tensors='''pt''' )
_a = self.tokenizer(
text_target=self.tgt_text , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=10 , return_tensors='''pt''' )
_a = targets['''input_ids''']
_a = shift_tokens_right(
lowerCAmelCase_ , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
nested_simplify(lowerCAmelCase_ ) , {
# A, test, EOS, en_XX
'''input_ids''': [[25_60_47, 70, 73_56, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 25_60_57,
} , )
@require_torch
def __lowerCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
_a = True
_a = self.tokenizer(
'''UN Chief says there is no military solution in Syria''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
inputs.input_ids , [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47] )
_a = False
_a = self.tokenizer(
'''UN Chief says there is no military solution in Syria''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
inputs.input_ids , [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2] )
| 377 | 1 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format='%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=os.environ.get('LOGLEVEL', 'INFO').upper(),
stream=sys.stdout,
)
lowerCAmelCase_ = logging.getLogger(__name__)
lowerCAmelCase_ = {"facebook/bart-base": BartForConditionalGeneration}
lowerCAmelCase_ = {"facebook/bart-base": BartTokenizer}
def __UpperCAmelCase ( ) -> List[Any]:
lowercase__ : str = argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.''' )
parser.add_argument(
'''--validation_file''' , type=A__ , default=A__ , help='''A csv or a json file containing the validation data.''' )
parser.add_argument(
'''--max_length''' , type=A__ , default=5 , help='''The maximum total input sequence length after tokenization.''' , )
parser.add_argument(
'''--num_beams''' , type=A__ , default=A__ , help=(
'''Number of beams to use for evaluation. This argument will be '''
'''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
) , )
parser.add_argument(
'''--model_name_or_path''' , type=A__ , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=A__ , )
parser.add_argument(
'''--config_name''' , type=A__ , default=A__ , help='''Pretrained config name or path if not the same as model_name''' , )
parser.add_argument(
'''--device''' , type=A__ , default='''cpu''' , help='''Device where the model will be run''' , )
parser.add_argument('''--output_file_path''' , type=A__ , default=A__ , help='''Where to store the final ONNX file.''' )
lowercase__ : int = parser.parse_args()
return args
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase="cpu" ) -> Optional[Any]:
lowercase__ : Any = model_dict[model_name].from_pretrained(A__ ).to(A__ )
lowercase__ : List[Any] = tokenizer_dict[model_name].from_pretrained(A__ )
if model_name in ["facebook/bart-base"]:
lowercase__ : Any = 0
lowercase__ : Tuple = None
lowercase__ : Optional[Any] = 0
return huggingface_model, tokenizer
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
model.eval()
lowercase__ : Dict = None
lowercase__ : str = torch.jit.script(BARTBeamSearchGenerator(A__ ) )
with torch.no_grad():
lowercase__ : List[Any] = '''My friends are cool but they eat too many carbs.'''
lowercase__ : Optional[Any] = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=10_24 , return_tensors='''pt''' ).to(model.device )
lowercase__ : Tuple = model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , num_beams=A__ , max_length=A__ , early_stopping=A__ , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
A__ , (
inputs['''input_ids'''],
inputs['''attention_mask'''],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , A__ , opset_version=14 , input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id'''] , output_names=['''output_ids'''] , dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''seq'''},
'''output_ids''': {0: '''batch''', 1: '''seq_out'''},
} , example_outputs=A__ , )
logger.info('''Model exported to {}'''.format(A__ ) )
lowercase__ : int = remove_dup_initializers(os.path.abspath(A__ ) )
logger.info('''Deduplicated and optimized model written to {}'''.format(A__ ) )
lowercase__ : List[Any] = onnxruntime.InferenceSession(A__ )
lowercase__ : int = ort_sess.run(
A__ , {
'''input_ids''': inputs['''input_ids'''].cpu().numpy(),
'''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
'''num_beams''': np.array(A__ ),
'''max_length''': np.array(A__ ),
'''decoder_start_token_id''': np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1E-3 , atol=1E-3 )
logger.info('''Model outputs from torch and ONNX Runtime are similar.''' )
logger.info('''Success.''' )
def __UpperCAmelCase ( ) -> Dict:
lowercase__ : str = parse_args()
lowercase__ : List[Any] = 5
lowercase__ : Dict = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
lowercase__ : Any = torch.device(args.device )
lowercase__ , lowercase__ : List[Any] = load_model_tokenizer(args.model_name_or_path , A__ )
if model.config.decoder_start_token_id is None:
raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined''' )
model.to(A__ )
if args.max_length:
lowercase__ : int = args.max_length
if args.num_beams:
lowercase__ : List[Any] = args.num_beams
if args.output_file_path:
lowercase__ : Union[str, Any] = args.output_file_path
else:
lowercase__ : str = '''BART.onnx'''
logger.info('''Exporting model to ONNX''' )
export_and_validate_model(A__ , A__ , A__ , A__ , A__ )
if __name__ == "__main__":
main()
| 560 |
'''simple docstring'''
from collections import deque
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = process_name # process name
UpperCamelCase = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
UpperCamelCase = arrival_time
UpperCamelCase = burst_time # remaining burst time
UpperCamelCase = 0 # total time of the process wait in ready queue
UpperCamelCase = 0 # time from arrival time to completion time
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : str , UpperCamelCase__ : int , UpperCamelCase__ : list[int] , UpperCamelCase__ : deque[Process] , UpperCamelCase__ : int , ):
"""simple docstring"""
UpperCamelCase = number_of_queues
# time slice of queues that round robin algorithm applied
UpperCamelCase = time_slices
# unfinished process is in this ready_queue
UpperCamelCase = queue
# current time
UpperCamelCase = current_time
# finished process is in this sequence queue
UpperCamelCase = deque()
def A ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def A ( self : Optional[Any] , UpperCamelCase__ : list[Process] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(len(UpperCamelCase__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def A ( self : Dict , UpperCamelCase__ : list[Process] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(len(UpperCamelCase__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def A ( self : int , UpperCamelCase__ : list[Process] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(len(UpperCamelCase__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def A ( self : Optional[int] , UpperCamelCase__ : deque[Process] ):
"""simple docstring"""
return [q.burst_time for q in queue]
def A ( self : Any , UpperCamelCase__ : Process ):
"""simple docstring"""
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def A ( self : Union[str, Any] , UpperCamelCase__ : deque[Process] ):
"""simple docstring"""
UpperCamelCase = deque() # sequence deque of finished process
while len(UpperCamelCase__ ) != 0:
UpperCamelCase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(UpperCamelCase__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
UpperCamelCase = 0
# set the process's turnaround time because it is finished
UpperCamelCase = self.current_time - cp.arrival_time
# set the completion time
UpperCamelCase = self.current_time
# add the process to queue that has finished queue
finished.append(UpperCamelCase__ )
self.finish_queue.extend(UpperCamelCase__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def A ( self : Union[str, Any] , UpperCamelCase__ : deque[Process] , UpperCamelCase__ : int ):
"""simple docstring"""
UpperCamelCase = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(UpperCamelCase__ ) ):
UpperCamelCase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(UpperCamelCase__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
UpperCamelCase = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(UpperCamelCase__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
UpperCamelCase = 0
# set the finish time
UpperCamelCase = self.current_time
# update the process' turnaround time because it is finished
UpperCamelCase = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(UpperCamelCase__ )
self.finish_queue.extend(UpperCamelCase__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def A ( self : Any ):
"""simple docstring"""
for i in range(self.number_of_queues - 1 ):
UpperCamelCase , UpperCamelCase = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
_lowerCamelCase : Optional[Any] = Process("P1", 0, 53)
_lowerCamelCase : List[str] = Process("P2", 0, 17)
_lowerCamelCase : Optional[int] = Process("P3", 0, 68)
_lowerCamelCase : Dict = Process("P4", 0, 24)
_lowerCamelCase : Union[str, Any] = 3
_lowerCamelCase : int = [17, 25]
_lowerCamelCase : List[Any] = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
_lowerCamelCase : Dict = Process("P1", 0, 53)
_lowerCamelCase : int = Process("P2", 0, 17)
_lowerCamelCase : Union[str, Any] = Process("P3", 0, 68)
_lowerCamelCase : str = Process("P4", 0, 24)
_lowerCamelCase : Optional[Any] = 3
_lowerCamelCase : Any = [17, 25]
_lowerCamelCase : str = deque([Pa, Pa, Pa, Pa])
_lowerCamelCase : List[Any] = MLFQ(number_of_queues, time_slices, queue, 0)
_lowerCamelCase : int = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f'''waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print completion times of processes(P1, P2, P3, P4)
print(
f'''completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f'''turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print sequence of finished processes
print(
f'''sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}'''
)
| 430 | 0 |
import pytest
lowerCamelCase : Optional[int] = "__dummy_dataset1__"
lowerCamelCase : Tuple = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] , lowercase : Optional[int] , lowercase : List[Any] ):
'''simple docstring'''
lowerCamelCase_ = dataset_loading_script_name
lowerCamelCase_ = tmp_path / 'datasets' / script_name
script_dir.mkdir(parents=lowercase )
lowerCamelCase_ = script_dir / f"""{script_name}.py"""
with open(lowercase , 'w' ) as f:
f.write(lowercase )
return str(lowercase )
| 716 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : List[str] = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = '''gpt_neox_japanese'''
def __init__( self : int , A_ : Dict=32000 , A_ : List[Any]=2560 , A_ : Dict=32 , A_ : Union[str, Any]=32 , A_ : List[Any]=4 , A_ : List[str]="gelu" , A_ : Dict=1.00 , A_ : int=10000 , A_ : Dict=2048 , A_ : Dict=0.02 , A_ : Any=1E-5 , A_ : Union[str, Any]=True , A_ : int=31996 , A_ : List[str]=31999 , A_ : List[Any]=0.1 , A_ : List[Any]=0.0 , **A_ : Tuple , ) -> Dict:
"""simple docstring"""
super().__init__(bos_token_id=A_ , eos_token_id=A_ , **A_ )
lowerCamelCase_ = vocab_size
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_multiple_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = rotary_pct
lowerCamelCase_ = rotary_emb_base
lowerCamelCase_ = initializer_range
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = use_cache
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = hidden_dropout
| 651 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Any = logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class UpperCamelCase_ ( __UpperCamelCase ):
'''simple docstring'''
UpperCamelCase_ = '''data2vec-audio'''
def __init__( self , UpperCamelCase=32 , UpperCamelCase=7_68 , UpperCamelCase=12 , UpperCamelCase=12 , UpperCamelCase=30_72 , UpperCamelCase="gelu" , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=0.0 , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=0.02 , UpperCamelCase=1E-5 , UpperCamelCase="gelu" , UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) , UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) , UpperCamelCase=False , UpperCamelCase=16 , UpperCamelCase=19 , UpperCamelCase=5 , UpperCamelCase=0.05 , UpperCamelCase=10 , UpperCamelCase=2 , UpperCamelCase=0.0 , UpperCamelCase=10 , UpperCamelCase=0 , UpperCamelCase="sum" , UpperCamelCase=False , UpperCamelCase=False , UpperCamelCase=2_56 , UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) , UpperCamelCase=(5, 3, 3, 1, 1) , UpperCamelCase=(1, 2, 3, 1, 1) , UpperCamelCase=5_12 , UpperCamelCase=0 , UpperCamelCase=1 , UpperCamelCase=2 , UpperCamelCase=False , UpperCamelCase=3 , UpperCamelCase=2 , UpperCamelCase=3 , UpperCamelCase=None , **UpperCamelCase , ) -> List[Any]:
super().__init__(**_SCREAMING_SNAKE_CASE , pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE)
UpperCamelCase__ : str = hidden_size
UpperCamelCase__ : Union[str, Any] = feat_extract_activation
UpperCamelCase__ : Tuple = list(_SCREAMING_SNAKE_CASE)
UpperCamelCase__ : List[str] = list(_SCREAMING_SNAKE_CASE)
UpperCamelCase__ : List[Any] = list(_SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Optional[Any] = conv_bias
UpperCamelCase__ : Optional[Any] = num_conv_pos_embeddings
UpperCamelCase__ : str = num_conv_pos_embedding_groups
UpperCamelCase__ : Dict = conv_pos_kernel_size
UpperCamelCase__ : Optional[int] = len(self.conv_dim)
UpperCamelCase__ : Any = num_hidden_layers
UpperCamelCase__ : Any = intermediate_size
UpperCamelCase__ : Optional[Any] = hidden_act
UpperCamelCase__ : Tuple = num_attention_heads
UpperCamelCase__ : Any = hidden_dropout
UpperCamelCase__ : Optional[int] = attention_dropout
UpperCamelCase__ : Optional[Any] = activation_dropout
UpperCamelCase__ : Dict = feat_proj_dropout
UpperCamelCase__ : Optional[int] = final_dropout
UpperCamelCase__ : str = layerdrop
UpperCamelCase__ : List[str] = layer_norm_eps
UpperCamelCase__ : Tuple = initializer_range
UpperCamelCase__ : Optional[int] = vocab_size
UpperCamelCase__ : Tuple = 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
UpperCamelCase__ : List[str] = mask_time_prob
UpperCamelCase__ : Dict = mask_time_length
UpperCamelCase__ : List[str] = mask_time_min_masks
UpperCamelCase__ : Optional[Any] = mask_feature_prob
UpperCamelCase__ : Dict = mask_feature_length
UpperCamelCase__ : List[str] = mask_feature_min_masks
# ctc loss
UpperCamelCase__ : List[str] = ctc_loss_reduction
UpperCamelCase__ : Optional[int] = ctc_zero_infinity
# adapter
UpperCamelCase__ : Optional[int] = add_adapter
UpperCamelCase__ : Optional[Any] = adapter_kernel_size
UpperCamelCase__ : Union[str, Any] = adapter_stride
UpperCamelCase__ : Union[str, Any] = num_adapter_layers
UpperCamelCase__ : List[str] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
UpperCamelCase__ : Optional[Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
UpperCamelCase__ : Dict = list(_SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Any = list(_SCREAMING_SNAKE_CASE)
UpperCamelCase__ : str = list(_SCREAMING_SNAKE_CASE)
UpperCamelCase__ : Tuple = xvector_output_dim
@property
def lowerCAmelCase__ ( self) -> int:
return math.prod(self.conv_stride)
| 410 |
def A_ ( a ):
"""simple docstring"""
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
SCREAMING_SNAKE_CASE_ : Tuple = 4
SCREAMING_SNAKE_CASE_ : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11))
| 511 | 0 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a__ ( UpperCAmelCase_ , unittest.TestCase ):
_a : Dict = RobertaTokenizer
_a : Union[str, Any] = RobertaTokenizerFast
_a : List[str] = True
_a : List[Any] = {'cls_token': '<s>'}
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowerCAmelCase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
__lowerCAmelCase = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
__lowerCAmelCase = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__lowerCAmelCase = {'unk_token': '<unk>'}
__lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
__lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(_lowercase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(_lowercase ) )
def __SCREAMING_SNAKE_CASE( self , **_A ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_lowercase )
def __SCREAMING_SNAKE_CASE( self , **_A ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **_lowercase )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
__lowerCAmelCase = 'lower newer'
__lowerCAmelCase = 'lower newer'
return input_text, output_text
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
__lowerCAmelCase = 'lower newer'
__lowerCAmelCase = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
__lowerCAmelCase = tokenizer.tokenize(_lowercase ) # , add_prefix_space=True)
self.assertListEqual(_lowercase , _lowercase )
__lowerCAmelCase = tokens + [tokenizer.unk_token]
__lowerCAmelCase = [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=_lowercase ) , [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=_lowercase ) , [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2] , )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.tokenizer_class.from_pretrained("roberta-base" )
__lowerCAmelCase = tokenizer.encode("sequence builders" , add_special_tokens=_lowercase )
__lowerCAmelCase = tokenizer.encode("multi-sequence build" , add_special_tokens=_lowercase )
__lowerCAmelCase = tokenizer.encode(
"sequence builders" , add_special_tokens=_lowercase , add_prefix_space=_lowercase )
__lowerCAmelCase = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=_lowercase , add_prefix_space=_lowercase )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_lowercase )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(_lowercase , _lowercase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = 'Encode this sequence.'
__lowerCAmelCase = tokenizer.byte_encoder[' '.encode("utf-8" )[0]]
# Testing encoder arguments
__lowerCAmelCase = tokenizer.encode(_lowercase , add_special_tokens=_lowercase , add_prefix_space=_lowercase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(_lowercase , _lowercase )
__lowerCAmelCase = tokenizer.encode(_lowercase , add_special_tokens=_lowercase , add_prefix_space=_lowercase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(_lowercase , _lowercase )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
__lowerCAmelCase = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(_lowercase , _lowercase )
# Testing spaces after special tokens
__lowerCAmelCase = '<mask>'
tokenizer.add_special_tokens(
{"mask_token": AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase )} ) # mask token has a left space
__lowerCAmelCase = tokenizer.convert_tokens_to_ids(_lowercase )
__lowerCAmelCase = 'Encode <mask> sequence'
__lowerCAmelCase = 'Encode <mask>sequence'
__lowerCAmelCase = tokenizer.encode(_lowercase )
__lowerCAmelCase = encoded.index(_lowercase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(_lowercase , _lowercase )
__lowerCAmelCase = tokenizer.encode(_lowercase )
__lowerCAmelCase = encoded.index(_lowercase )
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(_lowercase , _lowercase )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
__lowerCAmelCase = self.tokenizer_class.from_pretrained(_lowercase , **_lowercase )
__lowerCAmelCase = 'A, <mask> AllenNLP sentence.'
__lowerCAmelCase = tokenizer_r.encode_plus(_lowercase , add_special_tokens=_lowercase , return_token_type_ids=_lowercase )
__lowerCAmelCase = tokenizer_p.encode_plus(_lowercase , add_special_tokens=_lowercase , return_token_type_ids=_lowercase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
__lowerCAmelCase = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
__lowerCAmelCase = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(
_lowercase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
_lowercase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=_lowercase , add_prefix_space=_lowercase , trim_offsets=_lowercase )
__lowerCAmelCase = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
__lowerCAmelCase = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , _lowercase )
self.assertEqual(post_processor_state["add_prefix_space"] , _lowercase )
self.assertEqual(post_processor_state["trim_offsets"] , _lowercase )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__lowerCAmelCase = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
__lowerCAmelCase = f"""{text_of_1_token} {text_of_1_token}"""
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
_lowercase , use_fast=_lowercase , add_prefix_space=_lowercase , trim_offsets=_lowercase )
__lowerCAmelCase = tokenizer_r(_lowercase , return_offsets_mapping=_lowercase , add_special_tokens=_lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_lowercase ) + 1, len(_lowercase ) + 1 + len(_lowercase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
_lowercase , use_fast=_lowercase , add_prefix_space=_lowercase , trim_offsets=_lowercase )
__lowerCAmelCase = tokenizer_r(_lowercase , return_offsets_mapping=_lowercase , add_special_tokens=_lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_lowercase ) + 1, len(_lowercase ) + 1 + len(_lowercase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
_lowercase , use_fast=_lowercase , add_prefix_space=_lowercase , trim_offsets=_lowercase )
__lowerCAmelCase = tokenizer_r(_lowercase , return_offsets_mapping=_lowercase , add_special_tokens=_lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_lowercase ), len(_lowercase ) + 1 + len(_lowercase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
_lowercase , use_fast=_lowercase , add_prefix_space=_lowercase , trim_offsets=_lowercase )
__lowerCAmelCase = tokenizer_r(_lowercase , return_offsets_mapping=_lowercase , add_special_tokens=_lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_lowercase ), len(_lowercase ) + 1 + len(_lowercase )) , )
__lowerCAmelCase = f""" {text}"""
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
_lowercase , use_fast=_lowercase , add_prefix_space=_lowercase , trim_offsets=_lowercase )
__lowerCAmelCase = tokenizer_r(_lowercase , return_offsets_mapping=_lowercase , add_special_tokens=_lowercase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(_lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_lowercase ) + 1, 1 + len(_lowercase ) + 1 + len(_lowercase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
_lowercase , use_fast=_lowercase , add_prefix_space=_lowercase , trim_offsets=_lowercase )
__lowerCAmelCase = tokenizer_r(_lowercase , return_offsets_mapping=_lowercase , add_special_tokens=_lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_lowercase ), 1 + len(_lowercase ) + 1 + len(_lowercase )) , )
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(
_lowercase , use_fast=_lowercase , add_prefix_space=_lowercase , trim_offsets=_lowercase )
__lowerCAmelCase = tokenizer_r(_lowercase , return_offsets_mapping=_lowercase , add_special_tokens=_lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_lowercase ), 1 + len(_lowercase ) + 1 + len(_lowercase )) , )
| 707 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = data
__lowerCAmelCase = None
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = None
__lowerCAmelCase = None
def __iter__( self ):
"""simple docstring"""
__lowerCAmelCase = self.head
while self.head:
yield node.data
__lowerCAmelCase = node.next
if node == self.head:
break
def __len__( self ):
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self ):
"""simple docstring"""
return "->".join(str(_A ) for item in iter(self ) )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
self.insert_nth(len(self ) , _A )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
self.insert_nth(0 , _A )
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
if index < 0 or index > len(self ):
raise IndexError("list index out of range." )
__lowerCAmelCase = Node(_A )
if self.head is None:
__lowerCAmelCase = new_node # first node points itself
__lowerCAmelCase = __lowerCAmelCase = new_node
elif index == 0: # insert at head
__lowerCAmelCase = self.head
__lowerCAmelCase = __lowerCAmelCase = new_node
else:
__lowerCAmelCase = self.head
for _ in range(index - 1 ):
__lowerCAmelCase = temp.next
__lowerCAmelCase = temp.next
__lowerCAmelCase = new_node
if index == len(self ) - 1: # insert at tail
__lowerCAmelCase = new_node
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.delete_nth(0 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def __SCREAMING_SNAKE_CASE( self , _A = 0 ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise IndexError("list index out of range." )
__lowerCAmelCase = self.head
if self.head == self.tail: # just one node
__lowerCAmelCase = __lowerCAmelCase = None
elif index == 0: # delete head node
__lowerCAmelCase = self.tail.next.next
__lowerCAmelCase = self.head.next
else:
__lowerCAmelCase = self.head
for _ in range(index - 1 ):
__lowerCAmelCase = temp.next
__lowerCAmelCase = temp.next
__lowerCAmelCase = temp.next.next
if index == len(self ) - 1: # delete at tail
__lowerCAmelCase = temp
return delete_node.data
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return len(self ) == 0
def _a ( ):
__lowerCAmelCase = CircularLinkedList()
assert len(SCREAMING_SNAKE_CASE_ ) == 0
assert circular_linked_list.is_empty() is True
assert str(SCREAMING_SNAKE_CASE_ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(SCREAMING_SNAKE_CASE_ ) == i
circular_linked_list.insert_nth(SCREAMING_SNAKE_CASE_ , i + 1 )
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 552 | 0 |
"""simple docstring"""
def a ( __snake_case : int, __snake_case : int ):
'''simple docstring'''
while second != 0:
UpperCAmelCase_ :Tuple = first & second
first ^= second
UpperCAmelCase_ :Optional[Any] = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCamelCase = int(input("Enter the first number: ").strip())
__lowerCamelCase = int(input("Enter the second number: ").strip())
print(f'''{add(first, second) = }''')
| 608 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_lowercase = logging.get_logger(__name__)
@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , **_lowercase ):
"""simple docstring"""
super().__init__(**_lowercase )
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
# No specific FOR_XXX available yet
def __call__( self , _lowercase , **_lowercase ):
"""simple docstring"""
return super().__call__(_lowercase , **_lowercase )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {}
if "candidate_labels" in kwargs:
_lowerCAmelCase = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
_lowerCAmelCase = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def _lowercase ( self , _lowercase , _lowercase=None , _lowercase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(_lowercase , _lowercase ):
if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
_lowerCAmelCase = requests.get(_lowercase ).content
else:
with open(_lowercase , """rb""" ) as f:
_lowerCAmelCase = f.read()
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = ffmpeg_read(_lowercase , self.feature_extractor.sampling_rate )
if not isinstance(_lowercase , np.ndarray ):
raise ValueError("""We expect a numpy ndarray as input""" )
if len(audio.shape ) != 1:
raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )
_lowerCAmelCase = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="""pt""" )
_lowerCAmelCase = candidate_labels
_lowerCAmelCase = [hypothesis_template.format(_lowercase ) for x in candidate_labels]
_lowerCAmelCase = self.tokenizer(_lowercase , return_tensors=self.framework , padding=_lowercase )
_lowerCAmelCase = [text_inputs]
return inputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_inputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] , _lowercase ):
_lowerCAmelCase = text_inputs[0]
else:
# Batching case.
_lowerCAmelCase = text_inputs[0][0]
_lowerCAmelCase = self.model(**_lowercase , **_lowercase )
_lowerCAmelCase = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_audio,
}
return model_outputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_outputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_outputs["""logits"""][0]
if self.framework == "pt":
_lowerCAmelCase = logits.softmax(dim=0 )
_lowerCAmelCase = probs.tolist()
else:
raise ValueError("""`tf` framework not supported.""" )
_lowerCAmelCase = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(_lowercase , _lowercase ) , key=lambda _lowercase : -x[0] )
]
return result
| 5 | 0 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : torch.FloatTensor
_lowerCAmelCase : Optional[torch.FloatTensor] = None
def __lowercase ( _a , _a=0.999 , _a="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(_a ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_a ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )
snake_case_ : Union[str, Any] = []
for i in range(_a ):
snake_case_ : Union[str, Any] = i / num_diffusion_timesteps
snake_case_ : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_a ) / alpha_bar_fn(_a ) , _a ) )
return torch.tensor(_a , dtype=torch.floataa )
class _UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__):
_lowerCAmelCase : Union[str, Any] = 1
@register_to_config
def __init__( self : Tuple , lowercase_ : int = 1000 , lowercase_ : float = 0.00_01 , lowercase_ : float = 0.02 , lowercase_ : str = "linear" , lowercase_ : Optional[Union[np.ndarray, List[float]]] = None , lowercase_ : bool = True , lowercase_ : bool = True , lowercase_ : int = 0 , lowercase_ : str = "epsilon" , lowercase_ : float = 1.0 , **lowercase_ : int , ):
if kwargs.get('''set_alpha_to_one''' , lowercase_ ) is not None:
snake_case_ : int = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('''set_alpha_to_one''' , '''1.0.0''' , lowercase_ , standard_warn=lowercase_ )
snake_case_ : Dict = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
snake_case_ : str = torch.tensor(lowercase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
snake_case_ : List[str] = torch.linspace(lowercase_ , lowercase_ , lowercase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
snake_case_ : Dict = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowercase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
snake_case_ : Tuple = betas_for_alpha_bar(lowercase_ )
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )
snake_case_ : List[Any] = 1.0 - self.betas
snake_case_ : Optional[Any] = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
snake_case_ : str = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
snake_case_ : str = 1.0
# setable values
snake_case_ : Dict = None
snake_case_ : Tuple = torch.from_numpy(np.arange(0 , lowercase_ ).copy().astype(np.intaa ) )
def _snake_case ( self : List[str] , lowercase_ : torch.FloatTensor , lowercase_ : Optional[int] = None ):
return sample
def _snake_case ( self : List[str] , lowercase_ : int , lowercase_ : Union[str, torch.device] = None ):
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
f" maximal {self.config.num_train_timesteps} timesteps." )
snake_case_ : Optional[Any] = num_inference_steps
snake_case_ : Union[str, Any] = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
snake_case_ : str = (np.arange(0 , lowercase_ ) * step_ratio).round().copy().astype(np.intaa )
snake_case_ : List[Any] = torch.from_numpy(lowercase_ ).to(lowercase_ )
self.timesteps += self.config.steps_offset
def _snake_case ( self : Tuple , lowercase_ : torch.FloatTensor , lowercase_ : int , lowercase_ : torch.FloatTensor , lowercase_ : float = 0.0 , lowercase_ : bool = False , lowercase_ : Optional[torch.FloatTensor] = None , lowercase_ : bool = True , ):
# 1. get previous step value (=t+1)
snake_case_ : str = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
snake_case_ : str = self.alphas_cumprod[timestep]
snake_case_ : Optional[Any] = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
snake_case_ : Dict = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
snake_case_ : Union[str, Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
snake_case_ : List[Any] = model_output
elif self.config.prediction_type == "sample":
snake_case_ : List[Any] = model_output
snake_case_ : Dict = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
snake_case_ : Any = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
snake_case_ : Any = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
''' `v_prediction`''' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
snake_case_ : List[Any] = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
snake_case_ : Optional[int] = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
snake_case_ : int = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=lowercase_ , pred_original_sample=lowercase_ )
def __len__( self : str ):
return self.config.num_train_timesteps
| 485 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase__ : Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class _UpperCAmelCase :
_lowerCAmelCase : Optional[str] = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""})
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={"""help""": """The column name of the images in the files."""})
_lowerCAmelCase : Optional[str] = field(default=lowerCAmelCase__ , metadata={"""help""": """A folder containing the training data."""})
_lowerCAmelCase : Optional[str] = field(default=lowerCAmelCase__ , metadata={"""help""": """A folder containing the validation data."""})
_lowerCAmelCase : Optional[float] = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""})
_lowerCAmelCase : Optional[int] = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_lowerCAmelCase : Optional[int] = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def _snake_case ( self : Union[str, Any] ):
snake_case_ : List[Any] = {}
if self.train_dir is not None:
snake_case_ : str = self.train_dir
if self.validation_dir is not None:
snake_case_ : Union[str, Any] = self.validation_dir
snake_case_ : Tuple = data_files if data_files else None
@dataclass
class _UpperCAmelCase :
_lowerCAmelCase : str = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""})
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
_lowerCAmelCase : Optional[str] = field(
default=lowerCAmelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""})
_lowerCAmelCase : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_lowerCAmelCase : str = field(default=lowerCAmelCase__ , metadata={"""help""": """Name or path of preprocessor config."""})
_lowerCAmelCase : bool = field(
default=lowerCAmelCase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_lowerCAmelCase : float = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""})
_lowerCAmelCase : bool = field(
default=lowerCAmelCase__ , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""})
@dataclass
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : float = field(
default=1e-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""})
def __lowercase ( _a ):
snake_case_ : Tuple = torch.stack([example['''pixel_values'''] for example in examples] )
return {"pixel_values": pixel_values}
def __lowercase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
snake_case_ : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
snake_case_, snake_case_, snake_case_ : Optional[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case_, snake_case_, snake_case_ : List[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_mae''' , _a , _a )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
snake_case_ : List[str] = training_args.get_process_log_level()
logger.setLevel(_a )
transformers.utils.logging.set_verbosity(_a )
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.
snake_case_ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case_ : 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.''' )
# Initialize our dataset.
snake_case_ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
snake_case_ : Optional[Any] = None if '''validation''' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _a ) and data_args.train_val_split > 0.0:
snake_case_ : List[Any] = ds['''train'''].train_test_split(data_args.train_val_split )
snake_case_ : Tuple = split['''train''']
snake_case_ : str = split['''test''']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case_ : Optional[int] = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name:
snake_case_ : List[Any] = ViTMAEConfig.from_pretrained(model_args.config_name , **_a )
elif model_args.model_name_or_path:
snake_case_ : Dict = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_a )
else:
snake_case_ : Optional[int] = ViTMAEConfig()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(f"New config: {config}" )
# adapt config
config.update(
{
'''mask_ratio''': model_args.mask_ratio,
'''norm_pix_loss''': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
snake_case_ : Union[str, Any] = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_a )
elif model_args.model_name_or_path:
snake_case_ : Union[str, Any] = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_a )
else:
snake_case_ : Tuple = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
snake_case_ : Tuple = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_a , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
snake_case_ : Tuple = ViTMAEForPreTraining(_a )
if training_args.do_train:
snake_case_ : List[str] = ds['''train'''].column_names
else:
snake_case_ : Optional[Any] = ds['''validation'''].column_names
if data_args.image_column_name is not None:
snake_case_ : Tuple = data_args.image_column_name
elif "image" in column_names:
snake_case_ : Tuple = '''image'''
elif "img" in column_names:
snake_case_ : str = '''img'''
else:
snake_case_ : Union[str, Any] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
snake_case_ : str = image_processor.size['''shortest_edge''']
else:
snake_case_ : Dict = (image_processor.size['''height'''], image_processor.size['''width'''])
snake_case_ : str = Compose(
[
Lambda(lambda _a : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_a , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_a ):
snake_case_ : Tuple = [transforms(_a ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
snake_case_ : List[str] = ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_a )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
snake_case_ : Optional[Any] = (
ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_a )
# Compute absolute learning rate
snake_case_ : Any = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
snake_case_ : Union[str, Any] = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
snake_case_ : str = Trainer(
model=_a , args=_a , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=_a , data_collator=_a , )
# Training
if training_args.do_train:
snake_case_ : Any = None
if training_args.resume_from_checkpoint is not None:
snake_case_ : Optional[int] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case_ : str = last_checkpoint
snake_case_ : List[str] = trainer.train(resume_from_checkpoint=_a )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
snake_case_ : Any = trainer.evaluate()
trainer.log_metrics('''eval''' , _a )
trainer.save_metrics('''eval''' , _a )
# Write model card and (optionally) push to hub
snake_case_ : Optional[int] = {
'''tasks''': '''masked-auto-encoding''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''masked-auto-encoding'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_a )
else:
trainer.create_model_card(**_a )
def __lowercase ( _a ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 485 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
A = '''
Human: <<task>>
Assistant: '''
A = '''huggingface-tools/default-prompts'''
A = {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Dict , lowerCAmelCase__ : int="run") -> List[str]:
'''simple docstring'''
if prompt_or_repo_id is None:
_lowercase : Any = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('\\s' , lowerCAmelCase__) is not None:
return prompt_or_repo_id
_lowercase : List[str] = cached_file(
lowerCAmelCase__ , PROMPT_FILES[mode] , repo_type='dataset' , user_agent={'agent': agent_name})
with open(lowerCAmelCase__ , 'r' , encoding='utf-8') as f:
return f.read() | 125 |
def __magic_name__ ( lowercase = 100 ) -> int:
"""simple docstring"""
lowercase_ : Dict = (n * (n + 1) // 2) ** 2
lowercase_ : List[str] = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''') | 458 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
a_ : Union[str, Any] = TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''' )
a_ : str = {
'''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]] , dtype=tf.intaa ), # "My dog is cute"
'''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
a_ : str = model(__SCREAMING_SNAKE_CASE )['''last_hidden_state''']
a_ : Optional[int] = tf.TensorShape((1, 6, 768) )
self.assertEqual(output.shape , __SCREAMING_SNAKE_CASE )
# compare the actual values for a slice.
a_ : Tuple = tf.convert_to_tensor(
[
[
[0.068_1762, 0.1089_4451, 0.0677_2504],
[-0.0642_3668, 0.0236_6615, 0.0432_9344],
[-0.0605_7295, 0.0997_4135, -0.0007_0584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 666 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GitForCausalLM',
'GitModel',
'GitPreTrainedModel',
'GitVisionModel',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 666 | 1 |
def __lowerCAmelCase ( __lowerCamelCase : list ) -> List[str]:
__lowerCAmelCase =False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase =True
for i in range(0 , len(_A ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
__lowerCAmelCase , __lowerCAmelCase =input_list[i + 1], input_list[i]
# swapping if elements not in order
__lowerCAmelCase =False
for i in range(1 , len(_A ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
__lowerCAmelCase , __lowerCAmelCase =input_list[i + 1], input_list[i]
# swapping if elements not in order
__lowerCAmelCase =False
return input_list
if __name__ == "__main__":
print('''Enter list to be sorted''')
lowercase_ = [int(x) for x in input().split()]
# inputing elements of the list in one line
lowercase_ = odd_even_sort(input_list)
print('''The sorted list is''')
print(sorted_list)
| 354 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Dict = ['GPTSw3Tokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
_a : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 479 | 0 |
'''simple docstring'''
from math import ceil
def UpperCAmelCase_ ( __lowercase : int = 1001 ) -> int:
'''simple docstring'''
_UpperCAmelCase = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
_UpperCAmelCase = 2 * i + 1
_UpperCAmelCase = 2 * i
_UpperCAmelCase = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
__SCREAMING_SNAKE_CASE :Dict = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number''')
| 119 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def UpperCAmelCase_ ( __lowercase : str ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = fname.split(os.path.sep )[-1]
return re.search(r"^(.*)_\d+\.jpg$" , __lowercase ).groups()[0]
class A_ ( lowerCAmelCase_ ):
def __init__( self : List[str] , snake_case_ : Union[str, Any] , snake_case_ : int=None , snake_case_ : List[Any]=None ):
_UpperCAmelCase = file_names
_UpperCAmelCase = image_transform
_UpperCAmelCase = label_to_id
def __len__( self : Union[str, Any] ):
return len(self.file_names )
def __getitem__( self : Any , snake_case_ : Tuple ):
_UpperCAmelCase = self.file_names[idx]
_UpperCAmelCase = PIL.Image.open(snake_case_ )
_UpperCAmelCase = raw_image.convert("RGB" )
if self.image_transform is not None:
_UpperCAmelCase = self.image_transform(snake_case_ )
_UpperCAmelCase = extract_label(snake_case_ )
if self.label_to_id is not None:
_UpperCAmelCase = self.label_to_id[label]
return {"image": image, "label": label}
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Dict ) -> Optional[int]:
'''simple docstring'''
if args.with_tracking:
_UpperCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
_UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase = config["lr"]
_UpperCAmelCase = int(config["num_epochs"] )
_UpperCAmelCase = int(config["seed"] )
_UpperCAmelCase = int(config["batch_size"] )
_UpperCAmelCase = config["image_size"]
if not isinstance(__lowercase , (list, tuple) ):
_UpperCAmelCase = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , "isdigit" ):
if args.checkpointing_steps == "epoch":
_UpperCAmelCase = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
_UpperCAmelCase = int(args.checkpointing_steps )
else:
raise ValueError(
f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
else:
_UpperCAmelCase = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
_UpperCAmelCase = os.path.split(__lowercase )[-1].split("." )[0]
accelerator.init_trackers(__lowercase , __lowercase )
# Grab all the image filenames
_UpperCAmelCase = [os.path.join(args.data_dir , __lowercase ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]
# Build the label correspondences
_UpperCAmelCase = [extract_label(__lowercase ) for fname in file_names]
_UpperCAmelCase = list(set(__lowercase ) )
id_to_label.sort()
_UpperCAmelCase = {lbl: i for i, lbl in enumerate(__lowercase )}
# Set the seed before splitting the data.
np.random.seed(__lowercase )
torch.manual_seed(__lowercase )
torch.cuda.manual_seed_all(__lowercase )
# Split our filenames between train and validation
_UpperCAmelCase = np.random.permutation(len(__lowercase ) )
_UpperCAmelCase = int(0.8 * len(__lowercase ) )
_UpperCAmelCase = random_perm[:cut]
_UpperCAmelCase = random_perm[cut:]
# For training we use a simple RandomResizedCrop
_UpperCAmelCase = Compose([RandomResizedCrop(__lowercase , scale=(0.5, 1.0) ), ToTensor()] )
_UpperCAmelCase = PetsDataset(
[file_names[i] for i in train_split] , image_transform=__lowercase , label_to_id=__lowercase )
# For evaluation, we use a deterministic Resize
_UpperCAmelCase = Compose([Resize(__lowercase ), ToTensor()] )
_UpperCAmelCase = PetsDataset([file_names[i] for i in eval_split] , image_transform=__lowercase , label_to_id=__lowercase )
# Instantiate dataloaders.
_UpperCAmelCase = DataLoader(__lowercase , shuffle=__lowercase , batch_size=__lowercase , num_workers=4 )
_UpperCAmelCase = DataLoader(__lowercase , shuffle=__lowercase , batch_size=__lowercase , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase = create_model("resnet50d" , pretrained=__lowercase , num_classes=len(__lowercase ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
_UpperCAmelCase = False
for param in model.get_classifier().parameters():
_UpperCAmelCase = True
# We normalize the batches of images to be a bit faster.
_UpperCAmelCase = torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
_UpperCAmelCase = torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
_UpperCAmelCase = OneCycleLR(optimizer=__lowercase , max_lr=__lowercase , epochs=__lowercase , steps_per_epoch=len(__lowercase ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
# We need to keep track of how many total steps we have iterated over
_UpperCAmelCase = 0
# We also need to keep track of the starting epoch so files are named properly
_UpperCAmelCase = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}' )
accelerator.load_state(args.resume_from_checkpoint )
_UpperCAmelCase = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
_UpperCAmelCase = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
_UpperCAmelCase = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
_UpperCAmelCase = os.path.splitext(__lowercase )[0]
if "epoch" in training_difference:
_UpperCAmelCase = int(training_difference.replace("epoch_" , "" ) ) + 1
_UpperCAmelCase = None
else:
_UpperCAmelCase = int(training_difference.replace("step_" , "" ) )
_UpperCAmelCase = resume_step // len(__lowercase )
resume_step -= starting_epoch * len(__lowercase )
# Now we train the model
for epoch in range(__lowercase , __lowercase ):
model.train()
if args.with_tracking:
_UpperCAmelCase = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
_UpperCAmelCase = accelerator.skip_first_batches(__lowercase , __lowercase )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
_UpperCAmelCase = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
_UpperCAmelCase = {k: v.to(accelerator.device ) for k, v in batch.items()}
_UpperCAmelCase = (batch["image"] - mean) / std
_UpperCAmelCase = model(__lowercase )
_UpperCAmelCase = torch.nn.functional.cross_entropy(__lowercase , batch["label"] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(__lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(__lowercase , __lowercase ):
_UpperCAmelCase = f'step_{overall_step}'
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
_UpperCAmelCase = os.path.join(args.output_dir , __lowercase )
accelerator.save_state(__lowercase )
model.eval()
_UpperCAmelCase = 0
_UpperCAmelCase = 0
for step, batch in enumerate(__lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
_UpperCAmelCase = {k: v.to(accelerator.device ) for k, v in batch.items()}
_UpperCAmelCase = (batch["image"] - mean) / std
with torch.no_grad():
_UpperCAmelCase = model(__lowercase )
_UpperCAmelCase = outputs.argmax(dim=-1 )
_UpperCAmelCase , _UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch["label"]) )
_UpperCAmelCase = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
_UpperCAmelCase = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}' )
if args.with_tracking:
accelerator.log(
{
"accuracy": 100 * eval_metric,
"train_loss": total_loss.item() / len(__lowercase ),
"epoch": epoch,
} , step=__lowercase , )
if checkpointing_steps == "epoch":
_UpperCAmelCase = f'epoch_{epoch}'
if args.output_dir is not None:
_UpperCAmelCase = os.path.join(args.output_dir , __lowercase )
accelerator.save_state(__lowercase )
if args.with_tracking:
accelerator.end_training()
def UpperCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument("--data_dir" , required=__lowercase , help="The data folder on disk." )
parser.add_argument("--fp16" , action="store_true" , help="If passed, will use FP16 training." )
parser.add_argument(
"--mixed_precision" , type=__lowercase , default=__lowercase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
parser.add_argument(
"--checkpointing_steps" , type=__lowercase , default=__lowercase , help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." , )
parser.add_argument(
"--output_dir" , type=__lowercase , default="." , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." , )
parser.add_argument(
"--resume_from_checkpoint" , type=__lowercase , default=__lowercase , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
parser.add_argument(
"--project_dir" , type=__lowercase , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
_UpperCAmelCase = parser.parse_args()
_UpperCAmelCase = {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
training_function(__lowercase , __lowercase )
if __name__ == "__main__":
main()
| 119 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCamelCase ={
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
"processing_trocr": ["TrOCRProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase =[
"TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrOCRForCausalLM",
"TrOCRPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 285 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 285 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {"tokenizer_file": "tokenizer.json"}
UpperCamelCase__ = {
"tokenizer_file": {
"bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
"bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
"bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
"bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
"bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
"bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
"bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
},
}
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : List[Any] = VOCAB_FILES_NAMES
snake_case : Dict = PRETRAINED_VOCAB_FILES_MAP
snake_case : Optional[int] = ["""input_ids""", """attention_mask"""]
snake_case : List[Any] = None
def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase="<unk>" , __lowerCAmelCase="<s>" , __lowerCAmelCase="</s>" , __lowerCAmelCase="<pad>" , __lowerCAmelCase=False , __lowerCAmelCase=False , **__lowerCAmelCase , ):
super().__init__(
__lowerCAmelCase , __lowerCAmelCase , tokenizer_file=__lowerCAmelCase , unk_token=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , add_prefix_space=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase , **__lowerCAmelCase , )
UpperCamelCase__ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , __lowerCAmelCase ) != add_prefix_space:
UpperCamelCase__ = getattr(__lowerCAmelCase , pre_tok_state.pop("""type""" ) )
UpperCamelCase__ = add_prefix_space
UpperCamelCase__ = pre_tok_class(**__lowerCAmelCase )
UpperCamelCase__ = add_prefix_space
def _lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
UpperCamelCase__ = kwargs.get("""is_split_into_words""" , __lowerCAmelCase )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
""" pretokenized inputs.""" )
return super()._batch_encode_plus(*__lowerCAmelCase , **__lowerCAmelCase )
def _lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
UpperCamelCase__ = kwargs.get("""is_split_into_words""" , __lowerCAmelCase )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
""" pretokenized inputs.""" )
return super()._encode_plus(*__lowerCAmelCase , **__lowerCAmelCase )
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
UpperCamelCase__ = self._tokenizer.model.save(__lowerCAmelCase , name=__lowerCAmelCase )
return tuple(__lowerCAmelCase )
def _lowerCamelCase ( self , __lowerCAmelCase ):
UpperCamelCase__ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase ) + [self.eos_token_id] )
if len(__lowerCAmelCase ) > self.model_max_length:
UpperCamelCase__ = input_ids[-self.model_max_length :]
return input_ids
| 700 |
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 ):
snake_case : Union[str, Any] = MODEL_FOR_CAUSAL_LM_MAPPING
snake_case : List[str] = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def _lowerCamelCase ( self ):
UpperCamelCase__ = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""pt""" )
# Using `do_sample=False` to force deterministic output
UpperCamelCase__ = text_generator("""This is a test""" , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
{
"""generated_text""": (
"""This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
""" oscope. FiliFili@@"""
)
}
] , )
UpperCamelCase__ = text_generator(["""This is a test""", """This is a second test"""] )
self.assertEqual(
__lowerCAmelCase , [
[
{
"""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@@"""
)
}
],
] , )
UpperCamelCase__ = text_generator("""This is a test""" , do_sample=__lowerCAmelCase , num_return_sequences=2 , return_tensors=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
{"""generated_token_ids""": ANY(__lowerCAmelCase )},
{"""generated_token_ids""": ANY(__lowerCAmelCase )},
] , )
UpperCamelCase__ = text_generator.model.config.eos_token_id
UpperCamelCase__ = """<pad>"""
UpperCamelCase__ = text_generator(
["""This is a test""", """This is a second test"""] , do_sample=__lowerCAmelCase , num_return_sequences=2 , batch_size=2 , return_tensors=__lowerCAmelCase , )
self.assertEqual(
__lowerCAmelCase , [
[
{"""generated_token_ids""": ANY(__lowerCAmelCase )},
{"""generated_token_ids""": ANY(__lowerCAmelCase )},
],
[
{"""generated_token_ids""": ANY(__lowerCAmelCase )},
{"""generated_token_ids""": ANY(__lowerCAmelCase )},
],
] , )
@require_tf
def _lowerCamelCase ( self ):
UpperCamelCase__ = pipeline(task="""text-generation""" , model="""sshleifer/tiny-ctrl""" , framework="""tf""" )
# Using `do_sample=False` to force deterministic output
UpperCamelCase__ = text_generator("""This is a test""" , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
{
"""generated_text""": (
"""This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
""" please,"""
)
}
] , )
UpperCamelCase__ = text_generator(["""This is a test""", """This is a second test"""] , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
[
{
"""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 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = TextGenerationPipeline(model=__lowerCAmelCase , tokenizer=__lowerCAmelCase )
return text_generator, ["This is a test", "Another test"]
def _lowerCamelCase ( self ):
UpperCamelCase__ = """Hello I believe in"""
UpperCamelCase__ = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""" )
UpperCamelCase__ = text_generator(__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}] , )
UpperCamelCase__ = text_generator(__lowerCAmelCase , stop_sequence=""" fe""" )
self.assertEqual(__lowerCAmelCase , [{"""generated_text""": """Hello I believe in fe"""}] )
def _lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = text_generator.model
UpperCamelCase__ = text_generator.tokenizer
UpperCamelCase__ = text_generator("""This is a test""" )
self.assertEqual(__lowerCAmelCase , [{"""generated_text""": ANY(__lowerCAmelCase )}] )
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test""" ) )
UpperCamelCase__ = text_generator("""This is a test""" , return_full_text=__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , [{"""generated_text""": ANY(__lowerCAmelCase )}] )
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""] )
UpperCamelCase__ = pipeline(task="""text-generation""" , model=__lowerCAmelCase , tokenizer=__lowerCAmelCase , return_full_text=__lowerCAmelCase )
UpperCamelCase__ = text_generator("""This is a test""" )
self.assertEqual(__lowerCAmelCase , [{"""generated_text""": ANY(__lowerCAmelCase )}] )
self.assertNotIn("""This is a test""" , outputs[0]["""generated_text"""] )
UpperCamelCase__ = text_generator("""This is a test""" , return_full_text=__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , [{"""generated_text""": ANY(__lowerCAmelCase )}] )
self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test""" ) )
UpperCamelCase__ = text_generator(["""This is great !""", """Something else"""] , num_return_sequences=2 , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
[{"""generated_text""": ANY(__lowerCAmelCase )}, {"""generated_text""": ANY(__lowerCAmelCase )}],
[{"""generated_text""": ANY(__lowerCAmelCase )}, {"""generated_text""": ANY(__lowerCAmelCase )}],
] , )
if text_generator.tokenizer.pad_token is not None:
UpperCamelCase__ = text_generator(
["""This is great !""", """Something else"""] , num_return_sequences=2 , batch_size=2 , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
[{"""generated_text""": ANY(__lowerCAmelCase )}, {"""generated_text""": ANY(__lowerCAmelCase )}],
[{"""generated_text""": ANY(__lowerCAmelCase )}, {"""generated_text""": ANY(__lowerCAmelCase )}],
] , )
with self.assertRaises(__lowerCAmelCase ):
UpperCamelCase__ = text_generator("""test""" , return_full_text=__lowerCAmelCase , return_text=__lowerCAmelCase )
with self.assertRaises(__lowerCAmelCase ):
UpperCamelCase__ = text_generator("""test""" , return_full_text=__lowerCAmelCase , return_tensors=__lowerCAmelCase )
with self.assertRaises(__lowerCAmelCase ):
UpperCamelCase__ = text_generator("""test""" , return_text=__lowerCAmelCase , return_tensors=__lowerCAmelCase )
# 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__
):
UpperCamelCase__ = text_generator("""""" )
self.assertEqual(__lowerCAmelCase , [{"""generated_text""": ANY(__lowerCAmelCase )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
UpperCamelCase__ = 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.
UpperCamelCase__ = ["""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 )
UpperCamelCase__ = text_generator("""This is a test""" * 500 , handle_long_generation="""hole""" , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(__lowerCAmelCase ):
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`
UpperCamelCase__ = 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 )
UpperCamelCase__ = pipe("""This is a test""" )
self.assertEqual(
__lowerCAmelCase , [
{
"""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.)
UpperCamelCase__ = 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 )
UpperCamelCase__ = pipe("""This is a test""" )
self.assertEqual(
__lowerCAmelCase , [
{
"""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
UpperCamelCase__ = 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 )
UpperCamelCase__ = pipe("""This is a test""" )
self.assertEqual(
__lowerCAmelCase , [
{
"""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
UpperCamelCase__ = 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
UpperCamelCase__ = pipeline(model="""hf-internal-testing/tiny-random-bloom""" , device_map="""auto""" , torch_dtype=torch.floataa )
pipe("""This is a test""" , do_sample=__lowerCAmelCase , top_p=0.5 )
def _lowerCamelCase ( self ):
UpperCamelCase__ = """Hello world"""
UpperCamelCase__ = pipeline("""text-generation""" , model="""hf-internal-testing/tiny-random-gpt2""" )
if text_generator.model.framework == "tf":
UpperCamelCase__ = logging.get_logger("""transformers.generation.tf_utils""" )
else:
UpperCamelCase__ = logging.get_logger("""transformers.generation.utils""" )
UpperCamelCase__ = """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(__lowerCAmelCase ) as cl:
UpperCamelCase__ = text_generator(__lowerCAmelCase , max_length=10 , max_new_tokens=1 )
self.assertIn(__lowerCAmelCase , cl.out )
# The user only sets one -> no warning
with CaptureLogger(__lowerCAmelCase ) as cl:
UpperCamelCase__ = text_generator(__lowerCAmelCase , max_new_tokens=1 )
self.assertNotIn(__lowerCAmelCase , cl.out )
with CaptureLogger(__lowerCAmelCase ) as cl:
UpperCamelCase__ = text_generator(__lowerCAmelCase , max_length=10 )
self.assertNotIn(__lowerCAmelCase , cl.out )
| 548 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
a = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def lowercase (snake_case__ : Optional[Any] , snake_case__ : tuple , snake_case__ : Path , snake_case__ : List[str] , snake_case__ : Dict , snake_case__ : Union[str, Any] , snake_case__ : Dict , snake_case__ : Union[str, Any]=False , ) -> List[Any]:
'''simple docstring'''
output_path.parent.mkdir(parents=snake_case__ , exist_ok=snake_case__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
snake_case__ , snake_case__ , f=output_path.as_posix() , input_names=snake_case__ , output_names=snake_case__ , dynamic_axes=snake_case__ , do_constant_folding=snake_case__ , use_external_data_format=snake_case__ , enable_onnx_checker=snake_case__ , opset_version=snake_case__ , )
else:
export(
snake_case__ , snake_case__ , f=output_path.as_posix() , input_names=snake_case__ , output_names=snake_case__ , dynamic_axes=snake_case__ , do_constant_folding=snake_case__ , opset_version=snake_case__ , )
@torch.no_grad()
def lowercase (snake_case__ : str , snake_case__ : str , snake_case__ : int , snake_case__ : bool = False ) -> Dict:
'''simple docstring'''
lowerCAmelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
lowerCAmelCase = """cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
else:
lowerCAmelCase = """cpu"""
lowerCAmelCase = Path(snake_case__ )
# VAE DECODER
lowerCAmelCase = AutoencoderKL.from_pretrained(model_path + """/vae""" )
lowerCAmelCase = vae_decoder.config.latent_channels
# forward only through the decoder part
lowerCAmelCase = vae_decoder.decode
onnx_export(
snake_case__ , model_args=(
torch.randn(1 , snake_case__ , 25 , 25 ).to(device=snake_case__ , dtype=snake_case__ ),
False,
) , output_path=output_path / """vae_decoder""" / """model.onnx""" , ordered_input_names=["""latent_sample""", """return_dict"""] , output_names=["""sample"""] , dynamic_axes={
"""latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=snake_case__ , )
del vae_decoder
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
type=str,
required=True,
help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
)
parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--opset',
default=1_4,
type=int,
help='The version of the ONNX operator set to use.',
)
parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')
a = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('SD: Done: ONNX')
| 169 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
_a = GPTSwaTokenizer
_a = False
_a = True
_a = False
def __lowercase ( self : Tuple ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase = GPTSwaTokenizer(lowerCAmelCase , eos_token="""<unk>""" , bos_token="""<unk>""" , pad_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : Union[str, Any] , lowerCAmelCase : Tuple ):
lowerCAmelCase = """This is a test"""
lowerCAmelCase = """This is a test"""
return input_text, output_text
def __lowercase ( self : List[Any] ):
lowerCAmelCase = """<s>"""
lowerCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ) , lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ) , lowerCAmelCase )
def __lowercase ( self : Any ):
lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(lowerCAmelCase ) , 2000 )
def __lowercase ( self : Tuple ):
self.assertEqual(self.get_tokenizer().vocab_size , 2000 )
def __lowercase ( self : Union[str, Any] ):
lowerCAmelCase = GPTSwaTokenizer(lowerCAmelCase )
lowerCAmelCase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowerCAmelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [465, 287, 265, 631, 842] )
lowerCAmelCase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
# fmt: off
self.assertListEqual(
lowerCAmelCase , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] , )
# fmt: on
lowerCAmelCase = tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] , )
lowerCAmelCase = tokenizer.convert_ids_to_tokens(lowerCAmelCase )
# fmt: off
self.assertListEqual(
lowerCAmelCase , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] )
# fmt: on
def __lowercase ( self : int ):
lowerCAmelCase = GPTSwaTokenizer(lowerCAmelCase )
lowerCAmelCase = ["""This is a test""", """I was born in 92000, and this is falsé."""]
lowerCAmelCase = [
[465, 287, 265, 631, 842],
[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(lowerCAmelCase , lowerCAmelCase ):
self.assertListEqual(tokenizer.encode_fast(lowerCAmelCase ) , lowerCAmelCase )
# Test that decode_fast returns the input text
for text, token_ids in zip(lowerCAmelCase , lowerCAmelCase ):
self.assertEqual(tokenizer.decode_fast(lowerCAmelCase ) , lowerCAmelCase )
@slow
def __lowercase ( self : Any ):
lowerCAmelCase = [
"""<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')""",
"""Hey there, how are you doing this fine day?""",
"""This is a text with a trailing spaces followed by a dot .""",
"""Häj sväjs lillebrör! =)""",
"""Det är inget fel på Mr. Cool""",
]
# fmt: off
lowerCAmelCase = {"""input_ids""": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase , model_name="""AI-Sweden/gpt-sw3-126m""" , sequences=lowerCAmelCase , )
| 169 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DEISMultistepScheduler,)
_SCREAMING_SNAKE_CASE :List[Any] = (("""num_inference_steps""", 25),)
def _a ( self , **_a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
}
config.update(**_a )
return config
def _a ( self , _a=0 , **_a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE__ : Optional[Any] = kwargs.pop("""num_inference_steps""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_sample
SCREAMING_SNAKE_CASE__ : str = 0.1 * sample
SCREAMING_SNAKE_CASE__ : str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_scheduler_config(**_a )
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
scheduler.set_timesteps(_a )
# copy over dummy past residuals
SCREAMING_SNAKE_CASE__ : Any = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_a )
SCREAMING_SNAKE_CASE__ : Tuple = scheduler_class.from_pretrained(_a )
new_scheduler.set_timesteps(_a )
# copy over dummy past residuals
SCREAMING_SNAKE_CASE__ : str = dummy_past_residuals[: new_scheduler.config.solver_order]
SCREAMING_SNAKE_CASE__ : Any = sample, sample
for t in range(_a , time_step + scheduler.config.solver_order + 1 ):
SCREAMING_SNAKE_CASE__ : str = scheduler.step(_a , _a , _a , **_a ).prev_sample
SCREAMING_SNAKE_CASE__ : Any = new_scheduler.step(_a , _a , _a , **_a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _a ( self ) -> Tuple:
"""simple docstring"""
pass
def _a ( self , _a=0 , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE__ : int = kwargs.pop("""num_inference_steps""" , _a )
SCREAMING_SNAKE_CASE__ : Any = self.dummy_sample
SCREAMING_SNAKE_CASE__ : Any = 0.1 * sample
SCREAMING_SNAKE_CASE__ : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
scheduler.set_timesteps(_a )
# copy over dummy past residuals (must be after setting timesteps)
SCREAMING_SNAKE_CASE__ : Tuple = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_a )
SCREAMING_SNAKE_CASE__ : Tuple = scheduler_class.from_pretrained(_a )
# copy over dummy past residuals
new_scheduler.set_timesteps(_a )
# copy over dummy past residual (must be after setting timesteps)
SCREAMING_SNAKE_CASE__ : Any = dummy_past_residuals[: new_scheduler.config.solver_order]
SCREAMING_SNAKE_CASE__ : List[Any] = scheduler.step(_a , _a , _a , **_a ).prev_sample
SCREAMING_SNAKE_CASE__ : str = new_scheduler.step(_a , _a , _a , **_a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _a ( self , _a=None , **_a ) -> Union[str, Any]:
"""simple docstring"""
if scheduler is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Any = self.get_scheduler_config(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config(**_a )
SCREAMING_SNAKE_CASE__ : str = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : str = 10
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : Dict = self.dummy_sample_deter
scheduler.set_timesteps(_a )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : str = model(_a , _a )
SCREAMING_SNAKE_CASE__ : Tuple = scheduler.step(_a , _a , _a ).prev_sample
return sample
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE__ : List[str] = kwargs.pop("""num_inference_steps""" , _a )
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE__ : List[str] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : str = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_sample
SCREAMING_SNAKE_CASE__ : Optional[int] = 0.1 * sample
if num_inference_steps is not None and hasattr(_a , """set_timesteps""" ):
scheduler.set_timesteps(_a )
elif num_inference_steps is not None and not hasattr(_a , """set_timesteps""" ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
SCREAMING_SNAKE_CASE__ : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.10]
SCREAMING_SNAKE_CASE__ : List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
SCREAMING_SNAKE_CASE__ : Optional[int] = scheduler.timesteps[5]
SCREAMING_SNAKE_CASE__ : Any = scheduler.timesteps[6]
SCREAMING_SNAKE_CASE__ : Optional[Any] = scheduler.step(_a , _a , _a , **_a ).prev_sample
SCREAMING_SNAKE_CASE__ : str = scheduler.step(_a , _a , _a , **_a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DEISMultistepScheduler(**self.get_scheduler_config() )
SCREAMING_SNAKE_CASE__ : Tuple = self.full_loop(scheduler=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
SCREAMING_SNAKE_CASE__ : Dict = DPMSolverSinglestepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE__ : Tuple = DPMSolverMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE__ : Tuple = UniPCMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE__ : str = DEISMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE__ : str = self.full_loop(scheduler=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
def _a ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> Dict:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , algorithm_type="""deis""" , solver_order=_a , solver_type=_a , )
def _a ( self ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=_a , solver_type=_a , prediction_type=_a , algorithm_type=_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.full_loop(
solver_order=_a , solver_type=_a , prediction_type=_a , algorithm_type=_a , )
assert not torch.isnan(_a ).any(), "Samples have nan numbers"
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(lower_order_final=_a )
self.check_over_configs(lower_order_final=_a )
def _a ( self ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=_a , time_step=0 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.full_loop()
SCREAMING_SNAKE_CASE__ : Dict = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.full_loop(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.091 ) < 1E-3
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config(thresholding=_a , dynamic_thresholding_ratio=0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 10
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter.half()
scheduler.set_timesteps(_a )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : List[Any] = model(_a , _a )
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a ).prev_sample
assert sample.dtype == torch.floataa
| 716 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCamelCase :
'''simple docstring'''
a_ : int
a_ : int
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Tuple , _lowerCamelCase : int ):
'''simple docstring'''
__lowerCamelCase : list[list[Edge]] = [[] for _ in range(_lowerCamelCase )]
__lowerCamelCase : List[str] = size
def __getitem__( self : Any , _lowerCamelCase : int ):
'''simple docstring'''
return iter(self._graph[vertex] )
@property
def _snake_case ( self : int ):
'''simple docstring'''
return self._size
def _snake_case ( self : Union[str, Any] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(_lowerCamelCase , _lowerCamelCase ) )
def _snake_case ( self : List[Any] , _lowerCamelCase : int , _lowerCamelCase : int ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = deque([start_vertex] )
__lowerCamelCase : list[int | None] = [None] * self.size
__lowerCamelCase : Optional[int] = 0
while queue:
__lowerCamelCase : Dict = queue.popleft()
__lowerCamelCase : int = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
__lowerCamelCase : Dict = current_distance + edge.weight
__lowerCamelCase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(_lowerCamelCase , _lowerCamelCase )
and new_distance >= dest_vertex_distance
):
continue
__lowerCamelCase : Dict = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 519 |
def _UpperCAmelCase ( UpperCAmelCase : int ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
return 0
elif n == 2:
return 1
else:
__lowerCamelCase : Union[str, Any] = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def _UpperCAmelCase ( UpperCAmelCase : int ):
"""simple docstring"""
__lowerCamelCase : Tuple = 0
__lowerCamelCase : Dict = 2
while digits < n:
index += 1
__lowerCamelCase : str = len(str(fibonacci(UpperCAmelCase ) ) )
return index
def _UpperCAmelCase ( UpperCAmelCase : int = 1_000 ):
"""simple docstring"""
return fibonacci_digits_index(UpperCAmelCase )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 519 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
@slow
def __UpperCAmelCase ( self : int ) -> Optional[Any]:
lowerCAmelCase = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
lowerCAmelCase = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(UpperCAmelCase__ )
from datasets import load_dataset
lowerCAmelCase = load_dataset('nielsr/rvlcdip-demo' )
lowerCAmelCase = dataset['train'][0]['image'].convert('RGB' )
lowerCAmelCase = image_processor(UpperCAmelCase__ , return_tensors='pt' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
lowerCAmelCase = model(**UpperCAmelCase__ )
lowerCAmelCase = outputs.logits
lowerCAmelCase = torch.Size((1, 1_6) )
self.assertEqual(logits.shape , UpperCAmelCase__ )
lowerCAmelCase = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=UpperCAmelCase__ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 513 |
'''simple docstring'''
from __future__ import annotations
def a_ ( lowerCamelCase : list , lowerCamelCase : int ):
# Checks if the entire collection has been sorted
if len(lowerCamelCase ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase , n - 1 )
rec_insertion_sort(lowerCamelCase , n - 1 )
def a_ ( lowerCamelCase : list , lowerCamelCase : int ):
# Checks order between adjacent elements
if index >= len(lowerCamelCase ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
lowerCAmelCase , lowerCAmelCase = (
collection[index],
collection[index - 1],
)
insert_next(lowerCamelCase , index + 1 )
if __name__ == "__main__":
__snake_case =input("""Enter integers separated by spaces: """)
__snake_case =[int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 513 | 1 |
"""simple docstring"""
UpperCamelCase = 0 # The first color of the flag.
UpperCamelCase = 1 # The second color of the flag.
UpperCamelCase = 2 # The third color of the flag.
UpperCamelCase = (red, white, blue)
def _lowerCamelCase ( UpperCAmelCase_ : list ) -> list:
"""simple docstring"""
if not sequence:
return []
if len(UpperCAmelCase_ ) == 1:
return list(UpperCAmelCase_ )
A__ = 0
A__ = len(UpperCAmelCase_ ) - 1
A__ = 0
while mid <= high:
if sequence[mid] == colors[0]:
A__ , A__ = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
A__ , A__ = sequence[high], sequence[mid]
high -= 1
else:
A__ = F"""The elements inside the sequence must contains only {colors} values"""
raise ValueError(UpperCAmelCase_ )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase = input("""Enter numbers separated by commas:\n""").strip()
UpperCamelCase = [int(item.strip()) for item in user_input.split(""",""")]
print(f'{dutch_national_flag_sort(unsorted)}')
| 104 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self ) -> Dict:
A__ = ["a", "b", "c"]
# Defaults to last layer if both are None
A__ , A__ = get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , ["c"] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [2] )
# Out indices set to match out features
A__ , A__ = get_aligned_output_features_output_indices(["a", "c"] , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , ["a", "c"] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [0, 2] )
# Out features set to match out indices
A__ , A__ = get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__ , [0, 2] , SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , ["a", "c"] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [0, 2] )
# Out features selected from negative indices
A__ , A__ = get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__ , [-3, -1] , SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , ["a", "c"] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , [-3, -1] )
def snake_case__ ( self ) -> Dict:
# Stage names must be set
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
verify_out_features_out_indices(["a", "b"] , (0, 1) , SCREAMING_SNAKE_CASE__ )
# Out features must be a list
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
verify_out_features_out_indices(("a", "b") , (0, 1) , ["a", "b"] )
# Out features must be a subset of stage names
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
verify_out_features_out_indices(["a", "b"] , (0, 1) , ["a"] )
# Out indices must be a list or tuple
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
verify_out_features_out_indices(SCREAMING_SNAKE_CASE__ , 0 , ["a", "b"] )
# Out indices must be a subset of stage names
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
verify_out_features_out_indices(SCREAMING_SNAKE_CASE__ , (0, 1) , ["a"] )
# Out features and out indices must be the same length
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
verify_out_features_out_indices(["a", "b"] , (0,) , ["a", "b", "c"] )
# Out features should match out indices
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
verify_out_features_out_indices(["a", "b"] , (0, 2) , ["a", "b", "c"] )
# Out features and out indices should be in order
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
verify_out_features_out_indices(["b", "a"] , (0, 1) , ["a", "b"] )
# Check passes with valid inputs
verify_out_features_out_indices(["a", "b", "d"] , (0, 1, -1) , ["a", "b", "c", "d"] )
def snake_case__ ( self ) -> List[Any]:
A__ = BackboneMixin()
A__ = ["a", "b", "c"]
A__ = ["a", "c"]
A__ = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features , ["a", "c"] )
self.assertEqual(backbone.out_indices , [0, 2] )
# Check out features and indices are updated correctly
A__ = ["a", "b"]
self.assertEqual(backbone.out_features , ["a", "b"] )
self.assertEqual(backbone.out_indices , [0, 1] )
A__ = [-3, -1]
self.assertEqual(backbone.out_features , ["a", "c"] )
self.assertEqual(backbone.out_indices , [-3, -1] )
| 104 | 1 |
'''simple docstring'''
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 a_ ( datasets.Metric ):
def SCREAMING_SNAKE_CASE__ (self) -> Union[str, Any]:
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ (self , __a , __a , __a=4 , __a=False) -> Any:
"""simple docstring"""
__snake_case : Dict = compute_bleu(
reference_corpus=__a , translation_corpus=__a , max_order=__a , smooth=__a)
((__snake_case) ,(__snake_case) ,(__snake_case) ,(__snake_case) ,(__snake_case) ,(__snake_case)) : Dict = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
} | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : float , A : list[float] ) -> 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' )
__snake_case : List[str] = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(A ) )
return round(A , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 61 | 1 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> float:
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(lowerCAmelCase_ ) * abs(lowerCAmelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 358 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ = 1000000 ) -> int:
_a : Optional[int] = set(range(3 , lowerCAmelCase_ , 2 ) )
primes.add(2 )
for p in range(3 , lowerCAmelCase_ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , lowerCAmelCase_ , lowerCAmelCase_ ) ) )
_a : Tuple = [float(lowerCAmelCase_ ) for n in range(limit + 1 )]
for p in primes:
for n in range(lowerCAmelCase_ , limit + 1 , lowerCAmelCase_ ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 358 | 1 |
from random import randint, random
def __lowerCamelCase ( A__ : int , A__ : int , A__ : int , A__ : bool = False , A__ : bool = False , A__ : int = 5 , ) -> list:
lowerCamelCase_ : int = [[-1] * number_of_cells] # Create a highway without any car
lowerCamelCase_ : List[Any] = 0
lowerCamelCase_ : int = max(A__ , 0 )
while i < number_of_cells:
lowerCamelCase_ : Union[str, Any] = (
randint(0 , A__ ) if random_speed else initial_speed
) # Place the cars
i += (
randint(1 , max_speed * 2 ) if random_frequency else frequency
) # Arbitrary number, may need tuning
return highway
def __lowerCamelCase ( A__ : list , A__ : int ) -> int:
lowerCamelCase_ : Dict = 0
lowerCamelCase_ : Optional[Any] = highway_now[car_index + 1 :]
for cell in range(len(A__ ) ): # May need a better name for this
if cells[cell] != -1: # If the cell is not empty then
return distance # we have the distance we wanted
distance += 1
# Here if the car is near the end of the highway
return distance + get_distance(A__ , -1 )
def __lowerCamelCase ( A__ : list , A__ : float , A__ : int ) -> list:
lowerCamelCase_ : Dict = len(A__ )
# Beforce calculations, the highway is empty
lowerCamelCase_ : int = [-1] * number_of_cells
for car_index in range(A__ ):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
lowerCamelCase_ : List[Any] = min(highway_now[car_index] + 1 , A__ )
# Number of empty cell before the next car
lowerCamelCase_ : Optional[Any] = get_distance(A__ , A__ ) - 1
# We can't have the car causing an accident
lowerCamelCase_ : Any = min(next_highway[car_index] , A__ )
if random() < probability:
# Randomly, a driver will slow down
lowerCamelCase_ : Tuple = max(next_highway[car_index] - 1 , 0 )
return next_highway
def __lowerCamelCase ( A__ : list , A__ : int , A__ : float , A__ : int ) -> list:
lowerCamelCase_ : List[Any] = len(highway[0] )
for i in range(A__ ):
lowerCamelCase_ : List[Any] = update(highway[i] , A__ , A__ )
lowerCamelCase_ : Optional[Any] = [-1] * number_of_cells
for car_index in range(A__ ):
lowerCamelCase_ : Union[str, Any] = next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
lowerCamelCase_ : Dict = (car_index + speed) % number_of_cells
# Commit the change of position
lowerCamelCase_ : Tuple = speed
highway.append(A__ )
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| 171 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def __lowerCamelCase ( A__ : Dict=None ) -> Tuple:
if subparsers is not None:
lowerCamelCase_ : List[str] = subparsers.add_parser("""test""" )
else:
lowerCamelCase_ : Optional[int] = argparse.ArgumentParser("""Accelerate test command""" )
parser.add_argument(
"""--config_file""" , default=A__ , help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
) , )
if subparsers is not None:
parser.set_defaults(func=A__ )
return parser
def __lowerCamelCase ( A__ : int ) -> int:
lowerCamelCase_ : Optional[int] = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""] )
if args.config_file is None:
lowerCamelCase_ : Dict = script_name
else:
lowerCamelCase_ : List[Any] = f'''--config_file={args.config_file} {script_name}'''
lowerCamelCase_ : Any = ["""accelerate-launch"""] + test_args.split()
lowerCamelCase_ : Optional[int] = execute_subprocess_async(A__ , env=os.environ.copy() )
if result.returncode == 0:
print("""Test is a success! You are ready for your distributed training!""" )
def __lowerCamelCase ( ) -> int:
lowerCamelCase_ : Dict = test_command_parser()
lowerCamelCase_ : Optional[Any] = parser.parse_args()
test_command(A__ )
if __name__ == "__main__":
main()
| 171 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
a_ = logging.get_logger(__name__)
a_ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all BART models at https://huggingface.co/models?filter=bart
a_ = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
},
}
a_ = {
'''facebook/bart-base''': 1024,
'''facebook/bart-large''': 1024,
'''facebook/bart-large-mnli''': 1024,
'''facebook/bart-large-cnn''': 1024,
'''facebook/bart-large-xsum''': 1024,
'''yjernite/bart_eli5''': 1024,
}
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
_A : Optional[Any] = VOCAB_FILES_NAMES
_A : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_A : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : List[Any] = ["""input_ids""", """attention_mask"""]
_A : List[Any] = BartTokenizer
def __init__(self , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__="replace" , lowercase__="<s>" , lowercase__="</s>" , lowercase__="</s>" , lowercase__="<s>" , lowercase__="<unk>" , lowercase__="<pad>" , lowercase__="<mask>" , lowercase__=False , lowercase__=True , **lowercase__ , ):
super().__init__(
lowercase__ , lowercase__ , tokenizer_file=lowercase__ , errors=lowercase__ , bos_token=lowercase__ , eos_token=lowercase__ , sep_token=lowercase__ , cls_token=lowercase__ , unk_token=lowercase__ , pad_token=lowercase__ , mask_token=lowercase__ , add_prefix_space=lowercase__ , trim_offsets=lowercase__ , **lowercase__ , )
snake_case_ : List[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , lowercase__ ) != add_prefix_space:
snake_case_ : Union[str, Any] = getattr(lowercase__ , pre_tok_state.pop("""type""" ) )
snake_case_ : Union[str, Any] = add_prefix_space
snake_case_ : Optional[Any] = pre_tok_class(**lowercase__ )
snake_case_ : List[Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
snake_case_ : Dict = """post_processor"""
snake_case_ : Tuple = getattr(self.backend_tokenizer , lowercase__ , lowercase__ )
if tokenizer_component_instance:
snake_case_ : Tuple = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
snake_case_ : Tuple = tuple(state["""sep"""] )
if "cls" in state:
snake_case_ : int = tuple(state["""cls"""] )
snake_case_ : Optional[int] = False
if state.get("""add_prefix_space""" , lowercase__ ) != add_prefix_space:
snake_case_ : Union[str, Any] = add_prefix_space
snake_case_ : Any = True
if state.get("""trim_offsets""" , lowercase__ ) != trim_offsets:
snake_case_ : List[Any] = trim_offsets
snake_case_ : List[str] = True
if changes_to_apply:
snake_case_ : Dict = getattr(lowercase__ , state.pop("""type""" ) )
snake_case_ : Optional[int] = component_class(**lowercase__ )
setattr(self.backend_tokenizer , lowercase__ , lowercase__ )
@property
def __UpperCamelCase (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 __UpperCamelCase (self , lowercase__ ):
snake_case_ : Dict = AddedToken(lowercase__ , lstrip=lowercase__ , rstrip=lowercase__ ) if isinstance(lowercase__ , lowercase__ ) else value
snake_case_ : List[str] = value
def __UpperCamelCase (self , *lowercase__ , **lowercase__ ):
snake_case_ : List[Any] = kwargs.get("""is_split_into_words""" , lowercase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*lowercase__ , **lowercase__ )
def __UpperCamelCase (self , *lowercase__ , **lowercase__ ):
snake_case_ : int = kwargs.get("""is_split_into_words""" , lowercase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*lowercase__ , **lowercase__ )
def __UpperCamelCase (self , lowercase__ , lowercase__ = None ):
snake_case_ : Union[str, Any] = self._tokenizer.model.save(lowercase__ , name=lowercase__ )
return tuple(lowercase__ )
def __UpperCamelCase (self , lowercase__ , lowercase__=None ):
snake_case_ : Optional[int] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __UpperCamelCase (self , lowercase__ , lowercase__ = None ):
snake_case_ : List[Any] = [self.sep_token_id]
snake_case_ : Tuple = [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]
| 480 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a_ = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processing_trocr''': ['''TrOCRProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TrOCRForCausalLM''',
'''TrOCRPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
a_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 480 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 579 |
"""simple docstring"""
from timeit import timeit
def A__ ( A__ ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
_UpperCAmelCase = 0
while number:
number &= number - 1
result += 1
return result
def A__ ( A__ ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
_UpperCAmelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def A__ ( ) -> None:
'''simple docstring'''
def do_benchmark(A__ ) -> None:
_UpperCAmelCase = "import __main__ as z"
print(F"""Benchmark when {number = }:""" )
print(F"""{get_set_bits_count_using_modulo_operator(A__ ) = }""" )
_UpperCAmelCase = timeit("z.get_set_bits_count_using_modulo_operator(25)" , setup=A__ )
print(F"""timeit() runs in {timing} seconds""" )
print(F"""{get_set_bits_count_using_brian_kernighans_algorithm(A__ ) = }""" )
_UpperCAmelCase = timeit(
"z.get_set_bits_count_using_brian_kernighans_algorithm(25)" , setup=A__ , )
print(F"""timeit() runs in {timing} seconds""" )
for number in (25, 37, 58, 0):
do_benchmark(A__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 579 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
UpperCamelCase_ = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase_ = BASE_URL + '/user'
# https://github.com/settings/tokens
UpperCamelCase_ = os.environ.get('USER_TOKEN', '')
def _UpperCAmelCase ( A ):
'''simple docstring'''
UpperCAmelCase__ ={
"Authorization": F"""token {auth_token}""",
"Accept": "application/vnd.github.v3+json",
}
return requests.get(A , headers=A ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f"""{key}: {value}""")
else:
raise ValueError('\'USER_TOKEN\' field cannot be empty.')
| 625 |
from math import factorial
def _UpperCAmelCase ( A = 20 ):
'''simple docstring'''
UpperCAmelCase__ =2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase__ =n // 2
return int(factorial(A ) / (factorial(A ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(20))
else:
try:
UpperCamelCase_ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number.')
| 625 | 1 |
def _lowerCAmelCase ( _lowerCAmelCase = 1 , _lowerCAmelCase = 1000 ) -> int:
'''simple docstring'''
__snake_case = 1
__snake_case = 0
for divide_by_number in range(_lowerCAmelCase , digit + 1 ):
__snake_case = []
__snake_case = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(_lowerCAmelCase ):
__snake_case = len(_lowerCAmelCase )
__snake_case = divide_by_number
else:
has_been_divided.append(_lowerCAmelCase )
__snake_case = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 473 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class UpperCamelCase( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : str ) -> Dict:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
__snake_case = 1
__snake_case = 3
__snake_case = (3_2, 3_2)
__snake_case = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE )
return image
@property
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , )
return model
@property
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
return model
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(SCREAMING_SNAKE_CASE )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
def extract(*SCREAMING_SNAKE_CASE : List[str] , **SCREAMING_SNAKE_CASE : List[Any] ):
class UpperCamelCase:
def __init__( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
__snake_case = torch.ones([0] )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
'''simple docstring'''
self.pixel_values.to(SCREAMING_SNAKE_CASE )
return self
return Out()
return extract
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> Dict:
'''simple docstring'''
__snake_case = "cpu" # ensure determinism for the device-dependent torch.Generator
__snake_case = self.dummy_cond_unet
__snake_case = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=SCREAMING_SNAKE_CASE , set_alpha_to_one=SCREAMING_SNAKE_CASE , )
__snake_case = self.dummy_vae
__snake_case = self.dummy_text_encoder
__snake_case = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# make sure here that pndm scheduler skips prk
__snake_case = StableDiffusionPipeline(
unet=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE , vae=SCREAMING_SNAKE_CASE , text_encoder=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , safety_checker=SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__snake_case = sd_pipe.to(SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
__snake_case = "A painting of a squirrel eating a burger"
__snake_case = torch.Generator(device=SCREAMING_SNAKE_CASE ).manual_seed(0 )
__snake_case = sd_pipe([prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" )
__snake_case = output.images
__snake_case = torch.Generator(device=SCREAMING_SNAKE_CASE ).manual_seed(0 )
__snake_case = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , return_dict=SCREAMING_SNAKE_CASE , )[0]
__snake_case = image[0, -3:, -3:, -1]
__snake_case = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__snake_case = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case = "cpu" # ensure determinism for the device-dependent torch.Generator
__snake_case = self.dummy_cond_unet
__snake_case = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE )
__snake_case = self.dummy_vae
__snake_case = self.dummy_text_encoder
__snake_case = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# make sure here that pndm scheduler skips prk
__snake_case = StableDiffusionPipeline(
unet=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE , vae=SCREAMING_SNAKE_CASE , text_encoder=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , safety_checker=SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__snake_case = sd_pipe.to(SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
__snake_case = "A painting of a squirrel eating a burger"
__snake_case = torch.Generator(device=SCREAMING_SNAKE_CASE ).manual_seed(0 )
__snake_case = sd_pipe([prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" )
__snake_case = output.images
__snake_case = torch.Generator(device=SCREAMING_SNAKE_CASE ).manual_seed(0 )
__snake_case = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , return_dict=SCREAMING_SNAKE_CASE , )[0]
__snake_case = image[0, -3:, -3:, -1]
__snake_case = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__snake_case = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
__snake_case = StableDiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-lms-pipe" , safety_checker=SCREAMING_SNAKE_CASE )
assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
assert isinstance(pipe.scheduler , SCREAMING_SNAKE_CASE )
assert pipe.safety_checker is None
__snake_case = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(SCREAMING_SNAKE_CASE )
__snake_case = StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__snake_case = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case = self.dummy_cond_unet
__snake_case = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE )
__snake_case = self.dummy_vae
__snake_case = self.dummy_text_encoder
__snake_case = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
# put models in fp16
__snake_case = unet.half()
__snake_case = vae.half()
__snake_case = bert.half()
# make sure here that pndm scheduler skips prk
__snake_case = StableDiffusionPipeline(
unet=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE , vae=SCREAMING_SNAKE_CASE , text_encoder=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , safety_checker=SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__snake_case = sd_pipe.to(SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
__snake_case = "A painting of a squirrel eating a burger"
__snake_case = sd_pipe([prompt] , num_inference_steps=2 , output_type="np" ).images
assert image.shape == (1, 6_4, 6_4, 3)
@nightly
@require_torch_gpu
class UpperCamelCase( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" , safety_checker=SCREAMING_SNAKE_CASE )
__snake_case = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
__snake_case = sd_pipe.to(SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
__snake_case = (
"portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
" children from bahnhof zoo, detailed "
)
__snake_case = 4_0_0_3_6_6_0_3_4_6
__snake_case = 7
# without safety guidance (sld_guidance_scale = 0)
__snake_case = torch.manual_seed(SCREAMING_SNAKE_CASE )
__snake_case = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=SCREAMING_SNAKE_CASE , num_inference_steps=5_0 , output_type="np" , width=5_1_2 , height=5_1_2 , sld_guidance_scale=0 , )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
__snake_case = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
# without safety guidance (strong configuration)
__snake_case = torch.manual_seed(SCREAMING_SNAKE_CASE )
__snake_case = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=SCREAMING_SNAKE_CASE , num_inference_steps=5_0 , output_type="np" , width=5_1_2 , height=5_1_2 , sld_guidance_scale=2_0_0_0 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
__snake_case = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" , safety_checker=SCREAMING_SNAKE_CASE )
__snake_case = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
__snake_case = sd_pipe.to(SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
__snake_case = "padme amidala taking a bath artwork, safe for work, no nudity"
__snake_case = 2_7_3_4_9_7_1_7_5_5
__snake_case = 7
__snake_case = torch.manual_seed(SCREAMING_SNAKE_CASE )
__snake_case = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=SCREAMING_SNAKE_CASE , num_inference_steps=5_0 , output_type="np" , width=5_1_2 , height=5_1_2 , sld_guidance_scale=0 , )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
__snake_case = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
__snake_case = torch.manual_seed(SCREAMING_SNAKE_CASE )
__snake_case = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=SCREAMING_SNAKE_CASE , num_inference_steps=5_0 , output_type="np" , width=5_1_2 , height=5_1_2 , sld_guidance_scale=2_0_0_0 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
__snake_case = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> int:
'''simple docstring'''
__snake_case = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" )
__snake_case = sd_pipe.to(SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
__snake_case = (
"the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
" leyendecker"
)
__snake_case = 1_0_4_4_3_5_5_2_3_4
__snake_case = 1_2
__snake_case = torch.manual_seed(SCREAMING_SNAKE_CASE )
__snake_case = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=SCREAMING_SNAKE_CASE , num_inference_steps=5_0 , output_type="np" , width=5_1_2 , height=5_1_2 , sld_guidance_scale=0 , )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
__snake_case = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 5_1_2, 5_1_2, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7
__snake_case = torch.manual_seed(SCREAMING_SNAKE_CASE )
__snake_case = sd_pipe(
[prompt] , generator=SCREAMING_SNAKE_CASE , guidance_scale=SCREAMING_SNAKE_CASE , num_inference_steps=5_0 , output_type="np" , width=5_1_2 , height=5_1_2 , sld_guidance_scale=2_0_0_0 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
__snake_case = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 5_1_2, 5_1_2, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 473 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def a__ ( lowerCAmelCase__=None ) -> List[Any]:
if subparsers is not None:
UpperCAmelCase__ : Union[str, Any] = subparsers.add_parser('''test''' )
else:
UpperCAmelCase__ : List[Any] = argparse.ArgumentParser('''Accelerate test command''' )
parser.add_argument(
'''--config_file''' , default=lowerCAmelCase__ , help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) , )
if subparsers is not None:
parser.set_defaults(func=lowerCAmelCase__ )
return parser
def a__ ( lowerCAmelCase__ ) -> Union[str, Any]:
UpperCAmelCase__ : str = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )
if args.config_file is None:
UpperCAmelCase__ : Dict = script_name
else:
UpperCAmelCase__ : Optional[Any] = F"""--config_file={args.config_file} {script_name}"""
UpperCAmelCase__ : Any = ['''accelerate-launch'''] + test_args.split()
UpperCAmelCase__ : List[str] = execute_subprocess_async(lowerCAmelCase__ , env=os.environ.copy() )
if result.returncode == 0:
print('''Test is a success! You are ready for your distributed training!''' )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase__ : str = test_command_parser()
UpperCAmelCase__ : Tuple = parser.parse_args()
test_command(lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 75 |
'''simple docstring'''
import random
from typing import Any
def a__ ( lowerCAmelCase__ ) -> list[Any]:
for _ in range(len(lowerCAmelCase__ ) ):
UpperCAmelCase__ : int = random.randint(0 , len(lowerCAmelCase__ ) - 1 )
UpperCAmelCase__ : Optional[int] = random.randint(0 , len(lowerCAmelCase__ ) - 1 )
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = data[b], data[a]
return data
if __name__ == "__main__":
UpperCamelCase__ = [0, 1, 2, 3, 4, 5, 6, 7]
UpperCamelCase__ = ['''python''', '''says''', '''hello''', '''!''']
print('''Fisher-Yates Shuffle:''')
print('''List''', integers, strings)
print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 75 | 1 |
import sys
UpperCamelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = 1
for digit in s:
product *= int(UpperCAmelCase__ )
return product
def __magic_name__( __UpperCAmelCase = N ) -> int:
'''simple docstring'''
_lowerCamelCase = -sys.maxsize - 1
_lowerCamelCase = n[:13]
_lowerCamelCase = 13
while cur_index < len(UpperCAmelCase__ ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
_lowerCamelCase = substr[1:] + n[cur_index]
cur_index += 1
else:
_lowerCamelCase = max(UpperCAmelCase__ , str_eval(UpperCAmelCase__ ) )
_lowerCamelCase = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f'''{solution() = }''') | 708 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
_lowerCamelCase = dict(zip(A_ , range(len(A_ ) ) ) )
_lowerCamelCase = {
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
_lowerCamelCase = {
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 1_60_00,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase = os.path.join(self.tmpdirname , A_ )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
with open(self.feature_extraction_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
# load decoder from hub
_lowerCamelCase = '''hf-internal-testing/ngram-beam-search-decoder'''
def UpperCamelCase_ ( self , **A_ ) -> str:
"""simple docstring"""
_lowerCamelCase = self.add_kwargs_tokens_map.copy()
kwargs.update(A_ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **A_ )
def UpperCamelCase_ ( self , **A_ ) -> Optional[Any]:
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **A_ )
def UpperCamelCase_ ( self , **A_ ) -> int:
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
processor.save_pretrained(self.tmpdirname )
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , A_ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , A_ )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , A_ )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(A_ , '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=A_ , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = floats_list((3, 10_00) )
_lowerCamelCase = feature_extractor(A_ , return_tensors='''np''' )
_lowerCamelCase = processor(A_ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = '''This is a test string'''
_lowerCamelCase = processor(text=A_ )
_lowerCamelCase = tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase_ ( self , A_=(2, 10, 16) , A_=77 ) -> Optional[Any]:
"""simple docstring"""
np.random.seed(A_ )
return np.random.rand(*A_ )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits(shape=(10, 16) , seed=13 )
_lowerCamelCase = processor.decode(A_ )
_lowerCamelCase = decoder.decode_beams(A_ )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('''</s> <s> </s>''' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def UpperCamelCase_ ( self , A_ ) -> int:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
_lowerCamelCase = processor.batch_decode(A_ )
else:
with get_context(A_ ).Pool() as pool:
_lowerCamelCase = processor.batch_decode(A_ , A_ )
_lowerCamelCase = list(A_ )
with get_context('''fork''' ).Pool() as p:
_lowerCamelCase = decoder.decode_beams_batch(A_ , A_ )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(A_ , decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] , decoded_processor.text )
self.assertListEqual(A_ , decoded_processor.logit_score )
self.assertListEqual(A_ , decoded_processor.lm_score )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = 15
_lowerCamelCase = -20.0
_lowerCamelCase = -4.0
_lowerCamelCase = processor.batch_decode(
A_ , beam_width=A_ , beam_prune_logp=A_ , token_min_logp=A_ , )
_lowerCamelCase = decoded_processor_out.text
_lowerCamelCase = list(A_ )
with get_context('''fork''' ).Pool() as pool:
_lowerCamelCase = decoder.decode_beams_batch(
A_ , A_ , beam_width=A_ , beam_prune_logp=A_ , token_min_logp=A_ , )
_lowerCamelCase = [d[0][0] for d in decoded_decoder_out]
_lowerCamelCase = [d[0][2] for d in decoded_decoder_out]
_lowerCamelCase = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(A_ , A_ )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] , A_ )
self.assertTrue(np.array_equal(A_ , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , A_ , atol=1E-3 ) )
self.assertTrue(np.array_equal(A_ , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , A_ , atol=1E-3 ) )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = 2.0
_lowerCamelCase = 5.0
_lowerCamelCase = -20.0
_lowerCamelCase = True
_lowerCamelCase = processor.batch_decode(
A_ , alpha=A_ , beta=A_ , unk_score_offset=A_ , lm_score_boundary=A_ , )
_lowerCamelCase = decoded_processor_out.text
_lowerCamelCase = list(A_ )
decoder.reset_params(
alpha=A_ , beta=A_ , unk_score_offset=A_ , lm_score_boundary=A_ , )
with get_context('''fork''' ).Pool() as pool:
_lowerCamelCase = decoder.decode_beams_batch(
A_ , A_ , )
_lowerCamelCase = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(A_ , A_ )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] , A_ )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
_lowerCamelCase = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_lowerCamelCase = os.listdir(A_ )
_lowerCamelCase = ['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(A_ , A_ )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = snapshot_download('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained(A_ )
_lowerCamelCase = processor.decoder.model_container[processor.decoder._model_key]
_lowerCamelCase = Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
_lowerCamelCase = os.listdir(A_ )
_lowerCamelCase = os.listdir(A_ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(A_ , A_ )
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = floats_list((3, 10_00) )
_lowerCamelCase = processor_wavaveca(A_ , return_tensors='''np''' )
_lowerCamelCase = processor_auto(A_ , return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = processor_wavaveca.batch_decode(A_ )
_lowerCamelCase = processor_auto.batch_decode(A_ )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
_lowerCamelCase = self.get_feature_extractor()
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_decoder()
_lowerCamelCase = WavaVecaProcessorWithLM(tokenizer=A_ , feature_extractor=A_ , decoder=A_ )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
@staticmethod
def UpperCamelCase_ ( A_ , A_ ) -> str:
"""simple docstring"""
_lowerCamelCase = [d[key] for d in offsets]
return retrieved_list
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = self._get_dummy_logits()[0]
_lowerCamelCase = processor.decode(A_ , output_word_offsets=A_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(A_ , A_ ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''end_offset''' ) , [1, 3, 5] )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
_lowerCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
_lowerCamelCase = self._get_dummy_logits()
_lowerCamelCase = processor.batch_decode(A_ , output_word_offsets=A_ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(A_ , A_ ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) for o in outputs['''word_offsets''']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''end_offset''' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
import torch
_lowerCamelCase = load_dataset('''common_voice''' , '''en''' , split='''train''' , streaming=A_ )
_lowerCamelCase = ds.cast_column('''audio''' , datasets.Audio(sampling_rate=1_60_00 ) )
_lowerCamelCase = iter(A_ )
_lowerCamelCase = next(A_ )
_lowerCamelCase = AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
_lowerCamelCase = WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
_lowerCamelCase = processor(sample['''audio''']['''array'''] , return_tensors='''pt''' ).input_values
with torch.no_grad():
_lowerCamelCase = model(A_ ).logits.cpu().numpy()
_lowerCamelCase = processor.decode(logits[0] , output_word_offsets=A_ )
_lowerCamelCase = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
_lowerCamelCase = [
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
_lowerCamelCase = '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) , A_ )
self.assertEqual(''' '''.join(self.get_from_offsets(A_ , '''word''' ) ) , output.text )
# output times
_lowerCamelCase = torch.tensor(self.get_from_offsets(A_ , '''start_time''' ) )
_lowerCamelCase = torch.tensor(self.get_from_offsets(A_ , '''end_time''' ) )
# fmt: off
_lowerCamelCase = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
_lowerCamelCase = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(A_ , A_ , atol=0.01 ) )
self.assertTrue(torch.allclose(A_ , A_ , atol=0.01 ) ) | 638 | 0 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
snake_case : str = [10, 20, 30, 40, 50, 60]
snake_case : Any = [2, 4, 6, 8, 10, 12]
snake_case : Optional[Any] = 100
self.assertEqual(kp.calc_profit(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) , 210 )
def lowerCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
self.assertRaisesRegex(UpperCamelCase__ , '''max_weight must greater than zero.''' )
def lowerCAmelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self.assertRaisesRegex(UpperCamelCase__ , '''Weight can not be negative.''' )
def lowerCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
self.assertRaisesRegex(UpperCamelCase__ , '''Profit can not be negative.''' )
def lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
self.assertRaisesRegex(UpperCamelCase__ , '''max_weight must greater than zero.''' )
def lowerCAmelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
self.assertRaisesRegex(
UpperCamelCase__ , '''The length of profit and weight must be same.''' )
if __name__ == "__main__":
unittest.main()
| 638 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["EncoderDecoderModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["TFEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["FlaxEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 638 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=lowercase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""torch""", """scipy"""]
def __init__( self : List[Any], *_snake_case : int, **_snake_case : Dict ) ->Dict:
requires_backends(self, ['torch', 'scipy'] )
@classmethod
def lowercase_ ( cls : Optional[int], *_snake_case : Union[str, Any], **_snake_case : str ) ->int:
requires_backends(cls, ['torch', 'scipy'] )
@classmethod
def lowercase_ ( cls : Any, *_snake_case : List[Any], **_snake_case : Optional[Any] ) ->List[str]:
requires_backends(cls, ['torch', 'scipy'] )
| 700 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ (A : Optional[int] , A : Tuple , A : List[Any] , A : List[str]="attention" ):
snake_case__ : str = params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
snake_case__ : List[str] = params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
snake_case__ : Optional[int] = params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
snake_case__ : Union[str, Any] = params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
return k, o, q, v
def lowercase_ (A : Tuple , A : Union[str, Any] , A : int , A : Any=False ):
if split_mlp_wi:
snake_case__ : Dict = params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
snake_case__ : List[str] = params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
snake_case__ : Optional[Any] = (wi_a, wi_a)
else:
snake_case__ : Optional[Any] = params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']
snake_case__ : Tuple = params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
return wi, wo
def lowercase_ (A : Optional[int] , A : Dict , A : Any , A : List[str] ):
return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def lowercase_ (A : dict , *, A : int , A : bool ):
snake_case__ : Dict = traverse_util.flatten_dict(variables['target'] )
snake_case__ : Union[str, Any] = {'/'.join(A ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
snake_case__ : Union[str, Any] = 'encoder/layers_0/mlp/wi_0/kernel' in old
print('Split MLP:' , A )
snake_case__ : int = collections.OrderedDict()
# Shared embeddings.
snake_case__ : Dict = old['token_embedder/embedding']
# Encoder.
for i in range(A ):
# Block i, layer 0 (Self Attention).
snake_case__ : Dict = tax_layer_norm_lookup(A , A , 'encoder' , 'pre_attention_layer_norm' )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Dict = tax_attention_lookup(A , A , 'encoder' , 'attention' )
snake_case__ : Optional[Any] = layer_norm
snake_case__ : Union[str, Any] = k.T
snake_case__ : List[Any] = o.T
snake_case__ : Any = q.T
snake_case__ : Union[str, Any] = v.T
# Block i, layer 1 (MLP).
snake_case__ : List[str] = tax_layer_norm_lookup(A , A , 'encoder' , 'pre_mlp_layer_norm' )
snake_case__ , snake_case__ : Dict = tax_mlp_lookup(A , A , 'encoder' , A )
snake_case__ : Optional[int] = layer_norm
if split_mlp_wi:
snake_case__ : Union[str, Any] = wi[0].T
snake_case__ : int = wi[1].T
else:
snake_case__ : Optional[int] = wi.T
snake_case__ : Tuple = wo.T
snake_case__ : Optional[int] = old[
'encoder/relpos_bias/rel_embedding'
].T
snake_case__ : str = old['encoder/encoder_norm/scale']
if not is_encoder_only:
# Decoder.
for i in range(A ):
# Block i, layer 0 (Self Attention).
snake_case__ : List[str] = tax_layer_norm_lookup(A , A , 'decoder' , 'pre_self_attention_layer_norm' )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[str] = tax_attention_lookup(A , A , 'decoder' , 'self_attention' )
snake_case__ : int = layer_norm
snake_case__ : List[Any] = k.T
snake_case__ : Tuple = o.T
snake_case__ : Any = q.T
snake_case__ : List[str] = v.T
# Block i, layer 1 (Cross Attention).
snake_case__ : List[str] = tax_layer_norm_lookup(A , A , 'decoder' , 'pre_cross_attention_layer_norm' )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : int = tax_attention_lookup(A , A , 'decoder' , 'encoder_decoder_attention' )
snake_case__ : List[Any] = layer_norm
snake_case__ : Union[str, Any] = k.T
snake_case__ : List[str] = o.T
snake_case__ : List[str] = q.T
snake_case__ : List[Any] = v.T
# Block i, layer 2 (MLP).
snake_case__ : Any = tax_layer_norm_lookup(A , A , 'decoder' , 'pre_mlp_layer_norm' )
snake_case__ , snake_case__ : Tuple = tax_mlp_lookup(A , A , 'decoder' , A )
snake_case__ : List[str] = layer_norm
if split_mlp_wi:
snake_case__ : Any = wi[0].T
snake_case__ : str = wi[1].T
else:
snake_case__ : Optional[int] = wi.T
snake_case__ : int = wo.T
snake_case__ : Dict = old['decoder/decoder_norm/scale']
snake_case__ : int = old[
'decoder/relpos_bias/rel_embedding'
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
snake_case__ : int = old['decoder/logits_dense/kernel'].T
return new
def lowercase_ (A : List[Any] , A : bool ):
snake_case__ : Dict = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
snake_case__ : Any = state_dict['shared.weight']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
snake_case__ : Optional[int] = state_dict['shared.weight']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('Using shared word embeddings as lm_head.' )
snake_case__ : List[Any] = state_dict['shared.weight']
return state_dict
def lowercase_ (A : Union[str, Any] , A : Any , A : Union[str, Any] , A : Tuple ):
snake_case__ : Optional[Any] = checkpoints.load_tax_checkpoint(A )
snake_case__ : List[str] = convert_tax_to_pytorch(A , num_layers=config.num_layers , is_encoder_only=A )
snake_case__ : Optional[int] = make_state_dict(A , A )
model.load_state_dict(A , strict=A )
def lowercase_ (A : List[str] , A : Union[str, Any] , A : Optional[Any] , A : bool = False ):
snake_case__ : str = TaConfig.from_json_file(A )
print(F'''Building PyTorch model from configuration: {config}''' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
snake_case__ : List[str] = TaEncoderModel(A )
else:
snake_case__ : str = TaForConditionalGeneration(A )
# Load weights from tf checkpoint
load_tax_weights_in_ta(A , A , A , A )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(A )
# Verify that we can load the checkpoint.
model.from_pretrained(A )
print('Done' )
if __name__ == "__main__":
a_ :Tuple = argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
)
a_ :List[Any] = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 243 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class _UpperCAmelCase ( unittest.TestCase , _UpperCamelCase ):
"""simple docstring"""
def lowercase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase = load_tool('text-to-speech' )
self.tool.setup()
def lowercase ( self : Union[str, Any] ) -> Dict:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__lowerCAmelCase = self.tool('hey' )
__lowerCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85] ) , ) )
def lowercase ( self : Union[str, Any] ) -> Any:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
__lowerCAmelCase = self.tool('hey' )
__lowerCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85] ) , ) )
| 53 | import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self : List[str] , lowercase__ : Any , lowercase__ : List[Any]=7 , lowercase__ : List[str]=3 , lowercase__ : str=18 , lowercase__ : List[Any]=30 , lowercase__ : Optional[int]=4_00 , lowercase__ : Dict=True , lowercase__ : List[str]=None , lowercase__ : int=True , lowercase__ : Tuple=None , lowercase__ : int=True , lowercase__ : Tuple=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , lowercase__ : Optional[int]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , lowercase__ : Any=True , ):
_lowerCAmelCase = size if size is not None else {'height': 2_24, 'width': 2_24}
_lowerCAmelCase = crop_size if crop_size is not None else {'height': 18, 'width': 18}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean
_lowerCAmelCase = image_std
_lowerCAmelCase = do_convert_rgb
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def SCREAMING_SNAKE_CASE__ ( self : List[str] , lowercase__ : Tuple=False , lowercase__ : List[Any]=False , lowercase__ : str=False ):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
_lowerCAmelCase = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
2_55 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
_lowerCAmelCase = []
for i in range(self.batch_size ):
_lowerCAmelCase , _lowerCAmelCase = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(2_55 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
_lowerCAmelCase = [Image.fromarray(np.moveaxis(lowercase__ , 0 , -1 ) ) for x in image_inputs]
if torchify:
_lowerCAmelCase = [torch.from_numpy(lowercase__ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase ,unittest.TestCase ):
UpperCamelCase__ =ChineseCLIPImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
_lowerCAmelCase = ChineseCLIPImageProcessingTester(self , do_center_crop=lowercase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowercase__ , 'size' ) )
self.assertTrue(hasattr(lowercase__ , 'do_center_crop' ) )
self.assertTrue(hasattr(lowercase__ , 'center_crop' ) )
self.assertTrue(hasattr(lowercase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowercase__ , 'image_mean' ) )
self.assertTrue(hasattr(lowercase__ , 'image_std' ) )
self.assertTrue(hasattr(lowercase__ , 'do_convert_rgb' ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 2_24, 'width': 2_24} )
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18} )
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
pass
def SCREAMING_SNAKE_CASE__ ( self : str ):
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowercase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__ , numpify=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , np.ndarray )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowercase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def SCREAMING_SNAKE_CASE__ ( self : int ):
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__ , torchify=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , torch.Tensor )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowercase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
@require_torch
@require_vision
class lowerCamelCase__ ( UpperCAmelCase ,unittest.TestCase ):
UpperCamelCase__ =ChineseCLIPImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
_lowerCAmelCase = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=lowercase__ )
_lowerCAmelCase = 3
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowercase__ , 'size' ) )
self.assertTrue(hasattr(lowercase__ , 'do_center_crop' ) )
self.assertTrue(hasattr(lowercase__ , 'center_crop' ) )
self.assertTrue(hasattr(lowercase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowercase__ , 'image_mean' ) )
self.assertTrue(hasattr(lowercase__ , 'image_std' ) )
self.assertTrue(hasattr(lowercase__ , 'do_convert_rgb' ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
# Initialize image_processing
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowercase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 192 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 719 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
_A : int = logging.get_logger(__name__)
_A : int = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
_A : Optional[int] = {
'vocab_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
),
},
'merges_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
),
},
'tokenizer_file': {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
'roberta-base-openai-detector': (
'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
),
'roberta-large-openai-detector': (
'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
),
},
}
_A : Any = {
'roberta-base': 5_12,
'roberta-large': 5_12,
'roberta-large-mnli': 5_12,
'distilroberta-base': 5_12,
'roberta-base-openai-detector': 5_12,
'roberta-large-openai-detector': 5_12,
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : str = VOCAB_FILES_NAMES
_UpperCAmelCase : str = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase : int = ["input_ids", "attention_mask"]
_UpperCAmelCase : List[str] = RobertaTokenizer
def __init__( self : List[str] , A : List[str]=None , A : List[Any]=None , A : Optional[int]=None , A : Tuple="replace" , A : int="<s>" , A : Any="</s>" , A : Optional[int]="</s>" , A : Tuple="<s>" , A : int="<unk>" , A : Optional[int]="<pad>" , A : Tuple="<mask>" , A : Optional[Any]=False , A : Optional[int]=True , **A : Optional[int] , ) ->Dict:
super().__init__(
A , A , tokenizer_file=A , errors=A , bos_token=A , eos_token=A , sep_token=A , cls_token=A , unk_token=A , pad_token=A , mask_token=A , add_prefix_space=A , trim_offsets=A , **A , )
lowerCamelCase__ : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , A ) != add_prefix_space:
lowerCamelCase__ : Union[str, Any] = getattr(A , pre_tok_state.pop('''type''' ) )
lowerCamelCase__ : Optional[int] = add_prefix_space
lowerCamelCase__ : List[str] = pre_tok_class(**A )
lowerCamelCase__ : Any = add_prefix_space
lowerCamelCase__ : Any = '''post_processor'''
lowerCamelCase__ : str = getattr(self.backend_tokenizer , A , A )
if tokenizer_component_instance:
lowerCamelCase__ : str = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowerCamelCase__ : Any = tuple(state['''sep'''] )
if "cls" in state:
lowerCamelCase__ : Tuple = tuple(state['''cls'''] )
lowerCamelCase__ : Tuple = False
if state.get('''add_prefix_space''' , A ) != add_prefix_space:
lowerCamelCase__ : Optional[Any] = add_prefix_space
lowerCamelCase__ : Optional[Any] = True
if state.get('''trim_offsets''' , A ) != trim_offsets:
lowerCamelCase__ : Tuple = trim_offsets
lowerCamelCase__ : str = True
if changes_to_apply:
lowerCamelCase__ : Optional[int] = getattr(A , state.pop('''type''' ) )
lowerCamelCase__ : str = component_class(**A )
setattr(self.backend_tokenizer , A , A )
@property
def __lowerCamelCase ( self : List[str] ) ->str:
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __lowerCamelCase ( self : int , A : List[Any] ) ->List[Any]:
lowerCamelCase__ : Union[str, Any] = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else value
lowerCamelCase__ : int = value
def __lowerCamelCase ( self : Tuple , *A : List[Any] , **A : Optional[Any] ) ->BatchEncoding:
lowerCamelCase__ : Dict = kwargs.get('''is_split_into_words''' , A )
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(*A , **A )
def __lowerCamelCase ( self : Tuple , *A : List[Any] , **A : Tuple ) ->BatchEncoding:
lowerCamelCase__ : Optional[Any] = kwargs.get('''is_split_into_words''' , A )
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(*A , **A )
def __lowerCamelCase ( self : List[str] , A : str , A : Optional[str] = None ) ->Tuple[str]:
lowerCamelCase__ : List[str] = self._tokenizer.model.save(A , name=A )
return tuple(A )
def __lowerCamelCase ( self : Dict , A : List[str] , A : List[Any]=None ) ->List[str]:
lowerCamelCase__ : List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __lowerCamelCase ( self : Optional[int] , A : List[int] , A : Optional[List[int]] = None ) ->List[int]:
lowerCamelCase__ : int = [self.sep_token_id]
lowerCamelCase__ : 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]
| 130 | 0 |
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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : str = logging.get_logger(__name__)
def lowerCAmelCase__ ( _a : List[Any] , _a : List[Any]=False , _a : int=False , _a : Optional[Any]=False ):
snake_case_ : Any = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''transformer.blocks.{i}.norm1.weight''', F'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.norm1.bias''', F'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''transformer.blocks.{i}.attn.proj.weight''', F'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(F'''transformer.blocks.{i}.attn.proj.bias''', F'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''transformer.blocks.{i}.norm2.weight''', F'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.norm2.bias''', F'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(F'''transformer.blocks.{i}.mlp.fc1.weight''', F'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc1.bias''', F'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.weight''', F'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.bias''', F'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
("text_embeddings.word_embeddings.weight", "vilt.embeddings.text_embeddings.word_embeddings.weight"),
(
"text_embeddings.position_embeddings.weight",
"vilt.embeddings.text_embeddings.position_embeddings.weight",
),
("text_embeddings.position_ids", "vilt.embeddings.text_embeddings.position_ids"),
(
"text_embeddings.token_type_embeddings.weight",
"vilt.embeddings.text_embeddings.token_type_embeddings.weight",
),
("text_embeddings.LayerNorm.weight", "vilt.embeddings.text_embeddings.LayerNorm.weight"),
("text_embeddings.LayerNorm.bias", "vilt.embeddings.text_embeddings.LayerNorm.bias"),
# patch embeddings
("transformer.cls_token", "vilt.embeddings.cls_token"),
("transformer.patch_embed.proj.weight", "vilt.embeddings.patch_embeddings.projection.weight"),
("transformer.patch_embed.proj.bias", "vilt.embeddings.patch_embeddings.projection.bias"),
("transformer.pos_embed", "vilt.embeddings.position_embeddings"),
# token type embeddings
("token_type_embeddings.weight", "vilt.embeddings.token_type_embeddings.weight"),
] )
# final layernorm + pooler
rename_keys.extend(
[
("transformer.norm.weight", "vilt.layernorm.weight"),
("transformer.norm.bias", "vilt.layernorm.bias"),
("pooler.dense.weight", "vilt.pooler.dense.weight"),
("pooler.dense.bias", "vilt.pooler.dense.bias"),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
("vqa_classifier.0.weight", "classifier.0.weight"),
("vqa_classifier.0.bias", "classifier.0.bias"),
("vqa_classifier.1.weight", "classifier.1.weight"),
("vqa_classifier.1.bias", "classifier.1.bias"),
("vqa_classifier.3.weight", "classifier.3.weight"),
("vqa_classifier.3.bias", "classifier.3.bias"),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
("nlvr2_classifier.0.weight", "classifier.0.weight"),
("nlvr2_classifier.0.bias", "classifier.0.bias"),
("nlvr2_classifier.1.weight", "classifier.1.weight"),
("nlvr2_classifier.1.bias", "classifier.1.bias"),
("nlvr2_classifier.3.weight", "classifier.3.weight"),
("nlvr2_classifier.3.bias", "classifier.3.bias"),
] )
else:
pass
return rename_keys
def lowerCAmelCase__ ( _a : Union[str, Any] , _a : List[str] ):
for i in range(config.num_hidden_layers ):
snake_case_ : List[Any] = "vilt."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ : int = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.weight''' )
snake_case_ : Optional[int] = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : str = in_proj_weight[
: config.hidden_size, :
]
snake_case_ : str = in_proj_bias[: config.hidden_size]
snake_case_ : Optional[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case_ : int = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case_ : List[Any] = in_proj_weight[
-config.hidden_size :, :
]
snake_case_ : str = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase__ ( _a : List[str] ):
snake_case_ : Union[str, Any] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_a , _a )
def lowerCAmelCase__ ( _a : Optional[Any] , _a : Union[str, Any] , _a : Optional[Any] ):
snake_case_ : str = dct.pop(_a )
snake_case_ : Tuple = val
@torch.no_grad()
def lowerCAmelCase__ ( _a : Tuple , _a : Dict ):
snake_case_ : Union[str, Any] = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=_a )
snake_case_ : Union[str, Any] = False
snake_case_ : Union[str, Any] = False
snake_case_ : Tuple = False
snake_case_ : Optional[Any] = False
if "vqa" in checkpoint_url:
snake_case_ : int = True
snake_case_ : Union[str, Any] = 31_29
snake_case_ : int = "huggingface/label-files"
snake_case_ : List[str] = "vqa2-id2label.json"
snake_case_ : List[Any] = json.load(open(hf_hub_download(_a , _a , repo_type="dataset" ) , "r" ) )
snake_case_ : int = {int(_a ): v for k, v in idalabel.items()}
snake_case_ : Any = idalabel
snake_case_ : Any = {v: k for k, v in idalabel.items()}
snake_case_ : List[str] = ViltForQuestionAnswering(_a )
elif "nlvr" in checkpoint_url:
snake_case_ : int = True
snake_case_ : Optional[int] = 2
snake_case_ : Optional[int] = {0: "False", 1: "True"}
snake_case_ : List[Any] = {v: k for k, v in config.idalabel.items()}
snake_case_ : Any = 3
snake_case_ : Any = ViltForImagesAndTextClassification(_a )
elif "irtr" in checkpoint_url:
snake_case_ : str = True
snake_case_ : Union[str, Any] = ViltForImageAndTextRetrieval(_a )
elif "mlm_itm" in checkpoint_url:
snake_case_ : str = True
snake_case_ : Dict = ViltForMaskedLM(_a )
else:
raise ValueError("Unknown model type" )
# load state_dict of original model, remove and rename some keys
snake_case_ : List[str] = torch.hub.load_state_dict_from_url(_a , map_location="cpu" )["state_dict"]
snake_case_ : Tuple = create_rename_keys(_a , _a , _a , _a )
for src, dest in rename_keys:
rename_key(_a , _a , _a )
read_in_q_k_v(_a , _a )
if mlm_model or irtr_model:
snake_case_ : str = ["itm_score.fc.weight", "itm_score.fc.bias"]
for k in ignore_keys:
state_dict.pop(_a , _a )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
snake_case_ , snake_case_ : Union[str, Any] = model.load_state_dict(_a , strict=_a )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(_a )
# Define processor
snake_case_ : List[Any] = ViltImageProcessor(size=3_84 )
snake_case_ : str = BertTokenizer.from_pretrained("bert-base-uncased" )
snake_case_ : List[str] = ViltProcessor(_a , _a )
# Forward pass on example inputs (image + text)
if nlvr_model:
snake_case_ : List[str] = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg" , stream=_a ).raw )
snake_case_ : Union[str, Any] = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg" , stream=_a ).raw )
snake_case_ : Optional[Any] = (
"The left image contains twice the number of dogs as the right image, and at least two dogs in total are"
" standing."
)
snake_case_ : Union[str, Any] = processor(_a , _a , return_tensors="pt" )
snake_case_ : str = processor(_a , _a , return_tensors="pt" )
snake_case_ : Union[str, Any] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
snake_case_ : Any = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg" , stream=_a ).raw )
if mlm_model:
snake_case_ : Optional[int] = "a bunch of [MASK] laying on a [MASK]."
else:
snake_case_ : List[str] = "How many cats are there?"
snake_case_ : int = processor(_a , _a , return_tensors="pt" )
snake_case_ : str = model(**_a )
# Verify outputs
if mlm_model:
snake_case_ : Tuple = torch.Size([1, 11, 3_05_22] )
snake_case_ : List[Any] = torch.tensor([-12.5_061, -12.5_123, -12.5_174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , _a , atol=1E-4 )
# verify masked token prediction equals "cats"
snake_case_ : int = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
snake_case_ : Dict = torch.Size([1, 31_29] )
snake_case_ : Tuple = torch.tensor([-15.9_495, -18.1_472, -10.3_041] )
assert torch.allclose(outputs.logits[0, :3] , _a , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , _a , atol=1E-4 )
# verify vqa prediction equals "2"
snake_case_ : str = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
snake_case_ : Optional[Any] = torch.Size([1, 2] )
snake_case_ : Any = torch.tensor([-2.8_721, 2.1_291] )
assert torch.allclose(outputs.logits[0, :3] , _a , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(_a ).mkdir(exist_ok=_a )
print(F'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(_a )
processor.save_pretrained(_a )
if __name__ == "__main__":
lowercase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
lowercase : int = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 568 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
lowercase : List[Any] = re.compile(r'''\s+''')
def lowerCAmelCase__ ( _a : int ):
return {"hash": hashlib.mda(re.sub(_a , "" , example["content"] ).encode("utf-8" ) ).hexdigest()}
def lowerCAmelCase__ ( _a : Optional[int] ):
snake_case_ : Optional[int] = [len(_a ) for line in example["content"].splitlines()]
return {"line_mean": np.mean(_a ), "line_max": max(_a )}
def lowerCAmelCase__ ( _a : Optional[int] ):
snake_case_ : str = np.mean([c.isalnum() for c in example["content"]] )
return {"alpha_frac": alpha_frac}
def lowerCAmelCase__ ( _a : str , _a : Tuple ):
if example["hash"] in uniques:
uniques.remove(example["hash"] )
return True
else:
return False
def lowerCAmelCase__ ( _a : Tuple , _a : Any=5 ):
snake_case_ : Union[str, Any] = ["auto-generated", "autogenerated", "automatically generated"]
snake_case_ : Union[str, Any] = example["content"].splitlines()
for _, line in zip(range(_a ) , _a ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def lowerCAmelCase__ ( _a : List[str] , _a : Dict=5 , _a : Union[str, Any]=0.05 ):
snake_case_ : Optional[Any] = ["unit tests", "test file", "configuration file"]
snake_case_ : Optional[Any] = example["content"].splitlines()
snake_case_ : List[str] = 0
snake_case_ : List[Any] = 0
# first test
for _, line in zip(range(_a ) , _a ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
snake_case_ : List[str] = example["content"].count("\n" )
snake_case_ : Any = int(coeff * nlines )
for line in lines:
count_config += line.lower().count("config" )
count_test += line.lower().count("test" )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def lowerCAmelCase__ ( _a : Optional[Any] ):
snake_case_ : Optional[int] = ["def ", "class ", "for ", "while "]
snake_case_ : Optional[Any] = example["content"].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def lowerCAmelCase__ ( _a : List[Any] , _a : Tuple=4 ):
snake_case_ : List[Any] = example["content"].splitlines()
snake_case_ : Tuple = 0
for line in lines:
counter += line.lower().count("=" )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def lowerCAmelCase__ ( _a : Optional[Any] ):
snake_case_ : Dict = tokenizer(example["content"] , truncation=_a )["input_ids"]
snake_case_ : Dict = len(example["content"] ) / len(_a )
return {"ratio": ratio}
def lowerCAmelCase__ ( _a : Dict ):
snake_case_ : Any = {}
results.update(get_hash(_a ) )
results.update(line_stats(_a ) )
results.update(alpha_stats(_a ) )
results.update(char_token_ratio(_a ) )
results.update(is_autogenerated(_a ) )
results.update(is_config_or_test(_a ) )
results.update(has_no_keywords(_a ) )
results.update(has_few_assignments(_a ) )
return results
def lowerCAmelCase__ ( _a : Union[str, Any] , _a : Dict , _a : Any ):
if not check_uniques(_a , _a ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def lowerCAmelCase__ ( _a : Optional[int] ):
with open(_a , "rb" ) as f_in:
with gzip.open(str(_a ) + ".gz" , "wb" , compresslevel=6 ) as f_out:
shutil.copyfileobj(_a , _a )
os.unlink(_a )
# Settings
lowercase : Union[str, Any] = HfArgumentParser(PreprocessingArguments)
lowercase : List[Any] = parser.parse_args()
if args.num_workers is None:
lowercase : str = multiprocessing.cpu_count()
lowercase : int = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
lowercase : Any = time.time()
lowercase : Dict = load_dataset(args.dataset_name, split='''train''')
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
lowercase : str = time.time()
lowercase : List[Any] = ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
lowercase : Optional[int] = set(ds.unique('''hash'''))
lowercase : List[str] = len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
lowercase : str = time.time()
lowercase : List[str] = ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(F"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
lowercase : Dict = time.time()
lowercase ,lowercase : Any = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(F"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
lowercase : Dict = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
json.dump(duplicate_clusters, f)
lowercase : str = output_dir / '''data'''
data_dir.mkdir(exist_ok=True)
lowercase : Optional[int] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
lowercase : Optional[int] = str(data_dir / F"""file-{file_number+1:012}.json""")
lowercase : int = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"""Time to save dataset: {time.time()-t_start:.2f}""")
| 568 | 1 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
UpperCAmelCase: Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(__a )
class UpperCamelCase ( __a ):
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
super().__init__(*lowerCAmelCase_ ,**lowerCAmelCase_ )
self.check_model_type(lowerCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,**UpperCAmelCase_ ):
_lowercase , _lowercase : Union[str, Any] = {}, {}
if padding is not None:
_lowercase : Dict = padding
if truncation is not None:
_lowercase : Union[str, Any] = truncation
if top_k is not None:
_lowercase : Union[str, Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ):
if isinstance(lowerCAmelCase_ ,(Image.Image, str) ) and isinstance(lowerCAmelCase_ ,lowerCAmelCase_ ):
_lowercase : int = {"""image""": image, """question""": question}
else:
_lowercase : Optional[Any] = image
_lowercase : str = super().__call__(lowerCAmelCase_ ,**lowerCAmelCase_ )
return results
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=False ,UpperCAmelCase_=False ):
_lowercase : str = load_image(inputs["""image"""] )
_lowercase : List[Any] = self.tokenizer(
inputs["""question"""] ,return_tensors=self.framework ,padding=lowerCAmelCase_ ,truncation=lowerCAmelCase_ )
_lowercase : str = self.image_processor(images=lowerCAmelCase_ ,return_tensors=self.framework )
model_inputs.update(lowerCAmelCase_ )
return model_inputs
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.model(**lowerCAmelCase_ )
return model_outputs
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=5 ):
if top_k > self.model.config.num_labels:
_lowercase : Dict = self.model.config.num_labels
if self.framework == "pt":
_lowercase : int = model_outputs.logits.sigmoid()[0]
_lowercase , _lowercase : List[str] = probs.topk(lowerCAmelCase_ )
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
_lowercase : Optional[Any] = scores.tolist()
_lowercase : str = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_ ,lowerCAmelCase_ )]
| 719 |
"""simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
_lowercase : Dict = inspect.getfile(accelerate.test_utils )
_lowercase : List[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_script.py"""] )
_lowercase : List[Any] = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = f"""
{self.test_dir}/xla_spawn.py
--num_cores 8
{self.test_file_path}
""".split()
_lowercase : Any = [sys.executable] + distributed_args
execute_subprocess_async(UpperCAmelCase_ ,env=os.environ.copy() )
| 600 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ):
'''simple docstring'''
__magic_name__ : List[Any] =feature_size
__magic_name__ : Union[str, Any] =sampling_rate
__magic_name__ : List[Any] =padding_value
__magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" )
__magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case )
super().__init__(**__snake_case )
def A__ ( self :Any , __snake_case :Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ):
'''simple docstring'''
if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__magic_name__ : Union[str, Any] ={
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f" to this method that includes {self.model_input_names[0]}, but you provided"
f" {list(processed_features.keys() )}" )
__magic_name__ : int =processed_features[self.model_input_names[0]]
__magic_name__ : Union[str, Any] =(
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(__snake_case ) == 0:
if return_attention_mask:
__magic_name__ : List[str] =[]
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__magic_name__ : Optional[int] =required_input[0]
if isinstance(__snake_case , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__magic_name__ : Optional[Any] =0
while len(required_input[index] ) == 0:
index += 1
if index < len(__snake_case ):
__magic_name__ : List[str] =required_input[index][0]
if return_tensors is None:
if is_tf_tensor(__snake_case ):
__magic_name__ : int ="""tf"""
elif is_torch_tensor(__snake_case ):
__magic_name__ : str ="""pt"""
elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ):
__magic_name__ : List[Any] ="""np"""
else:
raise ValueError(
f"type of {first_element} unknown: {type(__snake_case )}. "
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__magic_name__ : List[str] =to_numpy(__snake_case )
else:
__magic_name__ : str =[to_numpy(__snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
__magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case )
__magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]]
__magic_name__ : Dict =len(__snake_case )
if not all(len(__snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
__magic_name__ : Optional[int] =[]
for i in range(__snake_case ):
__magic_name__ : Any ={k: v[i] for k, v in processed_features.items()}
# truncation
__magic_name__ : List[str] =self._truncate(
__snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , )
truncated_inputs.append(__snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH
__magic_name__ : str ={}
for i in range(__snake_case ):
# padding
__magic_name__ : List[str] =self._pad(
truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , )
for key, value in outputs.items():
if key not in batch_outputs:
__magic_name__ : Dict =[]
if value.dtype is np.dtype(np.floataa ):
__magic_name__ : Optional[int] =value.astype(np.floataa )
batch_outputs[key].append(__snake_case )
return BatchFeature(__snake_case , tensor_type=__snake_case )
def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
__magic_name__ : Dict =processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__magic_name__ : Any =len(__snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa )
if needs_to_be_padded:
__magic_name__ : List[Any] =max_length - len(__snake_case )
if self.padding_side == "right":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (0, difference) )
__magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__magic_name__ : str =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__magic_name__ : str =np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
__magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__magic_name__ : List[Any] =np.pad(
__snake_case , __snake_case , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
__magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Any =len(__snake_case ) > max_length
if needs_to_be_truncated:
__magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length]
return processed_features
def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
__magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(__snake_case , __snake_case ):
__magic_name__ : Optional[int] =PaddingStrategy(__snake_case )
elif isinstance(__snake_case , __snake_case ):
__magic_name__ : Any =padding
else:
__magic_name__ : Any =PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 21 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowercase_ ( unittest.TestCase ):
def UpperCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def UpperCamelCase ( self ):
_snake_case ,_snake_case : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2" , revision="bf16" , dtype=jnp.bfloataa , )
_snake_case : List[Any] = "A painting of a squirrel eating a burger"
_snake_case : Union[str, Any] = jax.device_count()
_snake_case : List[Any] = num_samples * [prompt]
_snake_case : Tuple = sd_pipe.prepare_inputs(lowercase_ )
_snake_case : str = replicate(lowercase_ )
_snake_case : Dict = shard(lowercase_ )
_snake_case : List[Any] = jax.random.PRNGKey(0 )
_snake_case : List[Any] = jax.random.split(lowercase_ , jax.device_count() )
_snake_case : Tuple = sd_pipe(lowercase_ , lowercase_ , lowercase_ , num_inference_steps=25 , jit=lowercase_ )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
_snake_case : List[Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_snake_case : str = images[0, 253:256, 253:256, -1]
_snake_case : Tuple = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_snake_case : Optional[Any] = jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def UpperCamelCase ( self ):
_snake_case : Optional[Any] = "stabilityai/stable-diffusion-2"
_snake_case ,_snake_case : List[Any] = FlaxDPMSolverMultistepScheduler.from_pretrained(lowercase_ , subfolder="scheduler" )
_snake_case ,_snake_case : int = FlaxStableDiffusionPipeline.from_pretrained(
lowercase_ , scheduler=lowercase_ , revision="bf16" , dtype=jnp.bfloataa , )
_snake_case : str = scheduler_params
_snake_case : Dict = "A painting of a squirrel eating a burger"
_snake_case : Dict = jax.device_count()
_snake_case : Optional[int] = num_samples * [prompt]
_snake_case : List[str] = sd_pipe.prepare_inputs(lowercase_ )
_snake_case : Optional[int] = replicate(lowercase_ )
_snake_case : Union[str, Any] = shard(lowercase_ )
_snake_case : List[Any] = jax.random.PRNGKey(0 )
_snake_case : Union[str, Any] = jax.random.split(lowercase_ , jax.device_count() )
_snake_case : str = sd_pipe(lowercase_ , lowercase_ , lowercase_ , num_inference_steps=25 , jit=lowercase_ )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
_snake_case : List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_snake_case : List[str] = images[0, 253:256, 253:256, -1]
_snake_case : Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_snake_case : Dict = jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2 | 670 | 0 |
"""simple docstring"""
import random
def lowerCamelCase__ ( _lowerCamelCase : Dict , _lowerCamelCase : Tuple , _lowerCamelCase : Tuple ) -> Tuple:
lowerCamelCase_ = a[left_index]
lowerCamelCase_ = left_index + 1
for j in range(left_index + 1 , _lowerCamelCase ):
if a[j] < pivot:
lowerCamelCase_ , lowerCamelCase_ = a[i], a[j]
i += 1
lowerCamelCase_ , lowerCamelCase_ = a[i - 1], a[left_index]
return i - 1
def lowerCamelCase__ ( _lowerCamelCase : List[Any] , _lowerCamelCase : str , _lowerCamelCase : Tuple ) -> List[Any]:
if left < right:
lowerCamelCase_ = random.randint(_lowerCamelCase , right - 1 )
lowerCamelCase_ , lowerCamelCase_ = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
lowerCamelCase_ = partition(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
quick_sort_random(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) # recursive quicksort to the left of the pivot point
quick_sort_random(
_lowerCamelCase , pivot_index + 1 , _lowerCamelCase ) # recursive quicksort to the right of the pivot point
def lowerCamelCase__ ( ) -> Dict:
lowerCamelCase_ = input('Enter numbers separated by a comma:\n' ).strip()
lowerCamelCase_ = [int(_lowerCamelCase ) for item in user_input.split(',' )]
quick_sort_random(_lowerCamelCase , 0 , len(_lowerCamelCase ) )
print(_lowerCamelCase )
if __name__ == "__main__":
main()
| 705 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : str ) -> bool:
lowerCamelCase_ = 0
for ch in input_str:
lowerCamelCase_ = ord(_lowerCamelCase )
lowerCamelCase_ = pow(2 , _lowerCamelCase )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 137 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class _UpperCAmelCase ( _lowerCamelCase ):
a = '''ctrl'''
a = ['''past_key_values''']
a = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , a__=246534 , a__=256 , a__=1280 , a__=8192 , a__=48 , a__=16 , a__=0.1 , a__=0.1 , a__=1E-6 , a__=0.02 , a__=True , **a__ , ):
A_ : List[Any] = vocab_size
A_ : Tuple = n_positions
A_ : int = n_embd
A_ : Optional[int] = n_layer
A_ : Any = n_head
A_ : List[str] = dff
A_ : Dict = resid_pdrop
A_ : int = embd_pdrop
A_ : Union[str, Any] = layer_norm_epsilon
A_ : Optional[int] = initializer_range
A_ : Tuple = use_cache
super().__init__(**a__ )
| 569 |
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
A_ : List[Any] = sum(_lowerCAmelCase ) / len(_lowerCAmelCase ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(_lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 569 | 1 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def __UpperCamelCase ( a : Optional[Any] ) ->str:
snake_case = torch.exp(a )
snake_case = torch.sum(a , dim=1 ) # sum of exp(x_i)
snake_case = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(a ) - B / A
class _lowercase ( nn.Module ):
def __init__( self , A__ ) -> Any:
super().__init__()
snake_case = config.output_attentions
snake_case = config.output_hidden_states
snake_case = nn.ModuleList([BertLayer(A__ ) for _ in range(config.num_hidden_layers )] )
snake_case = nn.ModuleList([BertHighway(A__ ) for _ in range(config.num_hidden_layers )] )
snake_case = [-1 for _ in range(config.num_hidden_layers )]
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
if (type(A__ ) is float) or (type(A__ ) is int):
for i in range(len(self.early_exit_entropy ) ):
snake_case = x
else:
snake_case = x
def UpperCamelCase ( self , A__ ) -> Optional[Any]:
snake_case = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def UpperCamelCase ( self , A__ , A__=None , A__=None , A__=None , A__=None , ) -> int:
snake_case = ()
snake_case = ()
snake_case = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
snake_case = all_hidden_states + (hidden_states,)
snake_case = layer_module(
A__ , A__ , head_mask[i] , A__ , A__ )
snake_case = layer_outputs[0]
if self.output_attentions:
snake_case = all_attentions + (layer_outputs[1],)
snake_case = (hidden_states,)
if self.output_hidden_states:
snake_case = current_outputs + (all_hidden_states,)
if self.output_attentions:
snake_case = current_outputs + (all_attentions,)
snake_case = self.highway[i](A__ )
# logits, pooled_output
if not self.training:
snake_case = highway_exit[0]
snake_case = entropy(A__ )
snake_case = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
snake_case = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
snake_case = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(A__ , i + 1 )
else:
snake_case = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
snake_case = all_hidden_states + (hidden_states,)
snake_case = (hidden_states,)
if self.output_hidden_states:
snake_case = outputs + (all_hidden_states,)
if self.output_attentions:
snake_case = outputs + (all_attentions,)
snake_case = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
'''The Bert Model transformer with early exiting (DeeBERT). ''' , __a , )
class _lowercase ( __a ):
def __init__( self , A__ ) -> str:
super().__init__(A__ )
snake_case = config
snake_case = BertEmbeddings(A__ )
snake_case = DeeBertEncoder(A__ )
snake_case = BertPooler(A__ )
self.init_weights()
def UpperCamelCase ( self ) -> Dict:
self.encoder.init_highway_pooler(self.pooler )
def UpperCamelCase ( self ) -> str:
return self.embeddings.word_embeddings
def UpperCamelCase ( self , A__ ) -> Any:
snake_case = value
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(A__ )
@add_start_docstrings_to_model_forward(A__ )
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__=None , A__=None , A__=None , A__=None , A__=None , ) -> str:
if input_ids is not None and inputs_embeds is not None:
raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
elif input_ids is not None:
snake_case = input_ids.size()
elif inputs_embeds is not None:
snake_case = inputs_embeds.size()[:-1]
else:
raise ValueError('''You have to specify either input_ids or inputs_embeds''' )
snake_case = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
snake_case = torch.ones(A__ , device=A__ )
if encoder_attention_mask is None:
snake_case = torch.ones(A__ , device=A__ )
if token_type_ids is None:
snake_case = torch.zeros(A__ , dtype=torch.long , device=A__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
snake_case = self.get_extended_attention_mask(A__ , A__ , A__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
snake_case = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
snake_case = encoder_attention_mask[:, None, None, :]
snake_case = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
snake_case = (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
snake_case = self.get_head_mask(A__ , self.config.num_hidden_layers )
snake_case = self.embeddings(
input_ids=A__ , position_ids=A__ , token_type_ids=A__ , inputs_embeds=A__ )
snake_case = self.encoder(
A__ , attention_mask=A__ , head_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , )
snake_case = encoder_outputs[0]
snake_case = self.pooler(A__ )
snake_case = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class _lowercase ( __a ):
def __init__( self , A__ , A__ ) -> Any:
snake_case = message
snake_case = exit_layer # start from 1!
class _lowercase ( nn.Module ):
def __init__( self , A__ ) -> str:
super().__init__()
snake_case = BertPooler(A__ )
snake_case = nn.Dropout(config.hidden_dropout_prob )
snake_case = nn.Linear(config.hidden_size , config.num_labels )
def UpperCamelCase ( self , A__ ) -> Optional[Any]:
# Pooler
snake_case = encoder_outputs[0]
snake_case = self.pooler(A__ )
# "return" pooler_output
# BertModel
snake_case = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
snake_case = bmodel_output[1]
snake_case = self.dropout(A__ )
snake_case = self.classifier(A__ )
return logits, pooled_output
@add_start_docstrings(
'''Bert Model (with early exiting - DeeBERT) with a classifier on top,
also takes care of multi-layer training. ''' , __a , )
class _lowercase ( __a ):
def __init__( self , A__ ) -> Union[str, Any]:
super().__init__(A__ )
snake_case = config.num_labels
snake_case = config.num_hidden_layers
snake_case = DeeBertModel(A__ )
snake_case = nn.Dropout(config.hidden_dropout_prob )
snake_case = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(A__ )
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__=None , A__=None , A__=None , A__=None , A__=-1 , A__=False , ) -> Tuple:
snake_case = self.num_layers
try:
snake_case = self.bert(
A__ , attention_mask=A__ , token_type_ids=A__ , position_ids=A__ , head_mask=A__ , inputs_embeds=A__ , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
snake_case = outputs[1]
snake_case = self.dropout(A__ )
snake_case = self.classifier(A__ )
snake_case = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
snake_case = e.message
snake_case = e.exit_layer
snake_case = outputs[0]
if not self.training:
snake_case = entropy(A__ )
snake_case = []
snake_case = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
snake_case = MSELoss()
snake_case = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
snake_case = CrossEntropyLoss()
snake_case = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
snake_case = []
for highway_exit in outputs[-1]:
snake_case = highway_exit[0]
if not self.training:
highway_logits_all.append(A__ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
snake_case = MSELoss()
snake_case = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
snake_case = CrossEntropyLoss()
snake_case = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(A__ )
if train_highway:
snake_case = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
snake_case = (loss,) + outputs
if not self.training:
snake_case = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
snake_case = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 44 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case = [tuple(A__ ) if isinstance(A__ , A__ ) else key for key in keys]
snake_case = Counter(A__ )
snake_case = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def UpperCamelCase ( self , A__ , A__=False ) -> List[Any]:
snake_case = super().construct_mapping(A__ , deep=A__ )
self._check_no_duplicates_on_constructed_node(A__ )
return mapping
def __UpperCamelCase ( a : str ) ->Tuple[Optional[str], str]:
snake_case = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case = full_content[1:].index('''---''' ) + 1
snake_case = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(a )
class _lowercase ( __a ):
# class attributes
_UpperCAmelCase = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case , snake_case = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(A__ )
else:
return cls()
def UpperCamelCase ( self , A__ ) -> str:
if path.exists():
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case = readme_file.read()
else:
snake_case = None
snake_case = self._to_readme(A__ )
with open(A__ , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(A__ )
def UpperCamelCase ( self , A__ = None ) -> str:
if readme_content is not None:
snake_case , snake_case = _split_yaml_from_readme(A__ )
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
snake_case = yaml.load(A__ , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**A__ )
def UpperCamelCase ( self ) -> str:
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=A__ , allow_unicode=A__ , encoding='''utf-8''' , ).decode('''utf-8''' )
_lowercase = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
_lowercase = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
_lowercase = ap.parse_args()
_lowercase = Path(args.readme_filepath)
_lowercase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 44 | 1 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: str ,__UpperCamelCase: str ):
"""simple docstring"""
assert x is not None
assert y is not None
SCREAMING_SNAKE_CASE : Dict = len(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Any = len(__UpperCamelCase )
# declaring the array for storing the dp values
SCREAMING_SNAKE_CASE : Optional[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 ):
SCREAMING_SNAKE_CASE : Dict = 1 if x[i - 1] == y[j - 1] else 0
SCREAMING_SNAKE_CASE : List[Any] = max(l[i - 1][j] ,l[i][j - 1] ,l[i - 1][j - 1] + match )
SCREAMING_SNAKE_CASE : Dict = ''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = m, n
while i > 0 and j > 0:
SCREAMING_SNAKE_CASE : Optional[Any] = 1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
SCREAMING_SNAKE_CASE : List[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__":
UpperCamelCase_ = "AGGTAB"
UpperCamelCase_ = "GXTXAYB"
UpperCamelCase_ = 4
UpperCamelCase_ = "GTAB"
UpperCamelCase_ , UpperCamelCase_ = longest_common_subsequence(a, b)
print("len =", ln, ", sub-sequence =", subseq)
import doctest
doctest.testmod()
| 28 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowercase__( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = 9, 14 # noqa: F841
SCREAMING_SNAKE_CASE : Optional[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
SCREAMING_SNAKE_CASE : Optional[int] = defaultdict(__UpperCamelCase )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
SCREAMING_SNAKE_CASE : Dict = mst(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
SCREAMING_SNAKE_CASE : Any = tuple(answer[:2] )
SCREAMING_SNAKE_CASE : List[Any] = tuple(edge[::-1] )
assert edge in result or reverse in result
| 28 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
a : Optional[int] = 5_0000
a : Optional[int] = 5000
a , a : Tuple = os.path.split(__file__)
a : Tuple = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def snake_case__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[Any]:
for i in range(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ = dataset[i]
@get_duration
def snake_case__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
for i in range(0 , len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ = dataset[i : i + batch_size]
@get_duration
def snake_case__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
with dataset.formatted_as(type=_SCREAMING_SNAKE_CASE ):
for i in range(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ = dataset[i]
@get_duration
def snake_case__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
with dataset.formatted_as(type=_SCREAMING_SNAKE_CASE ):
for i in range(0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase__ = dataset[i : i + batch_size]
def snake_case__ ( ) ->str:
UpperCAmelCase__ = {"""num examples""": SPEED_TEST_N_EXAMPLES}
UpperCAmelCase__ = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0_0}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0_0_0}),
]
UpperCAmelCase__ = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_0_0_0}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_0_0_0}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("""generating dataset""" )
UpperCAmelCase__ = datasets.Features(
{"""list""": datasets.Sequence(datasets.Value("""float32""" ) ), """numbers""": datasets.Value("""float32""" )} )
UpperCAmelCase__ = generate_example_dataset(
os.path.join(_SCREAMING_SNAKE_CASE , """dataset.arrow""" ) , _SCREAMING_SNAKE_CASE , num_examples=_SCREAMING_SNAKE_CASE , seq_shapes={"""list""": (1_0_0,)} , )
print("""first set of iterations""" )
for func, kwargs in functions:
print(func.__name__ , str(_SCREAMING_SNAKE_CASE ) )
UpperCAmelCase__ = func(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
print("""shuffling dataset""" )
UpperCAmelCase__ = dataset.shuffle()
print("""Second set of iterations (after shuffling""" )
for func, kwargs in functions_shuffled:
print("""shuffled """ , func.__name__ , str(_SCREAMING_SNAKE_CASE ) )
UpperCAmelCase__ = func(
_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , """wb""" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 422 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _UpperCamelCase ( __UpperCamelCase ):
'''simple docstring'''
def A__ ( self , __lowercase ):
with open(__lowercase , encoding="""utf-8""" ) as input_file:
UpperCAmelCase__ = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
UpperCAmelCase__ = input_file.read()
UpperCAmelCase__ = regexp.search(__lowercase )
return match
def A__ ( self , __lowercase ):
with open(__lowercase , encoding="""utf-8""" ) as input_file:
UpperCAmelCase__ = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
UpperCAmelCase__ = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
UpperCAmelCase__ = regexp.finditer(__lowercase )
UpperCAmelCase__ = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A__ ( self ):
UpperCAmelCase__ = Path("""./datasets""" )
UpperCAmelCase__ = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowercase ) ):
raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}''' )
def A__ ( self ):
UpperCAmelCase__ = Path("""./datasets""" )
UpperCAmelCase__ = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowercase ) ):
raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 422 | 1 |
'''simple docstring'''
import math
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
__snake_case = [True] * n
__snake_case = False
__snake_case = False
__snake_case = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
__snake_case = i * 2
while index < n:
__snake_case = False
__snake_case = index + i
__snake_case = [2]
for i in range(3 , _UpperCAmelCase , 2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def __UpperCAmelCase ( _UpperCAmelCase : int = 99_99_66_66_33_33 ) -> int:
__snake_case = math.floor(math.sqrt(_UpperCAmelCase ) ) + 1_00
__snake_case = prime_sieve(_UpperCAmelCase )
__snake_case = 0
__snake_case = 0
__snake_case = primes[prime_index]
while (last_prime**2) <= limit:
__snake_case = primes[prime_index + 1]
__snake_case = last_prime**2
__snake_case = next_prime**2
# Get numbers divisible by lps(current)
__snake_case = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__snake_case = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__snake_case = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__snake_case = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 69 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
lowerCamelCase = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCamelCase = get_tests_dir('fixtures/vocab.json')
lowerCamelCase = get_tests_dir('fixtures')
class A ( unittest.TestCase ):
UpperCamelCase__ : Dict =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
def lowerCamelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase : Dict =0
def lowerCamelCase ( self : str ) -> List[str]:
"""simple docstring"""
_lowerCamelCase : List[Any] =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : int =WavaVecaConfig()
_lowerCamelCase : Dict =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
# save in new folder
model_config.save_pretrained(lowercase_ )
processor.save_pretrained(lowercase_ )
_lowerCamelCase : Any =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(lowercase_ , os.path.join(lowercase_ , lowercase_ ) )
copyfile(lowercase_ , os.path.join(lowercase_ , 'vocab.json' ) )
_lowerCamelCase : Union[str, Any] =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : List[Any] =WavaVecaFeatureExtractor()
_lowerCamelCase : List[str] =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )
_lowerCamelCase : str =WavaVecaProcessor(lowercase_ , lowercase_ )
# save in new folder
processor.save_pretrained(lowercase_ )
# drop `processor_class` in tokenizer
with open(os.path.join(lowercase_ , lowercase_ ) , 'r' ) as f:
_lowerCamelCase : Optional[int] =json.load(lowercase_ )
config_dict.pop('processor_class' )
with open(os.path.join(lowercase_ , lowercase_ ) , 'w' ) as f:
f.write(json.dumps(lowercase_ ) )
_lowerCamelCase : Optional[int] =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : Optional[Any] =WavaVecaFeatureExtractor()
_lowerCamelCase : Tuple =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )
_lowerCamelCase : Dict =WavaVecaProcessor(lowercase_ , lowercase_ )
# save in new folder
processor.save_pretrained(lowercase_ )
# drop `processor_class` in feature extractor
with open(os.path.join(lowercase_ , lowercase_ ) , 'r' ) as f:
_lowerCamelCase : Union[str, Any] =json.load(lowercase_ )
config_dict.pop('processor_class' )
with open(os.path.join(lowercase_ , lowercase_ ) , 'w' ) as f:
f.write(json.dumps(lowercase_ ) )
_lowerCamelCase : Optional[int] =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : Optional[Any] =WavaVecaConfig(processor_class='Wav2Vec2Processor' )
model_config.save_pretrained(lowercase_ )
# copy relevant files
copyfile(lowercase_ , os.path.join(lowercase_ , 'vocab.json' ) )
# create emtpy sample processor
with open(os.path.join(lowercase_ , lowercase_ ) , 'w' ) as f:
f.write('{}' )
_lowerCamelCase : int =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
with self.assertRaises(lowercase_ ):
_lowerCamelCase : int =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowercase_ ):
_lowerCamelCase : Union[str, Any] =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ )
_lowerCamelCase : List[str] =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
_lowerCamelCase : int =processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
_lowerCamelCase : Optional[int] =processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
# Test we can also load the slow version
_lowerCamelCase : int =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ , use_fast=lowercase_ )
_lowerCamelCase : Optional[int] =new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , 'NewTokenizer' )
else:
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
def lowerCamelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
try:
AutoConfig.register('custom' , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
AutoTokenizer.register(lowercase_ , slow_tokenizer_class=lowercase_ )
AutoProcessor.register(lowercase_ , lowercase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase_ ):
AutoProcessor.register(lowercase_ , lowercase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : str =CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCamelCase : str =os.path.join(lowercase_ , 'vocab.txt' )
with open(lowercase_ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
_lowerCamelCase : List[Any] =CustomTokenizer(lowercase_ )
_lowerCamelCase : Optional[int] =CustomProcessor(lowercase_ , lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(lowercase_ )
_lowerCamelCase : List[Any] =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
class A ( UpperCamelCase_ ):
UpperCamelCase__ : Optional[Any] =False
class A ( UpperCamelCase_ ):
UpperCamelCase__ : int =False
class A ( UpperCamelCase_ ):
UpperCamelCase__ : Union[str, Any] ='AutoFeatureExtractor'
UpperCamelCase__ : str ='AutoTokenizer'
UpperCamelCase__ : List[Any] =False
try:
AutoConfig.register('custom' , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
AutoTokenizer.register(lowercase_ , slow_tokenizer_class=lowercase_ )
AutoProcessor.register(lowercase_ , lowercase_ )
# If remote code is not set, the default is to use local classes.
_lowerCamelCase : int =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
_lowerCamelCase : int =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
_lowerCamelCase : str =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_lowerCamelCase : List[Any] =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(processor.__class__.__name__ , 'BertTokenizerFast' )
def lowerCamelCase ( self : Any ) -> Dict:
"""simple docstring"""
_lowerCamelCase : Any =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext' )
self.assertEqual(processor.__class__.__name__ , 'ConvNextImageProcessor' )
@is_staging_test
class A ( unittest.TestCase ):
UpperCamelCase__ : List[Any] =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def lowerCamelCase ( cls : int ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] =TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def lowerCamelCase ( cls : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='test-processor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-processor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-processor' )
except HTTPError:
pass
def lowerCamelCase ( self : str ) -> int:
"""simple docstring"""
_lowerCamelCase : Tuple =WavaVecaProcessor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowercase_ , 'test-processor' ) , push_to_hub=lowercase_ , use_auth_token=self._token )
_lowerCamelCase : Union[str, Any] =WavaVecaProcessor.from_pretrained(F'''{USER}/test-processor''' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(new_processor.feature_extractor , lowercase_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def lowerCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
_lowerCamelCase : int =WavaVecaProcessor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowercase_ , 'test-processor-org' ) , push_to_hub=lowercase_ , use_auth_token=self._token , organization='valid_org' , )
_lowerCamelCase : str =WavaVecaProcessor.from_pretrained('valid_org/test-processor-org' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(new_processor.feature_extractor , lowercase_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def lowerCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
_lowerCamelCase : Optional[Any] =CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCamelCase : Dict =os.path.join(lowercase_ , 'vocab.txt' )
with open(lowercase_ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
_lowerCamelCase : Any =CustomTokenizer(lowercase_ )
_lowerCamelCase : List[Any] =CustomProcessor(lowercase_ , lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(F'''{USER}/test-dynamic-processor''' , token=self._token )
_lowerCamelCase : List[str] =Repository(lowercase_ , clone_from=F'''{USER}/test-dynamic-processor''' , token=self._token )
processor.save_pretrained(lowercase_ )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
'AutoProcessor': 'custom_processing.CustomProcessor',
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(lowercase_ , 'tokenizer_config.json' ) ) as f:
_lowerCamelCase : Union[str, Any] =json.load(lowercase_ )
self.assertDictEqual(
tokenizer_config['auto_map'] , {
'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
'AutoProcessor': 'custom_processing.CustomProcessor',
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , 'custom_feature_extraction.py' ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , 'custom_tokenization.py' ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , 'custom_processing.py' ) ) )
repo.push_to_hub()
_lowerCamelCase : Tuple =AutoProcessor.from_pretrained(F'''{USER}/test-dynamic-processor''' , trust_remote_code=lowercase_ )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , 'CustomProcessor' )
| 464 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A : Any = logging.get_logger(__name__)
class _lowercase ( lowercase__ , lowercase__):
"""simple docstring"""
A__ = "maskformer-swin"
A__ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Any , __lowerCamelCase : Dict=224 , __lowerCamelCase : List[str]=4 , __lowerCamelCase : int=3 , __lowerCamelCase : int=96 , __lowerCamelCase : Tuple=[2, 2, 6, 2] , __lowerCamelCase : Tuple=[3, 6, 12, 24] , __lowerCamelCase : Union[str, Any]=7 , __lowerCamelCase : Union[str, Any]=4.0 , __lowerCamelCase : Any=True , __lowerCamelCase : Tuple=0.0 , __lowerCamelCase : List[str]=0.0 , __lowerCamelCase : List[str]=0.1 , __lowerCamelCase : List[Any]="gelu" , __lowerCamelCase : Optional[int]=False , __lowerCamelCase : Tuple=0.0_2 , __lowerCamelCase : List[Any]=1E-5 , __lowerCamelCase : Dict=None , __lowerCamelCase : Dict=None , **__lowerCamelCase : Tuple , ):
'''simple docstring'''
super().__init__(**__lowerCamelCase )
lowerCamelCase__ : int = image_size
lowerCamelCase__ : Dict = patch_size
lowerCamelCase__ : Optional[int] = num_channels
lowerCamelCase__ : Optional[Any] = embed_dim
lowerCamelCase__ : str = depths
lowerCamelCase__ : str = len(__lowerCamelCase )
lowerCamelCase__ : Dict = num_heads
lowerCamelCase__ : Optional[Any] = window_size
lowerCamelCase__ : str = mlp_ratio
lowerCamelCase__ : Tuple = qkv_bias
lowerCamelCase__ : Optional[Any] = hidden_dropout_prob
lowerCamelCase__ : List[str] = attention_probs_dropout_prob
lowerCamelCase__ : Union[str, Any] = drop_path_rate
lowerCamelCase__ : int = hidden_act
lowerCamelCase__ : Optional[Any] = use_absolute_embeddings
lowerCamelCase__ : Dict = layer_norm_eps
lowerCamelCase__ : int = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase__ : List[str] = int(embed_dim * 2 ** (len(__lowerCamelCase ) - 1) )
lowerCamelCase__ : Optional[Any] = ["stem"] + [f"stage{idx}" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
lowerCamelCase__ : int = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names )
| 717 |
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 _lowercase :
"""simple docstring"""
def __init__( self : Dict , __lowerCamelCase : str , __lowerCamelCase : Optional[int]=13 , __lowerCamelCase : List[str]=7 , __lowerCamelCase : Tuple=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : List[str]=True , __lowerCamelCase : Union[str, Any]=99 , __lowerCamelCase : List[Any]=32 , __lowerCamelCase : List[Any]=5 , __lowerCamelCase : Optional[Any]=4 , __lowerCamelCase : Optional[int]=37 , __lowerCamelCase : List[str]="gelu" , __lowerCamelCase : List[str]=0.1 , __lowerCamelCase : int=0.1 , __lowerCamelCase : List[str]=512 , __lowerCamelCase : Optional[Any]=16 , __lowerCamelCase : Optional[Any]=2 , __lowerCamelCase : str=0.0_2 , __lowerCamelCase : List[str]=3 , __lowerCamelCase : Tuple=4 , __lowerCamelCase : Optional[int]=None , ):
'''simple docstring'''
lowerCamelCase__ : Tuple = parent
lowerCamelCase__ : int = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : Union[str, Any] = is_training
lowerCamelCase__ : Any = use_token_type_ids
lowerCamelCase__ : Union[str, Any] = use_labels
lowerCamelCase__ : List[str] = vocab_size
lowerCamelCase__ : Union[str, Any] = hidden_size
lowerCamelCase__ : List[Any] = num_hidden_layers
lowerCamelCase__ : Optional[Any] = num_attention_heads
lowerCamelCase__ : Any = intermediate_size
lowerCamelCase__ : str = hidden_act
lowerCamelCase__ : str = hidden_dropout_prob
lowerCamelCase__ : Any = attention_probs_dropout_prob
lowerCamelCase__ : List[str] = max_position_embeddings
lowerCamelCase__ : Optional[int] = type_vocab_size
lowerCamelCase__ : List[Any] = type_sequence_label_size
lowerCamelCase__ : List[str] = initializer_range
lowerCamelCase__ : List[str] = num_labels
lowerCamelCase__ : List[Any] = num_choices
lowerCamelCase__ : Optional[Any] = scope
lowerCamelCase__ : List[Any] = self.vocab_size - 1
def lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase__ : Optional[Any] = None
if self.use_token_type_ids:
lowerCamelCase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCamelCase__ : Any = None
lowerCamelCase__ : str = None
lowerCamelCase__ : str = None
if self.use_labels:
lowerCamelCase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase__ : Dict = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase__ : Union[str, 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 , )
lowerCamelCase__ : 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 : str , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : int , *__lowerCamelCase : List[Any] ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = OpenAIGPTModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
lowerCamelCase__ : Tuple = model(__lowerCamelCase , token_type_ids=__lowerCamelCase , head_mask=__lowerCamelCase )
lowerCamelCase__ : str = model(__lowerCamelCase , token_type_ids=__lowerCamelCase )
lowerCamelCase__ : Optional[int] = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase ( self : str , __lowerCamelCase : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[str] , __lowerCamelCase : Any , *__lowerCamelCase : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ : Tuple = OpenAIGPTLMHeadModel(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
lowerCamelCase__ : List[str] = model(__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase ( self : Dict , __lowerCamelCase : Any , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[int] , *__lowerCamelCase : Tuple ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = OpenAIGPTDoubleHeadsModel(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
lowerCamelCase__ : Optional[Any] = model(__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase ( self : Tuple , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[Any] , *__lowerCamelCase : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.num_labels
lowerCamelCase__ : Tuple = OpenAIGPTForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
lowerCamelCase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase__ : List[str] = model(__lowerCamelCase , token_type_ids=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
lowerCamelCase__ : str = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : Any = config_and_inputs
lowerCamelCase__ : Union[str, Any] = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class _lowercase ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase):
"""simple docstring"""
A__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
A__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
A__ = (
{
"feature-extraction": OpenAIGPTModel,
"text-classification": OpenAIGPTForSequenceClassification,
"text-generation": OpenAIGPTLMHeadModel,
"zero-shot": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowerCAmelCase ( self : List[str] , __lowerCamelCase : str , __lowerCamelCase : Tuple , __lowerCamelCase : Any , __lowerCamelCase : List[Any] , __lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
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 : Union[str, Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple , __lowerCamelCase : Tuple=False ):
'''simple docstring'''
lowerCamelCase__ : Tuple = super()._prepare_for_class(__lowerCamelCase , __lowerCamelCase , return_labels=__lowerCamelCase )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowerCamelCase__ : Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCamelCase , )
lowerCamelCase__ : Tuple = inputs_dict["labels"]
lowerCamelCase__ : Any = inputs_dict["labels"]
lowerCamelCase__ : Any = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=__lowerCamelCase , )
lowerCamelCase__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCamelCase )
return inputs_dict
def lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
lowerCamelCase__ : Tuple = OpenAIGPTModelTester(self )
lowerCamelCase__ : Union[str, Any] = ConfigTester(self , config_class=__lowerCamelCase , n_embd=37 )
def lowerCAmelCase ( self : int ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*__lowerCamelCase )
def lowerCAmelCase ( self : str ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__lowerCamelCase )
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*__lowerCamelCase )
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCamelCase )
@slow
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ : Any = OpenAIGPTModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
@require_torch
class _lowercase ( unittest.TestCase):
"""simple docstring"""
@slow
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
model.to(__lowerCamelCase )
lowerCamelCase__ : int = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=__lowerCamelCase ) # the president is
lowerCamelCase__ : Union[str, Any] = [
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
lowerCamelCase__ : int = model.generate(__lowerCamelCase , do_sample=__lowerCamelCase )
self.assertListEqual(output_ids[0].tolist() , __lowerCamelCase )
| 5 | 0 |
import argparse
import json
from tqdm import tqdm
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
__UpperCAmelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=__lowerCAmelCase , default='''biencoder-nq-dev.json''' , help='''Path to raw DPR training data''' , )
parser.add_argument(
'''--evaluation_set''' , type=__lowerCAmelCase , help='''where to store parsed evaluation_set file''' , )
parser.add_argument(
'''--gold_data_path''' , type=__lowerCAmelCase , help='''where to store parsed gold_data_path file''' , )
__UpperCAmelCase =parser.parse_args()
with open(args.src_path , '''r''' ) as src_file, open(args.evaluation_set , '''w''' ) as eval_file, open(
args.gold_data_path , '''w''' ) as gold_file:
__UpperCAmelCase =json.load(__lowerCAmelCase )
for dpr_record in tqdm(__lowerCAmelCase ):
__UpperCAmelCase =dpr_record["""question"""]
__UpperCAmelCase =[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
eval_file.write(question + '''\n''' )
gold_file.write('''\t'''.join(__lowerCAmelCase ) + '''\n''' )
if __name__ == "__main__":
main()
| 132 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase = logging.get_logger(__name__)
def __lowerCamelCase ( __lowerCAmelCase : Tuple ) -> List[str]:
__UpperCamelCase : List[str] = R"""\w+[.]\d+"""
__UpperCamelCase : Optional[int] = re.findall(__lowerCAmelCase , __lowerCAmelCase )
for pat in pats:
__UpperCamelCase : str = key.replace(__lowerCAmelCase , """_""".join(pat.split(""".""" ) ) )
return key
def __lowerCamelCase ( __lowerCAmelCase : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Union[str, Any] ) -> str:
__UpperCamelCase : Dict = pt_tuple_key[:-1] + ("""scale""",)
if (
any("""norm""" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
__UpperCamelCase : List[Any] = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
__UpperCamelCase : Dict = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
__UpperCamelCase : Any = pt_tuple_key[:-1] + ("""embedding""",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
__UpperCamelCase : List[Any] = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
__UpperCamelCase : Dict = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
__UpperCamelCase : Union[str, Any] = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight":
__UpperCamelCase : int = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
__UpperCamelCase : List[Any] = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
__UpperCamelCase : Tuple = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __lowerCamelCase ( __lowerCAmelCase : Dict , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int]=42 ) -> int:
# Step 1: Convert pytorch tensor to numpy
__UpperCamelCase : Dict = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
__UpperCamelCase : str = flax_model.init_weights(PRNGKey(__lowerCAmelCase ) )
__UpperCamelCase : Union[str, Any] = flatten_dict(__lowerCAmelCase )
__UpperCamelCase : List[str] = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
__UpperCamelCase : str = rename_key(__lowerCAmelCase )
__UpperCamelCase : Any = tuple(renamed_pt_key.split(""".""" ) )
# Correctly rename weight parameters
__UpperCamelCase , __UpperCamelCase : Tuple = rename_key_and_reshape_tensor(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# also add unexpected weight so that warning is thrown
__UpperCamelCase : Any = jnp.asarray(__lowerCAmelCase )
return unflatten_dict(__lowerCAmelCase )
| 269 | 0 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __lowerCAmelCase:
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :int = data
SCREAMING_SNAKE_CASE_ :Union[str, Any] = [0x67_45_23_01, 0xEF_CD_AB_89, 0x98_BA_DC_FE, 0x10_32_54_76, 0xC3_D2_E1_F0]
@staticmethod
def _lowercase ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
return ((n << b) | (n >> (32 - b))) & 0xFF_FF_FF_FF
def _lowercase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Optional[int] = b'\x80' + b'\x00' * (63 - (len(self.data ) + 8) % 64)
SCREAMING_SNAKE_CASE_ :List[str] = self.data + padding + struct.pack('>Q' , 8 * len(self.data ) )
return padded_data
def _lowercase ( self : Optional[int] ):
"""simple docstring"""
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def _lowercase ( self : Any , SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :List[str] = list(struct.unpack('>16L' , SCREAMING_SNAKE_CASE ) ) + [0] * 64
for i in range(16 , 80 ):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def _lowercase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :str = self.padding()
SCREAMING_SNAKE_CASE_ :Optional[int] = self.split_blocks()
for block in self.blocks:
SCREAMING_SNAKE_CASE_ :Dict = self.expand_block(SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :str = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
SCREAMING_SNAKE_CASE_ :Optional[int] = (b & c) | ((~b) & d)
SCREAMING_SNAKE_CASE_ :str = 0x5A_82_79_99
elif 20 <= i < 40:
SCREAMING_SNAKE_CASE_ :Optional[Any] = b ^ c ^ d
SCREAMING_SNAKE_CASE_ :Dict = 0x6E_D9_EB_A1
elif 40 <= i < 60:
SCREAMING_SNAKE_CASE_ :int = (b & c) | (b & d) | (c & d)
SCREAMING_SNAKE_CASE_ :Optional[int] = 0x8F_1B_BC_DC
elif 60 <= i < 80:
SCREAMING_SNAKE_CASE_ :Any = b ^ c ^ d
SCREAMING_SNAKE_CASE_ :Tuple = 0xCA_62_C1_D6
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :List[str] = (
self.rotate(SCREAMING_SNAKE_CASE , 5 ) + f + e + k + expanded_block[i] & 0xFF_FF_FF_FF,
a,
self.rotate(SCREAMING_SNAKE_CASE , 30 ),
c,
d,
)
SCREAMING_SNAKE_CASE_ :Any = (
self.h[0] + a & 0xFF_FF_FF_FF,
self.h[1] + b & 0xFF_FF_FF_FF,
self.h[2] + c & 0xFF_FF_FF_FF,
self.h[3] + d & 0xFF_FF_FF_FF,
self.h[4] + e & 0xFF_FF_FF_FF,
)
return ("{:08x}" * 5).format(*self.h )
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE_ :Optional[Any] = B'Test String'
assert SHAaHash(SCREAMING_SNAKE_CASE ).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE ).hexdigest() # noqa: S324
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE_ :List[str] = argparse.ArgumentParser(description='Process some strings or files' )
parser.add_argument(
'--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument('--file' , dest='input_file' , help='Hash contents of a file' )
SCREAMING_SNAKE_CASE_ :List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE_ :List[Any] = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = f.read()
else:
SCREAMING_SNAKE_CASE_ :Dict = bytes(SCREAMING_SNAKE_CASE , 'utf-8' )
print(SHAaHash(SCREAMING_SNAKE_CASE ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 233 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase( lowerCAmelCase__ ):
__snake_case : List[str] = ['image_processor', 'tokenizer']
__snake_case : Optional[int] = 'Pix2StructImageProcessor'
__snake_case : Optional[int] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Optional[Any] = False
super().__init__(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __call__( self : Tuple , SCREAMING_SNAKE_CASE : Any=None , SCREAMING_SNAKE_CASE : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Union[bool, str, PaddingStrategy] = False , SCREAMING_SNAKE_CASE : Union[bool, str, TruncationStrategy] = None , SCREAMING_SNAKE_CASE : Optional[int] = None , SCREAMING_SNAKE_CASE : Optional[int] = 2_048 , SCREAMING_SNAKE_CASE : int = 0 , SCREAMING_SNAKE_CASE : Optional[int] = None , SCREAMING_SNAKE_CASE : Optional[bool] = None , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , **SCREAMING_SNAKE_CASE : str , ):
"""simple docstring"""
if images is None and text is None:
raise ValueError('You have to specify either images or text.' )
# Get only text
if images is None and not self.image_processor.is_vqa:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = self.tokenizer
SCREAMING_SNAKE_CASE_ :Tuple = self.tokenizer(
text=SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , pad_to_multiple_of=SCREAMING_SNAKE_CASE , return_attention_mask=SCREAMING_SNAKE_CASE , return_overflowing_tokens=SCREAMING_SNAKE_CASE , return_special_tokens_mask=SCREAMING_SNAKE_CASE , return_offsets_mapping=SCREAMING_SNAKE_CASE , return_token_type_ids=SCREAMING_SNAKE_CASE , return_length=SCREAMING_SNAKE_CASE , verbose=SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
SCREAMING_SNAKE_CASE_ :Any = self.image_processor(
SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , max_patches=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
else:
# add pixel_values and bbox
SCREAMING_SNAKE_CASE_ :Union[str, Any] = self.image_processor(
SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , max_patches=SCREAMING_SNAKE_CASE , header_text=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
if text is not None and not self.image_processor.is_vqa:
SCREAMING_SNAKE_CASE_ :Any = self.tokenizer(
text=SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , pad_to_multiple_of=SCREAMING_SNAKE_CASE , return_attention_mask=SCREAMING_SNAKE_CASE , return_overflowing_tokens=SCREAMING_SNAKE_CASE , return_special_tokens_mask=SCREAMING_SNAKE_CASE , return_offsets_mapping=SCREAMING_SNAKE_CASE , return_token_type_ids=SCREAMING_SNAKE_CASE , return_length=SCREAMING_SNAKE_CASE , verbose=SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
if "attention_mask" in text_encoding:
SCREAMING_SNAKE_CASE_ :List[Any] = text_encoding.pop('attention_mask' )
if "input_ids" in text_encoding:
SCREAMING_SNAKE_CASE_ :Any = text_encoding.pop('input_ids' )
else:
SCREAMING_SNAKE_CASE_ :Any = None
if text_encoding is not None:
encoding_image_processor.update(SCREAMING_SNAKE_CASE )
return encoding_image_processor
def _lowercase ( self : Tuple , *SCREAMING_SNAKE_CASE : Optional[int] , **SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
def _lowercase ( self : Tuple , *SCREAMING_SNAKE_CASE : List[str] , **SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@property
def _lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Tuple = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ :Optional[int] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 233 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ : Tuple = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
"configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[str] = ["MaskFormerFeatureExtractor"]
UpperCamelCase__ : int = ["MaskFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Optional[int] = [
"MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"MaskFormerForInstanceSegmentation",
"MaskFormerModel",
"MaskFormerPreTrainedModel",
]
UpperCamelCase__ : str = [
"MaskFormerSwinBackbone",
"MaskFormerSwinModel",
"MaskFormerSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 614 | import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
SCREAMING_SNAKE_CASE : Optional[int] = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class UpperCamelCase ( __a ):
def __init__(self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=1 ) -> List[str]:
UpperCamelCase_ : Dict = tokenizer
UpperCamelCase_ : Dict = dataset
UpperCamelCase_ : str = len(__UpperCamelCase ) if n_tasks is None else n_tasks
UpperCamelCase_ : int = n_copies
def __iter__(self ) -> Any:
UpperCamelCase_ : int = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]["""prompt"""].strip() )
UpperCamelCase_ : Any = self.tokenizer(__UpperCamelCase , padding=__UpperCamelCase , return_tensors="""pt""" )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class UpperCamelCase ( __a ):
def __init__(self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Dict:
UpperCamelCase_ : List[Any] = start_length
UpperCamelCase_ : List[str] = eof_strings
UpperCamelCase_ : str = tokenizer
def __call__(self , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ) -> Union[str, Any]:
UpperCamelCase_ : List[str] = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
UpperCamelCase_ : List[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(__UpperCamelCase )
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : Dict ):
UpperCamelCase_ : int = re.split("""(%s)""" % """|""".join(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Optional[Any]=20 , **_SCREAMING_SNAKE_CASE : List[str] ):
UpperCamelCase_ : Union[str, Any] = defaultdict(_SCREAMING_SNAKE_CASE ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_SCREAMING_SNAKE_CASE ) ):
with torch.no_grad():
UpperCamelCase_ : str = batch["""ids"""].shape[-1]
UpperCamelCase_ : List[str] = accelerator.unwrap_model(_SCREAMING_SNAKE_CASE ).generate(
input_ids=batch["""ids"""][:, : batch["""input_len"""]] , num_return_sequences=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
# each task is generated batch_size times
UpperCamelCase_ : List[str] = batch["""task_id"""].repeat(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Union[str, Any] = accelerator.pad_across_processes(
_SCREAMING_SNAKE_CASE , dim=1 , pad_index=tokenizer.pad_token_id )
UpperCamelCase_,UpperCamelCase_ : Any = accelerator.gather((generated_tokens, generated_tasks) )
UpperCamelCase_ : str = generated_tokens.cpu().numpy()
UpperCamelCase_ : List[Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
gen_token_dict[task].append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ : List[str] = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
UpperCamelCase_ : List[Any] = tokenizer.decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE )
code_gens[task].append(remove_last_block(_SCREAMING_SNAKE_CASE ) )
return code_gens
def lowerCAmelCase_ ( ):
# Setup configuration
UpperCamelCase_ : List[Any] = HfArgumentParser(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Optional[Any] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
UpperCamelCase_ : int = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
UpperCamelCase_ : str = """false"""
if args.num_workers is None:
UpperCamelCase_ : List[str] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
UpperCamelCase_ : Optional[Any] = Accelerator()
set_seed(args.seed , device_specific=_SCREAMING_SNAKE_CASE )
# Load model and tokenizer
UpperCamelCase_ : str = AutoTokenizer.from_pretrained(args.model_ckpt )
UpperCamelCase_ : Any = tokenizer.eos_token
UpperCamelCase_ : Tuple = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
UpperCamelCase_ : Union[str, Any] = {
"""do_sample""": args.do_sample,
"""temperature""": args.temperature,
"""max_new_tokens""": args.max_new_tokens,
"""top_p""": args.top_p,
"""top_k""": args.top_k,
"""stopping_criteria""": StoppingCriteriaList([EndOfFunctionCriteria(0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )] ),
}
# Load evaluation dataset and metric
UpperCamelCase_ : Optional[int] = load_dataset("""openai_humaneval""" )
UpperCamelCase_ : Any = load_metric("""code_eval""" )
UpperCamelCase_ : int = args.num_tasks if args.num_tasks is not None else len(human_eval["""test"""] )
UpperCamelCase_ : List[str] = args.n_samples // args.batch_size
UpperCamelCase_ : Tuple = TokenizedDataset(_SCREAMING_SNAKE_CASE , human_eval["""test"""] , n_copies=_SCREAMING_SNAKE_CASE , n_tasks=_SCREAMING_SNAKE_CASE )
# do not confuse args.batch_size, which is actually the num_return_sequences
UpperCamelCase_ : List[str] = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
UpperCamelCase_ : str = code_eval_metric.compute(references=[""""""] , predictions=[[""""""]] )
except ValueError as exception:
print(
"""Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"""
""" flag to enable code evaluation.""" )
raise exception
UpperCamelCase_,UpperCamelCase_ : int = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Optional[int] = complete_code(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , n_tasks=_SCREAMING_SNAKE_CASE , batch_size=args.batch_size , **_SCREAMING_SNAKE_CASE , )
if accelerator.is_main_process:
UpperCamelCase_ : List[str] = []
for task in tqdm(range(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase_ : int = human_eval["""test"""][task]["""test"""]
UpperCamelCase_ : Tuple = f'''check({human_eval["test"][task]["entry_point"]})'''
references.append("""\n""" + test_func + """\n""" + entry_point )
# Evaluate completions with "code_eval" metric
UpperCamelCase_,UpperCamelCase_ : Any = code_eval_metric.compute(
references=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , """w""" ) as fp:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 635 | 0 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
lowercase__ :Tuple = 'base_with_context'
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ) ->Optional[int]:
"""simple docstring"""
__UpperCAmelCase : Dict = nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding'''] ) )
__UpperCAmelCase : Tuple = nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=UpperCAmelCase_ )
for lyr_num, lyr in enumerate(model.encoders ):
__UpperCAmelCase : int = weights[f'''layers_{lyr_num}''']
__UpperCAmelCase : Tuple = nn.Parameter(
torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale'''] ) )
__UpperCAmelCase : List[Any] = ly_weight['''attention''']
__UpperCAmelCase : int = nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
__UpperCAmelCase : Tuple = nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
__UpperCAmelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
__UpperCAmelCase : Dict = nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
__UpperCAmelCase : str = nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
__UpperCAmelCase : List[str] = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
__UpperCAmelCase : Optional[int] = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
__UpperCAmelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
__UpperCAmelCase : Tuple = nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale'''] ) )
return model
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ) ->Any:
"""simple docstring"""
__UpperCAmelCase : List[Any] = nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T ) )
__UpperCAmelCase : Dict = nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=UpperCAmelCase_ )
for lyr_num, lyr in enumerate(model.encoders ):
__UpperCAmelCase : Optional[int] = weights[f'''layers_{lyr_num}''']
__UpperCAmelCase : Any = ly_weight['''attention''']
__UpperCAmelCase : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
__UpperCAmelCase : int = nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
__UpperCAmelCase : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
__UpperCAmelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
__UpperCAmelCase : Tuple = nn.Parameter(
torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale'''] ) )
__UpperCAmelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
__UpperCAmelCase : str = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
__UpperCAmelCase : str = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
__UpperCAmelCase : Tuple = nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
__UpperCAmelCase : Any = nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale'''] ) )
return model
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ) ->Any:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T ) )
__UpperCAmelCase : List[Any] = nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T ) )
__UpperCAmelCase : int = nn.Parameter(
torch.FloatTensor(weights['''Embed_0''']['''embedding'''] ) , requires_grad=UpperCAmelCase_ )
__UpperCAmelCase : Dict = nn.Parameter(
torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
__UpperCAmelCase : Optional[Any] = weights[f'''layers_{lyr_num}''']
__UpperCAmelCase : List[str] = nn.Parameter(
torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale'''] ) )
__UpperCAmelCase : Any = nn.Parameter(
torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T ) )
__UpperCAmelCase : Union[str, Any] = ly_weight['''self_attention''']
__UpperCAmelCase : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
__UpperCAmelCase : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
__UpperCAmelCase : Any = nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
__UpperCAmelCase : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
__UpperCAmelCase : int = ly_weight['''MultiHeadDotProductAttention_0''']
__UpperCAmelCase : Tuple = nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T ) )
__UpperCAmelCase : Dict = nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T ) )
__UpperCAmelCase : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T ) )
__UpperCAmelCase : Dict = nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T ) )
__UpperCAmelCase : Dict = nn.Parameter(
torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale'''] ) )
__UpperCAmelCase : Optional[int] = nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale'''] ) )
__UpperCAmelCase : List[str] = nn.Parameter(
torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T ) )
__UpperCAmelCase : str = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T ) )
__UpperCAmelCase : Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T ) )
__UpperCAmelCase : str = nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T ) )
__UpperCAmelCase : Optional[Any] = nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale'''] ) )
__UpperCAmelCase : List[str] = nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T ) )
return model
def lowerCamelCase_ ( UpperCAmelCase_ ) ->str:
"""simple docstring"""
__UpperCAmelCase : int = checkpoints.load_tax_checkpoint(args.checkpoint_path )
__UpperCAmelCase : str = jnp.tree_util.tree_map(onp.array , UpperCAmelCase_ )
__UpperCAmelCase : List[str] = [
'''from __gin__ import dynamic_registration''',
'''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
'''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
'''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
]
__UpperCAmelCase : Union[str, Any] = os.path.join(args.checkpoint_path , '''..''' , '''config.gin''' )
__UpperCAmelCase : int = inference.parse_training_gin_file(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCAmelCase : Union[str, Any] = inference.InferenceModel(args.checkpoint_path , UpperCAmelCase_ )
__UpperCAmelCase : Tuple = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' , variance_type='''fixed_large''' )
__UpperCAmelCase : str = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length['''inputs'''] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='''gated-gelu''' , )
__UpperCAmelCase : Optional[int] = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length['''targets_context'''] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='''gated-gelu''' , )
__UpperCAmelCase : Dict = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length['''targets_context'''] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
__UpperCAmelCase : List[str] = load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''] , UpperCAmelCase_ )
__UpperCAmelCase : int = load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''] , UpperCAmelCase_ )
__UpperCAmelCase : Dict = load_decoder(ta_checkpoint['''target''']['''decoder'''] , UpperCAmelCase_ )
__UpperCAmelCase : Optional[int] = OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder''' )
__UpperCAmelCase : str = SpectrogramDiffusionPipeline(
notes_encoder=UpperCAmelCase_ , continuous_encoder=UpperCAmelCase_ , decoder=UpperCAmelCase_ , scheduler=UpperCAmelCase_ , melgan=UpperCAmelCase_ , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
lowercase__ :Optional[int] = argparse.ArgumentParser()
parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
parser.add_argument(
'--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
)
parser.add_argument(
'--checkpoint_path',
default=f"""{MODEL}/checkpoint_500000""",
type=str,
required=False,
help='Path to the original jax model checkpoint.',
)
lowercase__ :Dict = parser.parse_args()
main(args) | 374 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : torch.FloatTensor
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
@register_to_config
def __init__( self : Union[str, Any] , __lowercase : int = 32 , __lowercase : int = 64 , __lowercase : int = 20 , __lowercase : int = 768 , __lowercase : Optional[int]=77 , __lowercase : Union[str, Any]=4 , __lowercase : float = 0.0 , __lowercase : str = "silu" , __lowercase : Optional[str] = None , __lowercase : Optional[str] = None , __lowercase : Optional[str] = "linear" , __lowercase : Optional[str] = "prd" , __lowercase : Optional[int] = None , __lowercase : Optional[int] = None , __lowercase : Optional[int] = None , ):
'''simple docstring'''
super().__init__()
__UpperCAmelCase : int = num_attention_heads
__UpperCAmelCase : List[Any] = attention_head_dim
__UpperCAmelCase : int = num_attention_heads * attention_head_dim
__UpperCAmelCase : List[str] = additional_embeddings
__UpperCAmelCase : Optional[int] = time_embed_dim or inner_dim
__UpperCAmelCase : Tuple = embedding_proj_dim or embedding_dim
__UpperCAmelCase : Dict = clip_embed_dim or embedding_dim
__UpperCAmelCase : Dict = Timesteps(__lowercase , __lowercase , 0 )
__UpperCAmelCase : List[str] = TimestepEmbedding(__lowercase , __lowercase , out_dim=__lowercase , act_fn=__lowercase )
__UpperCAmelCase : Any = nn.Linear(__lowercase , __lowercase )
if embedding_proj_norm_type is None:
__UpperCAmelCase : Dict = None
elif embedding_proj_norm_type == "layer":
__UpperCAmelCase : Any = nn.LayerNorm(__lowercase )
else:
raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''' )
__UpperCAmelCase : List[Any] = nn.Linear(__lowercase , __lowercase )
if encoder_hid_proj_type is None:
__UpperCAmelCase : int = None
elif encoder_hid_proj_type == "linear":
__UpperCAmelCase : int = nn.Linear(__lowercase , __lowercase )
else:
raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''' )
__UpperCAmelCase : str = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , __lowercase ) )
if added_emb_type == "prd":
__UpperCAmelCase : Optional[int] = nn.Parameter(torch.zeros(1 , 1 , __lowercase ) )
elif added_emb_type is None:
__UpperCAmelCase : str = None
else:
raise ValueError(
f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''' )
__UpperCAmelCase : Union[str, Any] = nn.ModuleList(
[
BasicTransformerBlock(
__lowercase , __lowercase , __lowercase , dropout=__lowercase , activation_fn='''gelu''' , attention_bias=__lowercase , )
for d in range(__lowercase )
] )
if norm_in_type == "layer":
__UpperCAmelCase : Optional[Any] = nn.LayerNorm(__lowercase )
elif norm_in_type is None:
__UpperCAmelCase : List[Any] = None
else:
raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''' )
__UpperCAmelCase : str = nn.LayerNorm(__lowercase )
__UpperCAmelCase : List[Any] = nn.Linear(__lowercase , __lowercase )
__UpperCAmelCase : List[Any] = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_0_0_0_0.0 )
causal_attention_mask.triu_(1 )
__UpperCAmelCase : Optional[Any] = causal_attention_mask[None, ...]
self.register_buffer('''causal_attention_mask''' , __lowercase , persistent=__lowercase )
__UpperCAmelCase : Any = nn.Parameter(torch.zeros(1 , __lowercase ) )
__UpperCAmelCase : List[str] = nn.Parameter(torch.zeros(1 , __lowercase ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def A_ ( self : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = {}
def fn_recursive_add_processors(__lowercase : str , __lowercase : torch.nn.Module , __lowercase : Dict[str, AttentionProcessor] ):
if hasattr(__lowercase , '''set_processor''' ):
__UpperCAmelCase : Optional[Any] = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'''{name}.{sub_name}''' , __lowercase , __lowercase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__lowercase , __lowercase , __lowercase )
return processors
def A_ ( self : Any , __lowercase : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):
'''simple docstring'''
__UpperCAmelCase : Tuple = len(self.attn_processors.keys() )
if isinstance(__lowercase , __lowercase ) and len(__lowercase ) != count:
raise ValueError(
f'''A dict of processors was passed, but the number of processors {len(__lowercase )} does not match the'''
f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )
def fn_recursive_attn_processor(__lowercase : str , __lowercase : torch.nn.Module , __lowercase : int ):
if hasattr(__lowercase , '''set_processor''' ):
if not isinstance(__lowercase , __lowercase ):
module.set_processor(__lowercase )
else:
module.set_processor(processor.pop(f'''{name}.processor''' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'''{name}.{sub_name}''' , __lowercase , __lowercase )
for name, module in self.named_children():
fn_recursive_attn_processor(__lowercase , __lowercase , __lowercase )
def A_ ( self : Optional[Any] ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
def A_ ( self : Optional[int] , __lowercase : Dict , __lowercase : Union[torch.Tensor, float, int] , __lowercase : torch.FloatTensor , __lowercase : Optional[torch.FloatTensor] = None , __lowercase : Optional[torch.BoolTensor] = None , __lowercase : bool = True , ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = hidden_states.shape[0]
__UpperCAmelCase : Any = timestep
if not torch.is_tensor(__lowercase ):
__UpperCAmelCase : Union[str, Any] = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(__lowercase ) and len(timesteps.shape ) == 0:
__UpperCAmelCase : Union[str, Any] = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__UpperCAmelCase : List[str] = timesteps * torch.ones(__lowercase , dtype=timesteps.dtype , device=timesteps.device )
__UpperCAmelCase : List[str] = self.time_proj(__lowercase )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
__UpperCAmelCase : Any = timesteps_projected.to(dtype=self.dtype )
__UpperCAmelCase : List[str] = self.time_embedding(__lowercase )
if self.embedding_proj_norm is not None:
__UpperCAmelCase : Dict = self.embedding_proj_norm(__lowercase )
__UpperCAmelCase : Optional[int] = self.embedding_proj(__lowercase )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
__UpperCAmelCase : Dict = self.encoder_hidden_states_proj(__lowercase )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''' )
__UpperCAmelCase : int = self.proj_in(__lowercase )
__UpperCAmelCase : Tuple = self.positional_embedding.to(hidden_states.dtype )
__UpperCAmelCase : Optional[Any] = []
__UpperCAmelCase : str = 0
if encoder_hidden_states is not None:
additional_embeds.append(__lowercase )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
__UpperCAmelCase : str = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
__UpperCAmelCase : Optional[int] = hidden_states[:, None, :]
__UpperCAmelCase : str = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
__UpperCAmelCase : Dict = self.prd_embedding.to(hidden_states.dtype ).expand(__lowercase , -1 , -1 )
additional_embeds.append(__lowercase )
__UpperCAmelCase : Optional[Any] = torch.cat(
__lowercase , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
__UpperCAmelCase : Optional[int] = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
__UpperCAmelCase : List[str] = F.pad(
__lowercase , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
__UpperCAmelCase : Dict = hidden_states + positional_embeddings
if attention_mask is not None:
__UpperCAmelCase : Optional[Any] = (1 - attention_mask.to(hidden_states.dtype )) * -1_0_0_0_0.0
__UpperCAmelCase : Optional[Any] = F.pad(__lowercase , (0, self.additional_embeddings) , value=0.0 )
__UpperCAmelCase : Dict = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
__UpperCAmelCase : Tuple = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
__UpperCAmelCase : str = self.norm_in(__lowercase )
for block in self.transformer_blocks:
__UpperCAmelCase : Dict = block(__lowercase , attention_mask=__lowercase )
__UpperCAmelCase : Any = self.norm_out(__lowercase )
if self.prd_embedding is not None:
__UpperCAmelCase : List[Any] = hidden_states[:, -1]
else:
__UpperCAmelCase : List[str] = hidden_states[:, additional_embeddings_len:]
__UpperCAmelCase : Optional[int] = self.proj_to_clip_embeddings(__lowercase )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=__lowercase )
def A_ ( self : List[Any] , __lowercase : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : List[str] = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents | 374 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _SCREAMING_SNAKE_CASE ( lowercase : List[str] , lowercase : str , lowercase : Dict ):
'''simple docstring'''
lowerCamelCase_ = 0
while b > 0:
if b & 1:
lowerCamelCase_ = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 70 |
def _UpperCAmelCase ( UpperCAmelCase : list ):
"""simple docstring"""
__lowerCamelCase : Tuple = 0
while len(UpperCAmelCase ) > 1:
__lowerCamelCase : List[str] = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
__lowerCamelCase : List[Any] = files.index(min(UpperCAmelCase ) )
temp += files[min_index]
files.pop(UpperCAmelCase )
files.append(UpperCAmelCase )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| 519 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
_lowerCamelCase = logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCamelCase = 'cuda' if torch.cuda.is_available() else 'cpu'
def _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_=100 , snake_case_=" " ):
_lowercase = text.split(snake_case_ )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(snake_case_ ) , snake_case_ )]
def _SCREAMING_SNAKE_CASE ( snake_case_ ):
_lowercase , _lowercase = [], []
for title, text in zip(documents["""title"""] , documents["""text"""] ):
if text is not None:
for passage in split_text(snake_case_ ):
titles.append(title if title is not None else """""" )
texts.append(snake_case_ )
return {"title": titles, "text": texts}
def _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ ):
_lowercase = ctx_tokenizer(
documents["""title"""] , documents["""text"""] , truncation=snake_case_ , padding="""longest""" , return_tensors="""pt""" )["""input_ids"""]
_lowercase = ctx_encoder(input_ids.to(device=snake_case_ ) , return_dict=snake_case_ ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ , ):
######################################
logger.info("""Step 1 - Create the dataset""" )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
_lowercase = load_dataset(
"""csv""" , data_files=[rag_example_args.csv_path] , split="""train""" , delimiter="""\t""" , column_names=["""title""", """text"""] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
_lowercase = dataset.map(snake_case_ , batched=snake_case_ , num_proc=processing_args.num_proc )
# And compute the embeddings
_lowercase = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=snake_case_ )
_lowercase = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
_lowercase = Features(
{"""text""": Value("""string""" ), """title""": Value("""string""" ), """embeddings""": Sequence(Value("""float32""" ) )} ) # optional, save as float32 instead of float64 to save space
_lowercase = dataset.map(
partial(snake_case_ , ctx_encoder=snake_case_ , ctx_tokenizer=snake_case_ ) , batched=snake_case_ , batch_size=processing_args.batch_size , features=snake_case_ , )
# And finally save your dataset
_lowercase = os.path.join(rag_example_args.output_dir , """my_knowledge_dataset""" )
dataset.save_to_disk(snake_case_ )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info("""Step 2 - Index the dataset""" )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
_lowercase = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index("""embeddings""" , custom_index=snake_case_ )
# And save the index
_lowercase = os.path.join(rag_example_args.output_dir , """my_knowledge_dataset_hnsw_index.faiss""" )
dataset.get_index("""embeddings""" ).save(snake_case_ )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class __a :
__SCREAMING_SNAKE_CASE : str = field(
default=str(Path(_snake_case ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv' ) ,metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''} ,)
__SCREAMING_SNAKE_CASE : Optional[str] = field(
default=_snake_case ,metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} ,)
__SCREAMING_SNAKE_CASE : str = field(
default='facebook/rag-sequence-nq' ,metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} ,)
__SCREAMING_SNAKE_CASE : str = field(
default='facebook/dpr-ctx_encoder-multiset-base' ,metadata={
'help': (
'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
' \'facebook/dpr-ctx_encoder-multiset-base\''
)
} ,)
__SCREAMING_SNAKE_CASE : Optional[str] = field(
default=str(Path(_snake_case ).parent / 'test_run' / 'dummy-kb' ) ,metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} ,)
@dataclass
class __a :
__SCREAMING_SNAKE_CASE : Optional[int] = field(
default=_snake_case ,metadata={
'help': 'The number of processes to use to split the documents into passages. Default is single process.'
} ,)
__SCREAMING_SNAKE_CASE : int = field(
default=1_6 ,metadata={
'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
} ,)
@dataclass
class __a :
__SCREAMING_SNAKE_CASE : int = field(
default=7_6_8 ,metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} ,)
__SCREAMING_SNAKE_CASE : int = field(
default=1_2_8 ,metadata={
'help': (
'The number of bi-directional links created for every new element during the HNSW index construction.'
)
} ,)
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
_lowerCamelCase = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
_lowerCamelCase = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 572 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
'convert_funnel_original_tf_checkpoint_to_pytorch': [],
'tokenization_funnel': ['FunnelTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = ['FunnelTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'FunnelBaseModel',
'FunnelForMaskedLM',
'FunnelForMultipleChoice',
'FunnelForPreTraining',
'FunnelForQuestionAnswering',
'FunnelForSequenceClassification',
'FunnelForTokenClassification',
'FunnelModel',
'FunnelPreTrainedModel',
'load_tf_weights_in_funnel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFFunnelBaseModel',
'TFFunnelForMaskedLM',
'TFFunnelForMultipleChoice',
'TFFunnelForPreTraining',
'TFFunnelForQuestionAnswering',
'TFFunnelForSequenceClassification',
'TFFunnelForTokenClassification',
'TFFunnelModel',
'TFFunnelPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 572 | 1 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : Tuple = logging.get_logger(__name__)
_UpperCamelCase : Union[str, Any] = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
),
}
class snake_case__ ( UpperCamelCase):
a_ = "efficientformer"
def __init__( self : Optional[Any] , _A : List[int] = [3, 2, 6, 4] , _A : List[int] = [48, 96, 2_24, 4_48] , _A : List[bool] = [True, True, True, True] , _A : int = 4_48 , _A : int = 32 , _A : int = 4 , _A : int = 7 , _A : int = 5 , _A : int = 8 , _A : int = 4 , _A : float = 0.0 , _A : int = 16 , _A : int = 3 , _A : int = 3 , _A : int = 3 , _A : int = 2 , _A : int = 1 , _A : float = 0.0 , _A : int = 1 , _A : bool = True , _A : bool = True , _A : float = 1e-5 , _A : str = "gelu" , _A : float = 0.02 , _A : float = 1e-12 , _A : int = 2_24 , _A : float = 1e-05 , **_A : Union[str, Any] , ) -> None:
super().__init__(**_A )
UpperCAmelCase_ : Dict = hidden_act
UpperCAmelCase_ : List[str] = hidden_dropout_prob
UpperCAmelCase_ : List[str] = hidden_sizes
UpperCAmelCase_ : int = num_hidden_layers
UpperCAmelCase_ : List[str] = num_attention_heads
UpperCAmelCase_ : Dict = initializer_range
UpperCAmelCase_ : Dict = layer_norm_eps
UpperCAmelCase_ : Optional[int] = patch_size
UpperCAmelCase_ : Any = num_channels
UpperCAmelCase_ : Any = depths
UpperCAmelCase_ : Optional[int] = mlp_expansion_ratio
UpperCAmelCase_ : Union[str, Any] = downsamples
UpperCAmelCase_ : Any = dim
UpperCAmelCase_ : Optional[int] = key_dim
UpperCAmelCase_ : int = attention_ratio
UpperCAmelCase_ : Union[str, Any] = resolution
UpperCAmelCase_ : Union[str, Any] = pool_size
UpperCAmelCase_ : Dict = downsample_patch_size
UpperCAmelCase_ : int = downsample_stride
UpperCAmelCase_ : Optional[Any] = downsample_pad
UpperCAmelCase_ : Union[str, Any] = drop_path_rate
UpperCAmelCase_ : Optional[Any] = num_metaad_blocks
UpperCAmelCase_ : Optional[int] = distillation
UpperCAmelCase_ : Union[str, Any] = use_layer_scale
UpperCAmelCase_ : Optional[int] = layer_scale_init_value
UpperCAmelCase_ : str = image_size
UpperCAmelCase_ : str = batch_norm_eps
| 541 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( A : Optional[int] , A : Optional[int] ) -> str:
UpperCAmelCase_ : List[Any] = []
for part_id in partition_order:
UpperCAmelCase_ : Any = df.where(F"SPARK_PARTITION_ID() = {part_id}" ).collect()
for row_idx, row in enumerate(A ):
expected_row_ids_and_row_dicts.append((F"{part_id}_{row_idx}", row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ) -> Any:
UpperCAmelCase_ : Any = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
UpperCAmelCase_ : List[str] = spark.range(1_0_0 ).repartition(1 )
UpperCAmelCase_ : List[Any] = Spark(A )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=1_6 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ) -> str:
UpperCAmelCase_ : List[str] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
UpperCAmelCase_ : Optional[Any] = spark.range(1_0 ).repartition(2 )
UpperCAmelCase_ : int = [1, 0]
UpperCAmelCase_ : str = _generate_iterable_examples(A , A ) # Reverse the partitions.
UpperCAmelCase_ : Optional[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(A , A )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
UpperCAmelCase_ , UpperCAmelCase_ : int = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ) -> Union[str, Any]:
UpperCAmelCase_ : str = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
UpperCAmelCase_ : List[Any] = spark.range(1_0 ).repartition(1 )
UpperCAmelCase_ : Optional[int] = SparkExamplesIterable(A )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(A ):
assert row_id == F"0_{i}"
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ) -> Union[str, Any]:
UpperCAmelCase_ : Dict = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
UpperCAmelCase_ : Dict = spark.range(3_0 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch('''numpy.random.Generator''' ) as generator_mock:
UpperCAmelCase_ : Any = lambda A : x.reverse()
UpperCAmelCase_ : Dict = _get_expected_row_ids_and_row_dicts_for_partition_order(A , [2, 1, 0] )
UpperCAmelCase_ : List[Any] = SparkExamplesIterable(A ).shuffle_data_sources(A )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(A ):
UpperCAmelCase_ , UpperCAmelCase_ : Dict = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ) -> int:
UpperCAmelCase_ : Tuple = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
UpperCAmelCase_ : int = spark.range(2_0 ).repartition(4 )
# Partitions 0 and 2
UpperCAmelCase_ : int = SparkExamplesIterable(A ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
UpperCAmelCase_ : Union[str, Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(A , [0, 2] )
for i, (row_id, row_dict) in enumerate(A ):
UpperCAmelCase_ , UpperCAmelCase_ : Dict = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
UpperCAmelCase_ : Tuple = SparkExamplesIterable(A ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
UpperCAmelCase_ : List[str] = _get_expected_row_ids_and_row_dicts_for_partition_order(A , [1, 3] )
for i, (row_id, row_dict) in enumerate(A ):
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ) -> Any:
UpperCAmelCase_ : List[Any] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
UpperCAmelCase_ : List[str] = spark.range(1_0_0 ).repartition(1 )
UpperCAmelCase_ : int = Spark(A )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_0_0
| 541 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( _lowercase , unittest.TestCase ):
"""simple docstring"""
_lowercase : str = AudioLDMPipeline
_lowercase : Optional[int] = TEXT_TO_AUDIO_PARAMS
_lowercase : Optional[Any] = TEXT_TO_AUDIO_BATCH_PARAMS
_lowercase : str = frozenset(
[
'''num_inference_steps''',
'''num_waveforms_per_prompt''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def _UpperCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
a__ = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=(3_2, 6_4) , class_embed_type='''simple_projection''' , projection_class_embeddings_input_dim=3_2 , class_embeddings_concat=SCREAMING_SNAKE_CASE , )
a__ = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=SCREAMING_SNAKE_CASE , set_alpha_to_one=SCREAMING_SNAKE_CASE , )
torch.manual_seed(0 )
a__ = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=1 , out_channels=1 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
a__ = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , projection_dim=3_2 , )
a__ = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE )
a__ = RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta''' , model_max_length=7_7 )
a__ = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6_0_0_0 , upsample_initial_channel=1_6 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE , )
a__ = SpeechTaHifiGan(SCREAMING_SNAKE_CASE )
a__ = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''vocoder''': vocoder,
}
return components
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=0 ) -> Any:
if str(SCREAMING_SNAKE_CASE ).startswith('''mps''' ):
a__ = torch.manual_seed(SCREAMING_SNAKE_CASE )
else:
a__ = torch.Generator(device=SCREAMING_SNAKE_CASE ).manual_seed(SCREAMING_SNAKE_CASE )
a__ = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
}
return inputs
def _UpperCAmelCase ( self ) -> Dict:
a__ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a__ = self.get_dummy_components()
a__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe(**SCREAMING_SNAKE_CASE )
a__ = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE ) == 2_5_6
a__ = audio[:1_0]
a__ = np.array(
[-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ) -> Any:
a__ = self.get_dummy_components()
a__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE )
a__ = 3 * [inputs['''prompt''']]
# forward
a__ = audioldm_pipe(**SCREAMING_SNAKE_CASE )
a__ = output.audios[0]
a__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE )
a__ = 3 * [inputs.pop('''prompt''' )]
a__ = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE , padding='''max_length''' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
a__ = text_inputs['''input_ids'''].to(SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE , )
a__ = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
a__ = F.normalize(SCREAMING_SNAKE_CASE , dim=-1 )
a__ = prompt_embeds
# forward
a__ = audioldm_pipe(**SCREAMING_SNAKE_CASE )
a__ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def _UpperCAmelCase ( self ) -> Optional[Any]:
a__ = self.get_dummy_components()
a__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE )
a__ = 3 * ['''this is a negative prompt''']
a__ = negative_prompt
a__ = 3 * [inputs['''prompt''']]
# forward
a__ = audioldm_pipe(**SCREAMING_SNAKE_CASE )
a__ = output.audios[0]
a__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE )
a__ = 3 * [inputs.pop('''prompt''' )]
a__ = []
for p in [prompt, negative_prompt]:
a__ = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE , padding='''max_length''' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE , return_tensors='''pt''' , )
a__ = text_inputs['''input_ids'''].to(SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE , )
a__ = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
a__ = F.normalize(SCREAMING_SNAKE_CASE , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE )
a__ , a__ = embeds
# forward
a__ = audioldm_pipe(**SCREAMING_SNAKE_CASE )
a__ = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def _UpperCAmelCase ( self ) -> Dict:
a__ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a__ = self.get_dummy_components()
a__ = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE )
a__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE )
a__ = '''egg cracking'''
a__ = audioldm_pipe(**SCREAMING_SNAKE_CASE , negative_prompt=SCREAMING_SNAKE_CASE )
a__ = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE ) == 2_5_6
a__ = audio[:1_0]
a__ = np.array(
[-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def _UpperCAmelCase ( self ) -> List[str]:
a__ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a__ = self.get_dummy_components()
a__ = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE )
a__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = '''A hammer hitting a wooden surface'''
# test num_waveforms_per_prompt=1 (default)
a__ = audioldm_pipe(SCREAMING_SNAKE_CASE , num_inference_steps=2 ).audios
assert audios.shape == (1, 2_5_6)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
a__ = 2
a__ = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 2_5_6)
# test num_waveforms_per_prompt for single prompt
a__ = 2
a__ = audioldm_pipe(SCREAMING_SNAKE_CASE , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE ).audios
assert audios.shape == (num_waveforms_per_prompt, 2_5_6)
# test num_waveforms_per_prompt for batch of prompts
a__ = 2
a__ = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)
def _UpperCAmelCase ( self ) -> int:
a__ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a__ = self.get_dummy_components()
a__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.vocoder.config.sampling_rate
a__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe(audio_length_in_s=0.0_16 , **SCREAMING_SNAKE_CASE )
a__ = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE ) / vocoder_sampling_rate == 0.0_16
a__ = audioldm_pipe(audio_length_in_s=0.0_32 , **SCREAMING_SNAKE_CASE )
a__ = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE ) / vocoder_sampling_rate == 0.0_32
def _UpperCAmelCase ( self ) -> Union[str, Any]:
a__ = self.get_dummy_components()
a__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = ['''hey''']
a__ = audioldm_pipe(SCREAMING_SNAKE_CASE , num_inference_steps=1 )
a__ = output.audios.shape
assert audio_shape == (1, 2_5_6)
a__ = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
a__ = SpeechTaHifiGan(SCREAMING_SNAKE_CASE ).to(SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe(SCREAMING_SNAKE_CASE , num_inference_steps=1 )
a__ = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 2_5_6)
def _UpperCAmelCase ( self ) -> Dict:
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def _UpperCAmelCase ( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE )
@slow
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="cpu" , SCREAMING_SNAKE_CASE=torch.floataa , SCREAMING_SNAKE_CASE=0 ) -> Union[str, Any]:
a__ = torch.Generator(device=SCREAMING_SNAKE_CASE ).manual_seed(SCREAMING_SNAKE_CASE )
a__ = np.random.RandomState(SCREAMING_SNAKE_CASE ).standard_normal((1, 8, 1_2_8, 1_6) )
a__ = torch.from_numpy(SCREAMING_SNAKE_CASE ).to(device=SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE )
a__ = {
'''prompt''': '''A hammer hitting a wooden surface''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 2.5,
}
return inputs
def _UpperCAmelCase ( self ) -> str:
a__ = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''' )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = self.get_inputs(SCREAMING_SNAKE_CASE )
a__ = 2_5
a__ = audioldm_pipe(**SCREAMING_SNAKE_CASE ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE ) == 8_1_9_2_0
a__ = audio[7_7_2_3_0:7_7_2_4_0]
a__ = np.array(
[-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15] )
a__ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def _UpperCAmelCase ( self ) -> Any:
a__ = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''' )
a__ = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
a__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
a__ = self.get_inputs(SCREAMING_SNAKE_CASE )
a__ = audioldm_pipe(**SCREAMING_SNAKE_CASE ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE ) == 8_1_9_2_0
a__ = audio[2_7_7_8_0:2_7_7_9_0]
a__ = np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12] )
a__ = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 148 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
a__ = jnp.ones((batch_size, length) ) / length
return scores
def _UpperCAmelCase ( self ) -> int:
a__ = None
a__ = 2_0
a__ = self._get_uniform_logits(batch_size=2 , length=SCREAMING_SNAKE_CASE )
# tweak scores to not be uniform anymore
a__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
a__ = scores.at[1, 1_0].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
a__ = jax.nn.softmax(SCREAMING_SNAKE_CASE , axis=-1 )
a__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
a__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
a__ = jax.nn.softmax(temp_dist_warper_sharper(SCREAMING_SNAKE_CASE , scores.copy() , cur_len=SCREAMING_SNAKE_CASE ) , axis=-1 )
a__ = jax.nn.softmax(temp_dist_warper_smoother(SCREAMING_SNAKE_CASE , scores.copy() , cur_len=SCREAMING_SNAKE_CASE ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1e-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1e-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def _UpperCAmelCase ( self ) -> Dict:
a__ = None
a__ = 1_0
a__ = 2
# create ramp distribution
a__ = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE )[None, :] , (batch_size, vocab_size) ).copy()
a__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
a__ = FlaxTopKLogitsWarper(3 )
a__ = top_k_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
a__ = 5
a__ = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
a__ = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE )[None, :] , (batch_size, length) ).copy()
a__ = top_k_warp_safety_check(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def _UpperCAmelCase ( self ) -> List[str]:
a__ = None
a__ = 1_0
a__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
a__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
a__ = FlaxTopPLogitsWarper(0.8 )
a__ = np.exp(top_p_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
a__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# check edge cases with negative and extreme logits
a__ = np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
a__ = ramp_logits[1] * 1_00.0
# make sure at least 2 tokens are kept
a__ = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
a__ = top_p_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def _UpperCAmelCase ( self ) -> str:
a__ = 2_0
a__ = 4
a__ = 0
a__ = FlaxMinLengthLogitsProcessor(min_length=1_0 , eos_token_id=SCREAMING_SNAKE_CASE )
# check that min length is applied at length 5
a__ = ids_tensor((batch_size, 2_0) , vocab_size=2_0 )
a__ = 5
a__ = self._get_uniform_logits(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = min_dist_processor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('''inf''' )] )
# check that min length is not applied anymore at length 15
a__ = self._get_uniform_logits(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = 1_5
a__ = min_dist_processor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE ).any() )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
a__ = 2_0
a__ = 4
a__ = 0
a__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE )
# check that all scores are -inf except the bos_token_id score
a__ = ids_tensor((batch_size, 1) , vocab_size=2_0 )
a__ = 1
a__ = self._get_uniform_logits(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = logits_processor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
a__ = 3
a__ = self._get_uniform_logits(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = logits_processor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE ).any() )
def _UpperCAmelCase ( self ) -> str:
a__ = 2_0
a__ = 4
a__ = 0
a__ = 5
a__ = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE )
# check that all scores are -inf except the eos_token_id when max_length is reached
a__ = ids_tensor((batch_size, 4) , vocab_size=2_0 )
a__ = 4
a__ = self._get_uniform_logits(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = logits_processor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
a__ = 3
a__ = self._get_uniform_logits(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = logits_processor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE ).any() )
def _UpperCAmelCase ( self ) -> List[str]:
a__ = 4
a__ = 1_0
a__ = 1_5
a__ = 2
a__ = 1
a__ = 1_5
# dummy input_ids and scores
a__ = ids_tensor((batch_size, sequence_length) , SCREAMING_SNAKE_CASE )
a__ = input_ids.copy()
a__ = self._get_uniform_logits(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = scores.copy()
# instantiate all dist processors
a__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
a__ = FlaxTopKLogitsWarper(3 )
a__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
a__ = FlaxMinLengthLogitsProcessor(min_length=1_0 , eos_token_id=SCREAMING_SNAKE_CASE )
a__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE )
a__ = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE )
a__ = 1_0
# no processor list
a__ = temp_dist_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = top_k_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = top_p_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = min_dist_proc(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = bos_dist_proc(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = eos_dist_proc(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
# with processor list
a__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
a__ = processor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
# scores should be equal
self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def _UpperCAmelCase ( self ) -> Optional[int]:
a__ = 4
a__ = 1_0
a__ = 1_5
a__ = 2
a__ = 1
a__ = 1_5
# dummy input_ids and scores
a__ = ids_tensor((batch_size, sequence_length) , SCREAMING_SNAKE_CASE )
a__ = input_ids.copy()
a__ = self._get_uniform_logits(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = scores.copy()
# instantiate all dist processors
a__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
a__ = FlaxTopKLogitsWarper(3 )
a__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
a__ = FlaxMinLengthLogitsProcessor(min_length=1_0 , eos_token_id=SCREAMING_SNAKE_CASE )
a__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE )
a__ = FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE )
a__ = 1_0
# no processor list
def run_no_processor_list(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
a__ = temp_dist_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = top_k_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = top_p_warp(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = min_dist_proc(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = bos_dist_proc(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
a__ = eos_dist_proc(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
return scores
# with processor list
def run_processor_list(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
a__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
a__ = processor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cur_len=SCREAMING_SNAKE_CASE )
return scores
a__ = jax.jit(SCREAMING_SNAKE_CASE )
a__ = jax.jit(SCREAMING_SNAKE_CASE )
a__ = jitted_run_no_processor_list(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = jitted_run_processor_list(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# scores should be equal
self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 148 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class __lowerCamelCase (__UpperCamelCase ):
_lowercase = ["image_processor"]
_lowercase = "SamImageProcessor"
def __init__( self: Any,A_: Any ):
'''simple docstring'''
super().__init__(snake_case__ )
__UpperCamelCase = self.image_processor
__UpperCamelCase = -10
__UpperCamelCase = self.image_processor.size['longest_edge']
def __call__( self: List[str],A_: Tuple=None,A_: List[str]=None,A_: int=None,A_: Tuple=None,A_: Optional[Union[str, TensorType]] = None,**A_: Optional[Any],):
'''simple docstring'''
__UpperCamelCase = self.image_processor(
snake_case__,return_tensors=snake_case__,**snake_case__,)
# pop arguments that are not used in the foward but used nevertheless
__UpperCamelCase = encoding_image_processor['original_sizes']
if hasattr(snake_case__,'numpy' ): # Checks if Torch or TF tensor
__UpperCamelCase = original_sizes.numpy()
__UpperCamelCase, __UpperCamelCase, __UpperCamelCase = self._check_and_preprocess_points(
input_points=snake_case__,input_labels=snake_case__,input_boxes=snake_case__,)
__UpperCamelCase = self._normalize_and_convert(
snake_case__,snake_case__,input_points=snake_case__,input_labels=snake_case__,input_boxes=snake_case__,return_tensors=snake_case__,)
return encoding_image_processor
def snake_case_ ( self: List[Any],A_: Tuple,A_: Optional[Any],A_: Any=None,A_: Tuple=None,A_: Any=None,A_: Any="pt",):
'''simple docstring'''
if input_points is not None:
if len(snake_case__ ) != len(snake_case__ ):
__UpperCamelCase = [
self._normalize_coordinates(self.target_size,snake_case__,original_sizes[0] ) for point in input_points
]
else:
__UpperCamelCase = [
self._normalize_coordinates(self.target_size,snake_case__,snake_case__ )
for point, original_size in zip(snake_case__,snake_case__ )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
__UpperCamelCase, __UpperCamelCase = self._pad_points_and_labels(snake_case__,snake_case__ )
__UpperCamelCase = np.array(snake_case__ )
if input_labels is not None:
__UpperCamelCase = np.array(snake_case__ )
if input_boxes is not None:
if len(snake_case__ ) != len(snake_case__ ):
__UpperCamelCase = [
self._normalize_coordinates(self.target_size,snake_case__,original_sizes[0],is_bounding_box=snake_case__ )
for box in input_boxes
]
else:
__UpperCamelCase = [
self._normalize_coordinates(self.target_size,snake_case__,snake_case__,is_bounding_box=snake_case__ )
for box, original_size in zip(snake_case__,snake_case__ )
]
__UpperCamelCase = np.array(snake_case__ )
if input_boxes is not None:
if return_tensors == "pt":
__UpperCamelCase = torch.from_numpy(snake_case__ )
# boxes batch size of 1 by default
__UpperCamelCase = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
__UpperCamelCase = tf.convert_to_tensor(snake_case__ )
# boxes batch size of 1 by default
__UpperCamelCase = tf.expand_dims(snake_case__,1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'input_boxes': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
__UpperCamelCase = torch.from_numpy(snake_case__ )
# point batch size of 1 by default
__UpperCamelCase = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
__UpperCamelCase = tf.convert_to_tensor(snake_case__ )
# point batch size of 1 by default
__UpperCamelCase = tf.expand_dims(snake_case__,1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'input_points': input_points} )
if input_labels is not None:
if return_tensors == "pt":
__UpperCamelCase = torch.from_numpy(snake_case__ )
# point batch size of 1 by default
__UpperCamelCase = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
__UpperCamelCase = tf.convert_to_tensor(snake_case__ )
# point batch size of 1 by default
__UpperCamelCase = tf.expand_dims(snake_case__,1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'input_labels': input_labels} )
return encoding_image_processor
def snake_case_ ( self: str,A_: Any,A_: str ):
'''simple docstring'''
__UpperCamelCase = max([point.shape[0] for point in input_points] )
__UpperCamelCase = []
for i, point in enumerate(snake_case__ ):
if point.shape[0] != expected_nb_points:
__UpperCamelCase = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value],axis=0 )
__UpperCamelCase = np.append(input_labels[i],[self.point_pad_value] )
processed_input_points.append(snake_case__ )
__UpperCamelCase = processed_input_points
return input_points, input_labels
def snake_case_ ( self: Tuple,A_: int,A_: np.ndarray,A_: Dict,A_: Any=False ):
'''simple docstring'''
__UpperCamelCase, __UpperCamelCase = original_size
__UpperCamelCase, __UpperCamelCase = self.image_processor._get_preprocess_shape(snake_case__,longest_edge=snake_case__ )
__UpperCamelCase = deepcopy(snake_case__ ).astype(snake_case__ )
if is_bounding_box:
__UpperCamelCase = coords.reshape(-1,2,2 )
__UpperCamelCase = coords[..., 0] * (new_w / old_w)
__UpperCamelCase = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
__UpperCamelCase = coords.reshape(-1,4 )
return coords
def snake_case_ ( self: str,A_: int=None,A_: Union[str, Any]=None,A_: Union[str, Any]=None,):
'''simple docstring'''
if input_points is not None:
if hasattr(snake_case__,'numpy' ): # Checks for TF or Torch tensor
__UpperCamelCase = input_points.numpy().tolist()
if not isinstance(snake_case__,snake_case__ ) or not isinstance(input_points[0],snake_case__ ):
raise ValueError('Input points must be a list of list of floating points.' )
__UpperCamelCase = [np.array(snake_case__ ) for input_point in input_points]
else:
__UpperCamelCase = None
if input_labels is not None:
if hasattr(snake_case__,'numpy' ):
__UpperCamelCase = input_labels.numpy().tolist()
if not isinstance(snake_case__,snake_case__ ) or not isinstance(input_labels[0],snake_case__ ):
raise ValueError('Input labels must be a list of list integers.' )
__UpperCamelCase = [np.array(snake_case__ ) for label in input_labels]
else:
__UpperCamelCase = None
if input_boxes is not None:
if hasattr(snake_case__,'numpy' ):
__UpperCamelCase = input_boxes.numpy().tolist()
if (
not isinstance(snake_case__,snake_case__ )
or not isinstance(input_boxes[0],snake_case__ )
or not isinstance(input_boxes[0][0],snake_case__ )
):
raise ValueError('Input boxes must be a list of list of list of floating points.' )
__UpperCamelCase = [np.array(snake_case__ ).astype(np.floataa ) for box in input_boxes]
else:
__UpperCamelCase = None
return input_points, input_labels, input_boxes
@property
def snake_case_ ( self: Optional[int] ):
'''simple docstring'''
__UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(snake_case__ ) )
def snake_case_ ( self: List[str],*A_: str,**A_: List[Any] ):
'''simple docstring'''
return self.image_processor.post_process_masks(*snake_case__,**snake_case__ )
| 1 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return abs(lowerCamelCase__ ) if a == 0 else greatest_common_divisor(b % a , lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
while y: # --> when y=0 then loop will terminate and return x as final GCD.
lowerCAmelCase__ , lowerCAmelCase__ = y, x % y
return abs(lowerCamelCase__ )
def _UpperCAmelCase ( ):
"""simple docstring"""
try:
lowerCAmelCase__ = input("""Enter two integers separated by comma (,): """ ).split(""",""" )
lowerCAmelCase__ = int(nums[0] )
lowerCAmelCase__ = int(nums[1] )
print(
f"""greatest_common_divisor({num_a}, {num_a}) = """
f"""{greatest_common_divisor(lowerCamelCase__ , lowerCamelCase__ )}""" )
print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase__ , lowerCamelCase__ )}""" )
except (IndexError, UnboundLocalError, ValueError):
print("""Wrong input""" )
if __name__ == "__main__":
main()
| 644 | 0 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : float ) -> float:
"""simple docstring"""
if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def __snake_case ( SCREAMING_SNAKE_CASE_ : float ) -> float:
"""simple docstring"""
if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError('''Length must be a positive.''' )
return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 711 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase =StableDiffusionInpaintPipeline
_lowerCamelCase =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
_lowerCamelCase =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_lowerCamelCase =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_lowerCamelCase =frozenset([] )
def __snake_case ( self : List[str] ):
torch.manual_seed(0 )
UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=a__ , )
UpperCAmelCase = PNDMScheduler(skip_prk_steps=a__ )
torch.manual_seed(0 )
UpperCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=512 , )
UpperCAmelCase = CLIPTextModel(a__ )
UpperCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def __snake_case ( self : Dict , a__ : List[Any] , a__ : Tuple=0 ):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(a__ ) ).to(a__ )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase = Image.fromarray(np.uinta(a__ ) ).convert('''RGB''' ).resize((64, 64) )
UpperCAmelCase = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
if str(a__ ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(a__ )
else:
UpperCAmelCase = torch.Generator(device=a__ ).manual_seed(a__ )
UpperCAmelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': init_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def __snake_case ( self : int ):
UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = StableDiffusionInpaintPipeline(**a__ )
UpperCAmelCase = sd_pipe.to(a__ )
sd_pipe.set_progress_bar_config(disable=a__ )
UpperCAmelCase = self.get_dummy_inputs(a__ )
UpperCAmelCase = sd_pipe(**a__ ).images
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase = np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __snake_case ( self : List[Any] ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __snake_case ( self : Optional[int] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case ( self : Any ):
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench.npy''' )
UpperCAmelCase = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase = StableDiffusionInpaintPipeline.from_pretrained(a__ , safety_checker=a__ )
pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
pipe.enable_attention_slicing()
UpperCAmelCase = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(
prompt=a__ , image=a__ , mask_image=a__ , generator=a__ , output_type='''np''' , )
UpperCAmelCase = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def __snake_case ( self : int ):
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )
UpperCAmelCase = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase = StableDiffusionInpaintPipeline.from_pretrained(
a__ , torch_dtype=torch.floataa , safety_checker=a__ , )
pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
pipe.enable_attention_slicing()
UpperCAmelCase = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(
prompt=a__ , image=a__ , mask_image=a__ , generator=a__ , output_type='''np''' , )
UpperCAmelCase = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def __snake_case ( self : List[str] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
UpperCAmelCase = '''stabilityai/stable-diffusion-2-inpainting'''
UpperCAmelCase = PNDMScheduler.from_pretrained(a__ , subfolder='''scheduler''' )
UpperCAmelCase = StableDiffusionInpaintPipeline.from_pretrained(
a__ , safety_checker=a__ , scheduler=a__ , torch_dtype=torch.floataa , )
pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
UpperCAmelCase = '''Face of a yellow cat, high resolution, sitting on a park bench'''
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pipe(
prompt=a__ , image=a__ , mask_image=a__ , generator=a__ , num_inference_steps=2 , output_type='''np''' , )
UpperCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 570 | 0 |
from __future__ import annotations
from collections.abc import Callable
__A : int = list[list[float | int]]
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Matrix:
"""simple docstring"""
_A = len(_SCREAMING_SNAKE_CASE )
_A = [[0 for _ in range(size + 1 )] for _ in range(_SCREAMING_SNAKE_CASE )]
_A = 42
_A = 42
_A = 42
_A = 42
_A = 42
_A = 42
for row in range(_SCREAMING_SNAKE_CASE ):
for col in range(_SCREAMING_SNAKE_CASE ):
_A = matrix[row][col]
_A = vector[row][0]
_A = 0
_A = 0
while row < size and col < size:
# pivoting
_A = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_A, _A = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _SCREAMING_SNAKE_CASE ):
_A = augmented[rowa][col] / augmented[row][col]
_A = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _SCREAMING_SNAKE_CASE ):
for row in range(_SCREAMING_SNAKE_CASE ):
_A = augmented[row][col] / augmented[col][col]
for cola in range(_SCREAMING_SNAKE_CASE , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(_SCREAMING_SNAKE_CASE )
]
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> Callable[[int], int]:
"""simple docstring"""
_A = len(_SCREAMING_SNAKE_CASE )
_A = [[0 for _ in range(_SCREAMING_SNAKE_CASE )] for _ in range(_SCREAMING_SNAKE_CASE )]
_A = [[0] for _ in range(_SCREAMING_SNAKE_CASE )]
_A = 42
_A = 42
_A = 42
_A = 42
for x_val, y_val in enumerate(_SCREAMING_SNAKE_CASE ):
for col in range(_SCREAMING_SNAKE_CASE ):
_A = (x_val + 1) ** (size - col - 1)
_A = y_val
_A = solve(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def interpolated_func(_SCREAMING_SNAKE_CASE ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_SCREAMING_SNAKE_CASE ) )
return interpolated_func
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE = question_function , _SCREAMING_SNAKE_CASE = 10 ) -> int:
"""simple docstring"""
_A = [func(_SCREAMING_SNAKE_CASE ) for x_val in range(1 , order + 1 )]
_A = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_A = 0
_A = 42
_A = 42
for poly in polynomials:
_A = 1
while func(_SCREAMING_SNAKE_CASE ) == poly(_SCREAMING_SNAKE_CASE ):
x_val += 1
ret += poly(_SCREAMING_SNAKE_CASE )
return ret
if __name__ == "__main__":
print(f"{solution() = }")
| 27 |
__A : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_A = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
_A = Stack()
_A = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_SCREAMING_SNAKE_CASE ) )
elif i in operators:
# RULE 2
operator_stack.push(_SCREAMING_SNAKE_CASE )
elif i == ")":
# RULE 4
_A = operator_stack.peek()
operator_stack.pop()
_A = operand_stack.peek()
operand_stack.pop()
_A = operand_stack.peek()
operand_stack.pop()
_A = operators[opr](_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
operand_stack.push(_SCREAMING_SNAKE_CASE )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__A : Any = "(5 + ((4 * 2) * (2 + 3)))"
# answer = 45
print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| 27 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A_ :
'''simple docstring'''
def __init__( self , _A , _A=3 , _A=32 , _A=3 , _A=10 , _A=[10, 20, 30, 40] , _A=[1, 1, 2, 1] , _A=True , _A=True , _A="relu" , _A=3 , _A=None , ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = parent
_UpperCAmelCase : List[str] = batch_size
_UpperCAmelCase : Tuple = image_size
_UpperCAmelCase : List[str] = num_channels
_UpperCAmelCase : List[str] = embeddings_size
_UpperCAmelCase : str = hidden_sizes
_UpperCAmelCase : Union[str, Any] = depths
_UpperCAmelCase : List[Any] = is_training
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Dict = hidden_act
_UpperCAmelCase : Dict = num_labels
_UpperCAmelCase : Dict = scope
_UpperCAmelCase : Tuple = len(_A)
def snake_case__ ( self) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCAmelCase : int = None
if self.use_labels:
_UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.num_labels)
_UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def snake_case__ ( self) -> Optional[int]:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def snake_case__ ( self , _A , _A , _A) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = TFRegNetModel(config=_A)
_UpperCAmelCase : str = model(_A , training=_A)
# 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 snake_case__ ( self , _A , _A , _A) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = self.num_labels
_UpperCAmelCase : List[Any] = TFRegNetForImageClassification(_A)
_UpperCAmelCase : Any = model(_A , labels=_A , training=_A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def snake_case__ ( self) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = config_and_inputs
_UpperCAmelCase : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class A_ ( __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Dict = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE : Optional[int] = (
{"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE : int = False
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
_SCREAMING_SNAKE_CASE : List[str] = False
_SCREAMING_SNAKE_CASE : List[str] = False
_SCREAMING_SNAKE_CASE : List[Any] = False
def snake_case__ ( self) -> int:
"""simple docstring"""
_UpperCAmelCase : Tuple = TFRegNetModelTester(self)
_UpperCAmelCase : Tuple = ConfigTester(self , config_class=_A , has_text_modality=_A)
def snake_case__ ( self) -> Dict:
"""simple docstring"""
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''')
def snake_case__ ( self) -> List[str]:
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''')) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def snake_case__ ( self) -> Tuple:
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason='''RegNet does not support input and output embeddings''')
def snake_case__ ( self) -> Optional[int]:
"""simple docstring"""
pass
def snake_case__ ( self) -> Dict:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Any = model_class(_A)
_UpperCAmelCase : str = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : List[str] = [*signature.parameters.keys()]
_UpperCAmelCase : List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _A)
def snake_case__ ( self) -> str:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A)
def snake_case__ ( self) -> Optional[int]:
"""simple docstring"""
def check_hidden_states_output(_A , _A , _A):
_UpperCAmelCase : Optional[int] = model_class(_A)
_UpperCAmelCase : Any = model(**self._prepare_for_class(_A , _A) , training=_A)
_UpperCAmelCase : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCAmelCase : int = self.model_tester.num_stages
self.assertEqual(len(_A) , expected_num_stages + 1)
# RegNet'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 // 2, self.model_tester.image_size // 2] , )
_UpperCAmelCase , _UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : Optional[int] = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_UpperCAmelCase : Optional[Any] = layer_type
_UpperCAmelCase : int = True
check_hidden_states_output(_A , _A , _A)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase : Any = True
check_hidden_states_output(_A , _A , _A)
def snake_case__ ( self) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(_A , _A , _A , _A={}):
_UpperCAmelCase : Dict = model(_A , return_dict=_A , **_A)
_UpperCAmelCase : Optional[Any] = model(_A , return_dict=_A , **_A).to_tuple()
def recursive_check(_A , _A):
if isinstance(_A , (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(_A , _A):
recursive_check(_A , _A)
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(_A , _A)) , msg=(
'''Tuple and dict output are not equal. Difference:'''
f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}'''
) , )
recursive_check(_A , _A)
for model_class in self.all_model_classes:
_UpperCAmelCase : str = model_class(_A)
_UpperCAmelCase : Any = self._prepare_for_class(_A , _A)
_UpperCAmelCase : Tuple = self._prepare_for_class(_A , _A)
check_equivalence(_A , _A , _A)
_UpperCAmelCase : str = self._prepare_for_class(_A , _A , return_labels=_A)
_UpperCAmelCase : Tuple = self._prepare_for_class(_A , _A , return_labels=_A)
check_equivalence(_A , _A , _A)
_UpperCAmelCase : Union[str, Any] = self._prepare_for_class(_A , _A)
_UpperCAmelCase : Tuple = self._prepare_for_class(_A , _A)
check_equivalence(_A , _A , _A , {'''output_hidden_states''': True})
_UpperCAmelCase : Union[str, Any] = self._prepare_for_class(_A , _A , return_labels=_A)
_UpperCAmelCase : List[str] = self._prepare_for_class(_A , _A , return_labels=_A)
check_equivalence(_A , _A , _A , {'''output_hidden_states''': True})
def snake_case__ ( self) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A)
@slow
def snake_case__ ( self) -> Any:
"""simple docstring"""
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Dict = TFRegNetModel.from_pretrained(_A)
self.assertIsNotNone(_A)
def _lowerCamelCase ( ) -> Dict:
_UpperCAmelCase : Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class A_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self) -> Tuple:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def snake_case__ ( self) -> str:
"""simple docstring"""
_UpperCAmelCase : int = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
_UpperCAmelCase : Union[str, Any] = self.default_image_processor
_UpperCAmelCase : List[Any] = prepare_img()
_UpperCAmelCase : Optional[int] = image_processor(images=_A , return_tensors='''tf''')
# forward pass
_UpperCAmelCase : int = model(**_A , training=_A)
# verify the logits
_UpperCAmelCase : List[Any] = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape , _A)
_UpperCAmelCase : Dict = tf.constant([-0.4180, -1.5051, -3.4836])
tf.debugging.assert_near(outputs.logits[0, :3] , _A , atol=1e-4)
| 186 |
from __future__ import annotations
def _lowerCamelCase ( __A : int ) -> list[int]:
_UpperCAmelCase : List[str] = [True] * limit
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : Dict = False
_UpperCAmelCase : List[str] = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
_UpperCAmelCase : List[str] = i * 2
while index < limit:
_UpperCAmelCase : Union[str, Any] = False
_UpperCAmelCase : int = index + i
_UpperCAmelCase : Optional[int] = [2]
for i in range(3 , __A , 2 ):
if is_prime[i]:
primes.append(__A )
return primes
def _lowerCamelCase ( __A : int = 1_000_000 ) -> int:
_UpperCAmelCase : Any = prime_sieve(__A )
_UpperCAmelCase : Optional[Any] = 0
_UpperCAmelCase : Tuple = 0
for i in range(len(__A ) ):
for j in range(i + length , len(__A ) ):
_UpperCAmelCase : List[Any] = sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
_UpperCAmelCase : List[str] = j - i
_UpperCAmelCase : Optional[Any] = sol
return largest
if __name__ == "__main__":
print(F'{solution() = }')
| 186 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
a = None
a = logging.get_logger(__name__)
a = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
},
"tokenizer_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
},
}
# TODO(PVP) - this should be removed in Transformers v5
a = {
"t5-small": 512,
"t5-base": 512,
"t5-large": 512,
"t5-3b": 512,
"t5-11b": 512,
}
class _A ( __lowercase ):
__a = VOCAB_FILES_NAMES
__a = PRETRAINED_VOCAB_FILES_MAP
__a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a = ["""input_ids""", """attention_mask"""]
__a = TaTokenizer
__a = []
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE=100 , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
_UpperCAmelCase = [F"<extra_id_{i}>" for i in range(_SCREAMING_SNAKE_CASE )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
_UpperCAmelCase = len(set(filter(lambda _SCREAMING_SNAKE_CASE : bool("""extra_id_""" in str(_SCREAMING_SNAKE_CASE ) ) , _SCREAMING_SNAKE_CASE ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
""" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
""" tokens""" )
super().__init__(
_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , extra_ids=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
_UpperCAmelCase = extra_ids
@staticmethod
def UpperCAmelCase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
_UpperCAmelCase = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"""This tokenizer was incorrectly instantiated with a model max length of"""
F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
""" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
""" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
F" {pretrained_model_name_or_path} automatically truncating your input to"
F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
""" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
""" instantiate this tokenizer with `model_max_length` set to your preferred value.""" , _SCREAMING_SNAKE_CASE , )
return max_model_length
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_UpperCAmelCase = 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 ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
logger.info(F"Copy vocab file to {out_vocab_file}" )
return (out_vocab_file,)
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
_UpperCAmelCase = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
_UpperCAmelCase = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
_UpperCAmelCase = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCAmelCase ( self ):
return list(
set(filter(lambda _SCREAMING_SNAKE_CASE : bool(re.search(r"""<extra_id_\d+>""" , _SCREAMING_SNAKE_CASE ) ) is not None , self.additional_special_tokens ) ) )
def UpperCAmelCase ( self ):
return [self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE ) for token in self.get_sentinel_tokens()] | 518 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
a = False
try:
a = _is_package_available("google.colab")
except ModuleNotFoundError:
pass
@input.register
class _A :
def __init__( self , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = [] ):
_UpperCAmelCase = 0
_UpperCAmelCase = choices
_UpperCAmelCase = prompt
if sys.platform == "win32":
_UpperCAmelCase = """*"""
else:
_UpperCAmelCase = """➔ """
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "" ):
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , _SCREAMING_SNAKE_CASE )
else:
forceWrite(self.choices[index] , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
if index == self.position:
forceWrite(F" {self.arrow_char} " )
self.write_choice(_SCREAMING_SNAKE_CASE )
else:
forceWrite(F" {self.choices[index]}" )
reset_cursor()
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 1 ):
_UpperCAmelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_SCREAMING_SNAKE_CASE )
move_cursor(_SCREAMING_SNAKE_CASE , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def UpperCAmelCase ( self ):
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def UpperCAmelCase ( self ):
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def UpperCAmelCase ( self ):
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def UpperCAmelCase ( self ):
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_SCREAMING_SNAKE_CASE )] for number in range(10 )] )
def UpperCAmelCase ( self ):
_UpperCAmelCase = int(chr(self.current_selection ) )
_UpperCAmelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _SCREAMING_SNAKE_CASE )
else:
return
else:
return
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE = 0 ):
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
_UpperCAmelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(_SCREAMING_SNAKE_CASE )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
_UpperCAmelCase = int(builtins.input() )
except ValueError:
_UpperCAmelCase = default_choice
else:
_UpperCAmelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(_SCREAMING_SNAKE_CASE , """\n""" )
return choice | 518 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_A : str = (UniPCMultistepScheduler,)
_A : Any = (("""num_inference_steps""", 25),)
def lowerCamelCase(self , **lowerCAmelCase_ ):
A_ : Tuple = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
"""solver_type""": """bh2""",
}
config.update(**lowerCAmelCase_ )
return config
def lowerCamelCase(self , lowerCAmelCase_=0 , **lowerCAmelCase_ ):
A_ : Dict = dict(self.forward_default_kwargs )
A_ : int = kwargs.pop("""num_inference_steps""" , lowerCAmelCase_ )
A_ : Tuple = self.dummy_sample
A_ : Optional[Any] = 0.1 * sample
A_ : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
A_ : Any = self.get_scheduler_config(**lowerCAmelCase_ )
A_ : Optional[Any] = scheduler_class(**lowerCAmelCase_ )
scheduler.set_timesteps(lowerCAmelCase_ )
# copy over dummy past residuals
A_ : str = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase_ )
A_ : List[Any] = scheduler_class.from_pretrained(lowerCAmelCase_ )
new_scheduler.set_timesteps(lowerCAmelCase_ )
# copy over dummy past residuals
A_ : Union[str, Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
A_ , A_ : Union[str, Any] = sample, sample
for t in range(lowerCAmelCase_ , time_step + scheduler.config.solver_order + 1 ):
A_ : Any = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ).prev_sample
A_ : List[Any] = new_scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowerCamelCase(self , lowerCAmelCase_=0 , **lowerCAmelCase_ ):
A_ : Union[str, Any] = dict(self.forward_default_kwargs )
A_ : int = kwargs.pop("""num_inference_steps""" , lowerCAmelCase_ )
A_ : Any = self.dummy_sample
A_ : int = 0.1 * sample
A_ : Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
A_ : Tuple = self.get_scheduler_config()
A_ : List[str] = scheduler_class(**lowerCAmelCase_ )
scheduler.set_timesteps(lowerCAmelCase_ )
# copy over dummy past residuals (must be after setting timesteps)
A_ : Any = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase_ )
A_ : Any = scheduler_class.from_pretrained(lowerCAmelCase_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCAmelCase_ )
# copy over dummy past residual (must be after setting timesteps)
A_ : Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
A_ : Tuple = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ).prev_sample
A_ : List[Any] = new_scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowerCamelCase(self , lowerCAmelCase_=None , **lowerCAmelCase_ ):
if scheduler is None:
A_ : Any = self.scheduler_classes[0]
A_ : Dict = self.get_scheduler_config(**lowerCAmelCase_ )
A_ : List[Any] = scheduler_class(**lowerCAmelCase_ )
A_ : Any = self.scheduler_classes[0]
A_ : Any = self.get_scheduler_config(**lowerCAmelCase_ )
A_ : Any = scheduler_class(**lowerCAmelCase_ )
A_ : int = 10
A_ : Tuple = self.dummy_model()
A_ : Tuple = self.dummy_sample_deter
scheduler.set_timesteps(lowerCAmelCase_ )
for i, t in enumerate(scheduler.timesteps ):
A_ : List[str] = model(lowerCAmelCase_ , lowerCAmelCase_ )
A_ : Optional[int] = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ).prev_sample
return sample
def lowerCamelCase(self ):
A_ : Any = dict(self.forward_default_kwargs )
A_ : Dict = kwargs.pop("""num_inference_steps""" , lowerCAmelCase_ )
for scheduler_class in self.scheduler_classes:
A_ : int = self.get_scheduler_config()
A_ : Dict = scheduler_class(**lowerCAmelCase_ )
A_ : Dict = self.dummy_sample
A_ : Dict = 0.1 * sample
if num_inference_steps is not None and hasattr(lowerCAmelCase_ , """set_timesteps""" ):
scheduler.set_timesteps(lowerCAmelCase_ )
elif num_inference_steps is not None and not hasattr(lowerCAmelCase_ , """set_timesteps""" ):
A_ : Tuple = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
A_ : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
A_ : int = dummy_past_residuals[: scheduler.config.solver_order]
A_ : str = scheduler.timesteps[5]
A_ : List[Any] = scheduler.timesteps[6]
A_ : List[Any] = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ).prev_sample
A_ : Dict = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCamelCase(self ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
A_ : Optional[Any] = UniPCMultistepScheduler(**self.get_scheduler_config() )
A_ : List[str] = self.full_loop(scheduler=lowerCAmelCase_ )
A_ : Tuple = torch.mean(torch.abs(lowerCAmelCase_ ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
A_ : Dict = DPMSolverSinglestepScheduler.from_config(scheduler.config )
A_ : Dict = DEISMultistepScheduler.from_config(scheduler.config )
A_ : List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config )
A_ : Dict = UniPCMultistepScheduler.from_config(scheduler.config )
A_ : Union[str, Any] = self.full_loop(scheduler=lowerCAmelCase_ )
A_ : Optional[int] = torch.mean(torch.abs(lowerCAmelCase_ ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def lowerCamelCase(self ):
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase_ )
def lowerCamelCase(self ):
self.check_over_configs(thresholding=lowerCAmelCase_ )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowerCAmelCase_ , prediction_type=lowerCAmelCase_ , sample_max_value=lowerCAmelCase_ , solver_order=lowerCAmelCase_ , solver_type=lowerCAmelCase_ , )
def lowerCamelCase(self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase_ )
def lowerCamelCase(self ):
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowerCAmelCase_ , solver_type=lowerCAmelCase_ , prediction_type=lowerCAmelCase_ , )
A_ : Optional[Any] = self.full_loop(
solver_order=lowerCAmelCase_ , solver_type=lowerCAmelCase_ , prediction_type=lowerCAmelCase_ , )
assert not torch.isnan(lowerCAmelCase_ ).any(), "Samples have nan numbers"
def lowerCamelCase(self ):
self.check_over_configs(lower_order_final=lowerCAmelCase_ )
self.check_over_configs(lower_order_final=lowerCAmelCase_ )
def lowerCamelCase(self ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=lowerCAmelCase_ , time_step=0 )
def lowerCamelCase(self ):
A_ : str = self.full_loop()
A_ : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase_ ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def lowerCamelCase(self ):
A_ : Union[str, Any] = self.full_loop(prediction_type="""v_prediction""" )
A_ : Tuple = torch.mean(torch.abs(lowerCAmelCase_ ) )
assert abs(result_mean.item() - 0.1014 ) < 1e-3
def lowerCamelCase(self ):
A_ : Dict = self.scheduler_classes[0]
A_ : Tuple = self.get_scheduler_config(thresholding=lowerCAmelCase_ , dynamic_thresholding_ratio=0 )
A_ : List[str] = scheduler_class(**lowerCAmelCase_ )
A_ : Any = 10
A_ : str = self.dummy_model()
A_ : Any = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowerCAmelCase_ )
for i, t in enumerate(scheduler.timesteps ):
A_ : Any = model(lowerCAmelCase_ , lowerCAmelCase_ )
A_ : Union[str, Any] = scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ).prev_sample
assert sample.dtype == torch.floataa
def lowerCamelCase(self , **lowerCAmelCase_ ):
for scheduler_class in self.scheduler_classes:
A_ : List[Any] = self.get_scheduler_config(**lowerCAmelCase_ )
A_ : Union[str, Any] = scheduler_class(**lowerCAmelCase_ )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 480 |
"""simple docstring"""
_lowerCAmelCase = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowerCAmelCase = ["a", "b", "c", "d", "e"]
def __UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ ):
A_ : int = start
# add current to visited
visited.append(snake_case__ )
A_ : Any = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
A_ : Optional[Any] = topological_sort(snake_case__ , snake_case__ , snake_case__ )
# if all neighbors visited add current to sort
sort.append(snake_case__ )
# if all vertices haven't been visited select a new one to visit
if len(snake_case__ ) != len(snake_case__ ):
for vertice in vertices:
if vertice not in visited:
A_ : Optional[Any] = topological_sort(snake_case__ , snake_case__ , snake_case__ )
# return sort
return sort
if __name__ == "__main__":
_lowerCAmelCase = topological_sort("a", [], [])
print(sort)
| 480 | 1 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class lowercase_ (unittest.TestCase ):
def __init__( self , lowercase_ , lowercase_=100 , lowercase_=13 , lowercase_=30 , lowercase_=2 , lowercase_=3 , lowercase_=True , lowercase_=True , lowercase_=32 , lowercase_=5 , lowercase_=4 , lowercase_=37 , lowercase_="gelu" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=10 , lowercase_=0.02 , lowercase_=3 , ) -> Dict:
a__ =parent
a__ =vocab_size
a__ =batch_size
a__ =image_size
a__ =patch_size
a__ =num_channels
a__ =is_training
a__ =use_labels
a__ =hidden_size
a__ =num_hidden_layers
a__ =num_attention_heads
a__ =intermediate_size
a__ =hidden_act
a__ =hidden_dropout_prob
a__ =attention_probs_dropout_prob
a__ =type_sequence_label_size
a__ =initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
a__ =(image_size // patch_size) ** 2
a__ =num_patches + 1
def __UpperCamelCase ( self) -> Dict:
a__ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a__ =None
if self.use_labels:
a__ =ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__ =BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase_ , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def __UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_) -> List[Any]:
a__ =FlaxBeitModel(config=lowercase_)
a__ =model(lowercase_)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def __UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_) -> Any:
a__ =FlaxBeitForMaskedImageModeling(config=lowercase_)
a__ =model(lowercase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size))
def __UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_) -> List[str]:
a__ =self.type_sequence_label_size
a__ =FlaxBeitForImageClassification(config=lowercase_)
a__ =model(lowercase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
a__ =1
a__ =FlaxBeitForImageClassification(lowercase_)
a__ =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
a__ =model(lowercase_)
def __UpperCamelCase ( self) -> Dict:
a__ =self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) ,
) =config_and_inputs
a__ ={'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class lowercase_ (lowercase__ , unittest.TestCase ):
snake_case =(
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def __UpperCamelCase ( self) -> None:
a__ =FlaxBeitModelTester(self)
a__ =ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37)
def __UpperCamelCase ( self) -> Union[str, Any]:
self.config_tester.run_common_tests()
def __UpperCamelCase ( self) -> Dict:
a__ , a__ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ =model_class(lowercase_)
a__ =inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ =[*signature.parameters.keys()]
a__ =['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase_)
def __UpperCamelCase ( self) -> int:
a__ , a__ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
a__ =self._prepare_for_class(lowercase_ , lowercase_)
a__ =model_class(lowercase_)
@jax.jit
def model_jitted(lowercase_ , **lowercase_):
return model(pixel_values=lowercase_ , **lowercase_)
with self.subTest('JIT Enabled'):
a__ =model_jitted(**lowercase_).to_tuple()
with self.subTest('JIT Disabled'):
with jax.disable_jit():
a__ =model_jitted(**lowercase_).to_tuple()
self.assertEqual(len(lowercase_) , len(lowercase_))
for jitted_output, output in zip(lowercase_ , lowercase_):
self.assertEqual(jitted_output.shape , output.shape)
def __UpperCamelCase ( self) -> int:
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_)
def __UpperCamelCase ( self) -> Optional[Any]:
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase_)
def __UpperCamelCase ( self) -> Any:
a__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_)
@slow
def __UpperCamelCase ( self) -> Union[str, Any]:
for model_class_name in self.all_model_classes:
a__ =model_class_name.from_pretrained('microsoft/beit-base-patch16-224')
a__ =model(np.ones((1, 3, 224, 224)))
self.assertIsNotNone(lowercase_)
def _lowercase( ):
a__ =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@require_flax
class lowercase_ (unittest.TestCase ):
@cached_property
def __UpperCamelCase ( self) -> List[Any]:
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224') if is_vision_available() else None
@slow
def __UpperCamelCase ( self) -> Dict:
a__ =FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k')
a__ =self.default_image_processor
a__ =prepare_img()
a__ =image_processor(images=lowercase_ , return_tensors='np').pixel_values
# prepare bool_masked_pos
a__ =np.ones((1, 196) , dtype=lowercase_)
# forward pass
a__ =model(pixel_values=lowercase_ , bool_masked_pos=lowercase_)
a__ =outputs.logits
# verify the logits
a__ =(1, 196, 8192)
self.assertEqual(logits.shape , lowercase_)
a__ =np.array(
[[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]])
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , lowercase_ , atol=1e-2))
@slow
def __UpperCamelCase ( self) -> List[Any]:
a__ =FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224')
a__ =self.default_image_processor
a__ =prepare_img()
a__ =image_processor(images=lowercase_ , return_tensors='np')
# forward pass
a__ =model(**lowercase_)
a__ =outputs.logits
# verify the logits
a__ =(1, 1000)
self.assertEqual(logits.shape , lowercase_)
a__ =np.array([-1.23_85, -1.09_87, -1.01_08])
self.assertTrue(np.allclose(logits[0, :3] , lowercase_ , atol=1e-4))
a__ =281
self.assertEqual(logits.argmax(-1).item() , lowercase_)
@slow
def __UpperCamelCase ( self) -> Union[str, Any]:
a__ =FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k')
a__ =self.default_image_processor
a__ =prepare_img()
a__ =image_processor(images=lowercase_ , return_tensors='np')
# forward pass
a__ =model(**lowercase_)
a__ =outputs.logits
# verify the logits
a__ =(1, 21841)
self.assertEqual(logits.shape , lowercase_)
a__ =np.array([1.68_81, -0.27_87, 0.59_01])
self.assertTrue(np.allclose(logits[0, :3] , lowercase_ , atol=1e-4))
a__ =2396
self.assertEqual(logits.argmax(-1).item() , lowercase_)
| 20 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE__ :
@staticmethod
def A__ ( *__lowerCamelCase : List[str] , **__lowerCamelCase : Optional[int] ):
"""simple docstring"""
pass
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = np.array(__lowerCAmelCase )
lowerCAmelCase__ = npimg.shape
return {"hash": hashimage(__lowerCAmelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE__ (unittest.TestCase ):
lowercase_ : Any = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
lowercase_ : str = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def A__ ( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : int ):
"""simple docstring"""
lowerCAmelCase__ = MaskGenerationPipeline(model=__lowerCamelCase , image_processor=__lowerCamelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def A__ ( self : Optional[int] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
pass
@require_tf
@unittest.skip('''Image segmentation not implemented in TF''' )
def A__ ( self : Dict ):
"""simple docstring"""
pass
@slow
@require_torch
def A__ ( self : Optional[int] ):
"""simple docstring"""
lowerCAmelCase__ = pipeline('''mask-generation''' , model='''facebook/sam-vit-huge''' )
lowerCAmelCase__ = image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg''' , points_per_batch=2_56 )
# Shortening by hashing
lowerCAmelCase__ = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(__lowerCamelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.021},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
{'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9967},
{'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.993},
{'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9909},
{'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9879},
{'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9834},
{'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9716},
{'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9612},
{'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9599},
{'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9552},
{'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9532},
{'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9516},
{'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9499},
{'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9483},
{'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9464},
{'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
{'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
{'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9408},
{'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9335},
{'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9326},
{'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9262},
{'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8999},
{'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8986},
{'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8984},
{'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8873},
{'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8871}
] , )
# fmt: on
@require_torch
@slow
def A__ ( self : Optional[int] ):
"""simple docstring"""
lowerCAmelCase__ = '''facebook/sam-vit-huge'''
lowerCAmelCase__ = pipeline('''mask-generation''' , model=__lowerCamelCase )
lowerCAmelCase__ = image_segmenter(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , pred_iou_thresh=1 , points_per_batch=2_56 )
# Shortening by hashing
lowerCAmelCase__ = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(__lowerCamelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(__lowerCamelCase , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0210},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
] , )
| 615 | 0 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class __snake_case :
def __init__( self, A = "cpu", A = "openai/clip-vit-large-patch14" ):
"""simple docstring"""
lowerCamelCase : Dict = device
lowerCamelCase : Any = CLIPTokenizerFast.from_pretrained(A )
lowerCamelCase : Union[str, Any] = [0.4814_5466, 0.457_8275, 0.4082_1073]
lowerCamelCase : int = [0.2686_2954, 0.2613_0258, 0.2757_7711]
lowerCamelCase : Optional[int] = torchvision.transforms.Normalize(self.image_mean, self.image_std )
lowerCamelCase : str = torchvision.transforms.Resize(224 )
lowerCamelCase : Union[str, Any] = torchvision.transforms.CenterCrop(224 )
def UpperCAmelCase_ ( self, A ):
"""simple docstring"""
lowerCamelCase : str = self.resize(A )
lowerCamelCase : List[str] = self.center_crop(A )
lowerCamelCase : List[str] = self.normalize(A )
return images
def __call__( self, A=None, A=None, **A ):
"""simple docstring"""
lowerCamelCase : List[str] = self.tokenizer(text=A, **A )
lowerCamelCase : List[str] = self.preprocess_img(A )
lowerCamelCase : Any = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class __snake_case ( nn.Module):
def __init__( self, A=10, A=0.01, A=None, A=None, A=None, A=None, A=None, A=None, A=False, A=True, A="image", A=True, A=False, A=False, A=False, ):
"""simple docstring"""
super().__init__()
lowerCamelCase : List[str] = None
lowerCamelCase : str = device if device else get_device()
if vqgan:
lowerCamelCase : Optional[Any] = vqgan
else:
lowerCamelCase : str = load_vqgan(self.device, conf_path=A, ckpt_path=A )
self.vqgan.eval()
if clip:
lowerCamelCase : Dict = clip
else:
lowerCamelCase : str = CLIPModel.from_pretrained('openai/clip-vit-base-patch32' )
self.clip.to(self.device )
lowerCamelCase : Union[str, Any] = ProcessorGradientFlow(device=self.device )
lowerCamelCase : Any = iterations
lowerCamelCase : List[str] = lr
lowerCamelCase : int = log
lowerCamelCase : Optional[int] = make_grid
lowerCamelCase : Optional[Any] = return_val
lowerCamelCase : str = quantize
lowerCamelCase : int = self.vqgan.decoder.z_shape
def UpperCAmelCase_ ( self, A=None, A=None, A=5, A=True ):
"""simple docstring"""
lowerCamelCase : List[Any] = []
if output_path is None:
lowerCamelCase : Optional[Any] = './animation.gif'
if input_path is None:
lowerCamelCase : Tuple = self.save_path
lowerCamelCase : Tuple = sorted(glob(input_path + '/*' ) )
if not len(A ):
raise ValueError(
'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
' function?)' )
if len(A ) == 1:
print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)' )
lowerCamelCase : int = total_duration / len(A )
lowerCamelCase : str = [frame_duration] * len(A )
if extend_frames:
lowerCamelCase : Optional[Any] = 1.5
lowerCamelCase : Union[str, Any] = 3
for file_name in paths:
if file_name.endswith('.png' ):
images.append(imageio.imread(A ) )
imageio.mimsave(A, A, duration=A )
print(F'''gif saved to {output_path}''' )
def UpperCAmelCase_ ( self, A=None, A=None ):
"""simple docstring"""
if not (path or img):
raise ValueError('Input either path or tensor' )
if img is not None:
raise NotImplementedError
lowerCamelCase : Tuple = preprocess(Image.open(A ), target_image_size=256 ).to(self.device )
lowerCamelCase : int = preprocess_vqgan(A )
lowerCamelCase : Optional[int] = self.vqgan.encode(A )
return z
def UpperCAmelCase_ ( self, A ):
"""simple docstring"""
lowerCamelCase : str = self.latent.detach().requires_grad_()
lowerCamelCase : int = base_latent + transform_vector
if self.quantize:
lowerCamelCase : int = self.vqgan.quantize(A )
else:
lowerCamelCase : int = trans_latent
return self.vqgan.decode(A )
def UpperCAmelCase_ ( self, A, A, A=None ):
"""simple docstring"""
lowerCamelCase : Any = self.clip_preprocessor(text=A, images=A, return_tensors='pt', padding=A )
lowerCamelCase : Dict = self.clip(**A )
lowerCamelCase : Optional[int] = clip_outputs.logits_per_image
if weights is not None:
lowerCamelCase : int = similarity_logits * weights
return similarity_logits.sum()
def UpperCAmelCase_ ( self, A, A, A ):
"""simple docstring"""
lowerCamelCase : Optional[int] = self._get_clip_similarity(pos_prompts['prompts'], A, weights=(1 / pos_prompts['weights']) )
if neg_prompts:
lowerCamelCase : List[Any] = self._get_clip_similarity(neg_prompts['prompts'], A, weights=neg_prompts['weights'] )
else:
lowerCamelCase : Optional[Any] = torch.tensor([1], device=self.device )
lowerCamelCase : int = -torch.log(A ) + torch.log(A )
return loss
def UpperCAmelCase_ ( self, A, A, A ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = torch.randn_like(self.latent, requires_grad=A, device=self.device )
lowerCamelCase : Tuple = torch.optim.Adam([vector], lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
lowerCamelCase : int = self._add_vector(A )
lowerCamelCase : List[Any] = loop_post_process(A )
lowerCamelCase : Any = self._get_CLIP_loss(A, A, A )
print('CLIP loss', A )
if self.log:
wandb.log({'CLIP Loss': clip_loss} )
clip_loss.backward(retain_graph=A )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def UpperCAmelCase_ ( self, A, A, A ):
"""simple docstring"""
wandb.init(reinit=A, project='face-editor' )
wandb.config.update({'Positive Prompts': positive_prompts} )
wandb.config.update({'Negative Prompts': negative_prompts} )
wandb.config.update({'lr': self.lr, 'iterations': self.iterations} )
if image_path:
lowerCamelCase : List[str] = Image.open(A )
lowerCamelCase : str = image.resize((256, 256) )
wandb.log('Original Image', wandb.Image(A ) )
def UpperCAmelCase_ ( self, A ):
"""simple docstring"""
if not prompts:
return []
lowerCamelCase : str = []
lowerCamelCase : Union[str, Any] = []
if isinstance(A, A ):
lowerCamelCase : Optional[Any] = [prompt.strip() for prompt in prompts.split('|' )]
for prompt in prompts:
if isinstance(A, (tuple, list) ):
lowerCamelCase : Optional[Any] = prompt[0]
lowerCamelCase : Any = float(prompt[1] )
elif ":" in prompt:
lowerCamelCase : str = prompt.split(':' )
lowerCamelCase : Any = float(A )
else:
lowerCamelCase : Optional[Any] = prompt
lowerCamelCase : List[Any] = 1.0
processed_prompts.append(A )
weights.append(A )
return {
"prompts": processed_prompts,
"weights": torch.tensor(A, device=self.device ),
}
def UpperCAmelCase_ ( self, A, A=None, A=None, A=True, A=False, A=True, A=True, A=None, ):
"""simple docstring"""
if image_path:
lowerCamelCase : Dict = self._get_latent(A )
else:
lowerCamelCase : List[str] = torch.randn(self.latent_dim, device=self.device )
if self.log:
self._init_logging(A, A, A )
assert pos_prompts, "You must provide at least one positive prompt."
lowerCamelCase : str = self.process_prompts(A )
lowerCamelCase : Optional[int] = self.process_prompts(A )
if save_final and save_path is None:
lowerCamelCase : Dict = os.path.join('./outputs/', '_'.join(pos_prompts['prompts'] ) )
if not os.path.exists(A ):
os.makedirs(A )
else:
lowerCamelCase : List[Any] = save_path + '_' + get_timestamp()
os.makedirs(A )
lowerCamelCase : List[Any] = save_path
lowerCamelCase : Tuple = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print('Original Image' )
show_pil(custom_to_pil(A ) )
lowerCamelCase : Optional[Any] = loop_post_process(A )
for iter, transformed_img in enumerate(self._optimize_CLIP(A, A, A ) ):
if show_intermediate:
show_pil(A )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path, F'''iter_{iter:03d}.png''' ) )
if self.log:
wandb.log({'Image': wandb.Image(A )} )
if show_final:
show_pil(A )
if save_final:
transformed_img.save(os.path.join(self.save_path, F'''iter_{iter:03d}_final.png''' ) )
| 713 |
'''simple docstring'''
from manim import *
class __snake_case ( a__):
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = Rectangle(height=0.5, width=0.5 )
lowerCamelCase : List[Any] = Rectangle(height=0.46, width=0.46 ).set_stroke(width=0 )
lowerCamelCase : List[str] = [mem.copy() for i in range(6 )]
lowerCamelCase : List[Any] = [mem.copy() for i in range(6 )]
lowerCamelCase : str = VGroup(*A ).arrange(A, buff=0 )
lowerCamelCase : Any = VGroup(*A ).arrange(A, buff=0 )
lowerCamelCase : Dict = VGroup(A, A ).arrange(A, buff=0 )
lowerCamelCase : str = Text('CPU', font_size=24 )
lowerCamelCase : int = Group(A, A ).arrange(A, buff=0.5, aligned_edge=A )
cpu.move_to([-2.5, -0.5, 0] )
self.add(A )
lowerCamelCase : Optional[int] = [mem.copy() for i in range(1 )]
lowerCamelCase : Union[str, Any] = VGroup(*A ).arrange(A, buff=0 )
lowerCamelCase : Optional[Any] = Text('GPU', font_size=24 )
lowerCamelCase : Tuple = Group(A, A ).arrange(A, buff=0.5, aligned_edge=A )
gpu.align_to(A, A )
gpu.set_x(gpu.get_x() - 1 )
self.add(A )
lowerCamelCase : Optional[int] = [mem.copy() for i in range(6 )]
lowerCamelCase : Optional[Any] = VGroup(*A ).arrange(A, buff=0 )
lowerCamelCase : Any = Text('Model', font_size=24 )
lowerCamelCase : Tuple = Group(A, A ).arrange(A, buff=0.5, aligned_edge=A )
model.move_to([3, -1.0, 0] )
self.play(
Create(A, run_time=1 ), Create(A, run_time=1 ), Create(A, run_time=1 ), )
lowerCamelCase : str = MarkupText(
F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''', font_size=24, )
lowerCamelCase : Any = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCamelCase : Tuple = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''', font_size=18, )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(A, run_time=2.5 ), Write(A ), Write(A ) )
self.add(A )
lowerCamelCase : str = []
lowerCamelCase : Optional[int] = []
lowerCamelCase : Optional[Any] = []
for i, rect in enumerate(A ):
lowerCamelCase : List[str] = Rectangle(height=0.46, width=0.46 ).set_stroke(width=0.0 ).set_fill(A, opacity=0.7 )
cpu_target.move_to(A )
cpu_target.generate_target()
lowerCamelCase : int = 0.46 / 4
lowerCamelCase : Optional[int] = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ), buff=0.02, direction=A )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target, direction=A, buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target, direction=A, buff=0.0 )
cpu_targs.append(A )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(A ) )
second_animations.append(MoveToTarget(A, run_time=1.5 ) )
self.play(*A )
self.play(*A )
self.wait()
| 449 | 0 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _UpperCAmelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Any] ) -> List[Any]:
try:
with open(_lowerCamelCase , """rb""" ) as flax_state_f:
_lowerCAmelCase : Dict = from_bytes(_lowerCamelCase , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(_lowerCamelCase ) as f:
if f.read().startswith("""version""" ):
raise OSError(
"""You seem to have cloned a repository without having git-lfs installed. Please"""
""" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
""" folder you cloned.""" )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(_lowerCamelCase , _lowerCamelCase )
def _UpperCAmelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : List[str] ) -> int:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
_lowerCAmelCase : int = flatten_dict(jax.tree_util.tree_map(lambda _lowerCamelCase : x.dtype == jnp.bfloataa , _lowerCamelCase ) ).values()
if any(_lowerCamelCase ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
_lowerCAmelCase : List[Any] = jax.tree_util.tree_map(
lambda _lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , _lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = """"""
_lowerCAmelCase : Dict = flatten_dict(_lowerCamelCase , sep=""".""" )
_lowerCAmelCase : int = pt_model.state_dict()
# keep track of unexpected & missing keys
_lowerCAmelCase : Dict = []
_lowerCAmelCase : int = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
_lowerCAmelCase : Tuple = flax_key_tuple.split(""".""" )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
_lowerCAmelCase : Dict = flax_key_tuple_array[:-1] + ["""weight"""]
_lowerCAmelCase : List[str] = jnp.transpose(_lowerCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
_lowerCAmelCase : Any = flax_key_tuple_array[:-1] + ["""weight"""]
_lowerCAmelCase : str = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
_lowerCAmelCase : List[Any] = flax_key_tuple_array[:-1] + ["""weight"""]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(_lowerCamelCase ):
_lowerCAmelCase : Dict = (
flax_key_tuple_string.replace("""_0""" , """.0""" )
.replace("""_1""" , """.1""" )
.replace("""_2""" , """.2""" )
.replace("""_3""" , """.3""" )
.replace("""_4""" , """.4""" )
.replace("""_5""" , """.5""" )
.replace("""_6""" , """.6""" )
.replace("""_7""" , """.7""" )
.replace("""_8""" , """.8""" )
.replace("""_9""" , """.9""" )
)
_lowerCAmelCase : Tuple = """.""".join(_lowerCamelCase )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
else:
# add weight to pytorch dict
_lowerCAmelCase : List[Any] = np.asarray(_lowerCamelCase ) if not isinstance(_lowerCamelCase , np.ndarray ) else flax_tensor
_lowerCAmelCase : Union[str, Any] = torch.from_numpy(_lowerCamelCase )
# remove from missing keys
missing_keys.remove(_lowerCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(_lowerCamelCase )
pt_model.load_state_dict(_lowerCamelCase )
# re-transform missing_keys to list
_lowerCAmelCase : Optional[int] = list(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
if len(_lowerCamelCase ) > 0:
logger.warning(
f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
""" use it for predictions and inference.""" )
return pt_model
| 384 |
'''simple docstring'''
from collections.abc import Sequence
def _UpperCAmelCase ( _lowerCamelCase : Sequence[float] , _lowerCamelCase : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCamelCase ) )
def _UpperCAmelCase ( _lowerCamelCase : Sequence[float] , _lowerCamelCase : float ) -> float:
_lowerCAmelCase : List[Any] = 0.0
for coeff in reversed(_lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = result * x + coeff
return result
if __name__ == "__main__":
UpperCamelCase_ = (0.0, 0.0, 5.0, 9.3, 7.0)
UpperCamelCase_ = 1_0.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 384 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''facebook/data2vec-vision-base-ft''': (
'''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
),
}
class snake_case_ (lowercase__ ):
"""simple docstring"""
_lowerCamelCase = """data2vec-vision"""
def __init__( self ,lowercase=768 ,lowercase=12 ,lowercase=12 ,lowercase=3072 ,lowercase="gelu" ,lowercase=0.0 ,lowercase=0.0 ,lowercase=0.02 ,lowercase=1E-12 ,lowercase=224 ,lowercase=16 ,lowercase=3 ,lowercase=False ,lowercase=False ,lowercase=False ,lowercase=False ,lowercase=0.1 ,lowercase=0.1 ,lowercase=True ,lowercase=[3, 5, 7, 11] ,lowercase=[1, 2, 3, 6] ,lowercase=True ,lowercase=0.4 ,lowercase=256 ,lowercase=1 ,lowercase=False ,lowercase=255 ,**lowercase ,):
"""simple docstring"""
super().__init__(**lowercase)
UpperCAmelCase_ : Dict = hidden_size
UpperCAmelCase_ : Any = num_hidden_layers
UpperCAmelCase_ : int = num_attention_heads
UpperCAmelCase_ : str = intermediate_size
UpperCAmelCase_ : Tuple = hidden_act
UpperCAmelCase_ : str = hidden_dropout_prob
UpperCAmelCase_ : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase_ : Any = initializer_range
UpperCAmelCase_ : List[str] = layer_norm_eps
UpperCAmelCase_ : int = image_size
UpperCAmelCase_ : Optional[Any] = patch_size
UpperCAmelCase_ : str = num_channels
UpperCAmelCase_ : int = use_mask_token
UpperCAmelCase_ : Any = use_absolute_position_embeddings
UpperCAmelCase_ : str = use_relative_position_bias
UpperCAmelCase_ : Union[str, Any] = use_shared_relative_position_bias
UpperCAmelCase_ : Optional[Any] = layer_scale_init_value
UpperCAmelCase_ : Optional[Any] = drop_path_rate
UpperCAmelCase_ : List[Any] = use_mean_pooling
# decode head attributes (semantic segmentation)
UpperCAmelCase_ : List[Any] = out_indices
UpperCAmelCase_ : Dict = pool_scales
# auxiliary head attributes (semantic segmentation)
UpperCAmelCase_ : List[Any] = use_auxiliary_head
UpperCAmelCase_ : str = auxiliary_loss_weight
UpperCAmelCase_ : Dict = auxiliary_channels
UpperCAmelCase_ : Union[str, Any] = auxiliary_num_convs
UpperCAmelCase_ : List[str] = auxiliary_concat_input
UpperCAmelCase_ : List[Any] = semantic_loss_ignore_index
class snake_case_ (lowercase__ ):
"""simple docstring"""
_lowerCamelCase = version.parse("""1.11""" )
@property
def A_ ( self):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def A_ ( self):
"""simple docstring"""
return 1E-4
| 714 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class snake_case_ (lowercase__ ):
"""simple docstring"""
_lowerCamelCase = """umt5"""
_lowerCamelCase = ["""past_key_values"""]
def __init__( self ,lowercase=250112 ,lowercase=512 ,lowercase=64 ,lowercase=1024 ,lowercase=8 ,lowercase=None ,lowercase=6 ,lowercase=32 ,lowercase=128 ,lowercase=0.1 ,lowercase=1E-6 ,lowercase=1.0 ,lowercase="gated-gelu" ,lowercase=True ,lowercase=True ,lowercase="T5Tokenizer" ,lowercase=True ,lowercase=0 ,lowercase=1 ,lowercase=0 ,**lowercase ,):
"""simple docstring"""
super().__init__(
is_encoder_decoder=lowercase ,tokenizer_class=lowercase ,tie_word_embeddings=lowercase ,pad_token_id=lowercase ,eos_token_id=lowercase ,decoder_start_token_id=lowercase ,**lowercase ,)
UpperCAmelCase_ : Optional[int] = vocab_size
UpperCAmelCase_ : Any = d_model
UpperCAmelCase_ : Any = d_kv
UpperCAmelCase_ : int = d_ff
UpperCAmelCase_ : Tuple = num_layers
UpperCAmelCase_ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
UpperCAmelCase_ : Optional[int] = num_heads
UpperCAmelCase_ : str = relative_attention_num_buckets
UpperCAmelCase_ : Any = relative_attention_max_distance
UpperCAmelCase_ : Optional[Any] = dropout_rate
UpperCAmelCase_ : Union[str, Any] = layer_norm_epsilon
UpperCAmelCase_ : Optional[Any] = initializer_factor
UpperCAmelCase_ : int = feed_forward_proj
UpperCAmelCase_ : str = use_cache
UpperCAmelCase_ : List[str] = self.feed_forward_proj.split("-")
UpperCAmelCase_ : Any = act_info[-1]
UpperCAmelCase_ : Optional[int] = act_info[0] == "gated"
if len(lowercase) > 1 and act_info[0] != "gated" or len(lowercase) > 2:
raise ValueError(
F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"'gated-gelu' or 'relu'")
if feed_forward_proj == "gated-gelu":
UpperCAmelCase_ : Tuple = "gelu_new"
@property
def A_ ( self):
"""simple docstring"""
return self.d_model
@property
def A_ ( self):
"""simple docstring"""
return self.num_heads
@property
def A_ ( self):
"""simple docstring"""
return self.num_layers
class snake_case_ (lowercase__ ):
"""simple docstring"""
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : int = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
UpperCAmelCase_ : Union[str, Any] = "past_encoder_sequence + sequence"
UpperCAmelCase_ : Optional[int] = {0: "batch"}
UpperCAmelCase_ : Union[str, Any] = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
UpperCAmelCase_ : Optional[int] = {0: "batch", 1: "decoder_sequence"}
UpperCAmelCase_ : Dict = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(lowercase ,direction="inputs")
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def A_ ( self):
"""simple docstring"""
return 13
@property
def A_ ( self):
"""simple docstring"""
return 5E-4
| 455 | 0 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=0.0, lowerCamelCase = None, lowerCamelCase = "geglu", lowerCamelCase = None, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = True, lowerCamelCase = "layer_norm", lowerCamelCase = False, ) -> Dict:
"""simple docstring"""
super().__init__()
_lowercase : Union[str, Any] = only_cross_attention
_lowercase : Union[str, Any] = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
_lowercase : List[str] = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.''')
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
_lowercase : Union[str, Any] = AdaLayerNorm(lowerCamelCase, lowerCamelCase)
elif self.use_ada_layer_norm_zero:
_lowercase : List[str] = AdaLayerNormZero(lowerCamelCase, lowerCamelCase)
else:
_lowercase : Tuple = nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase)
_lowercase : Optional[int] = Attention(
query_dim=lowerCamelCase, heads=lowerCamelCase, dim_head=lowerCamelCase, dropout=lowerCamelCase, bias=lowerCamelCase, cross_attention_dim=cross_attention_dim if only_cross_attention else None, upcast_attention=lowerCamelCase, )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
_lowercase : Dict = (
AdaLayerNorm(lowerCamelCase, lowerCamelCase)
if self.use_ada_layer_norm
else nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase)
)
_lowercase : str = Attention(
query_dim=lowerCamelCase, cross_attention_dim=cross_attention_dim if not double_self_attention else None, heads=lowerCamelCase, dim_head=lowerCamelCase, dropout=lowerCamelCase, bias=lowerCamelCase, upcast_attention=lowerCamelCase, ) # is self-attn if encoder_hidden_states is none
else:
_lowercase : Optional[int] = None
_lowercase : List[Any] = None
# 3. Feed-forward
_lowercase : Dict = nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase)
_lowercase : Dict = FeedForward(lowerCamelCase, dropout=lowerCamelCase, activation_fn=lowerCamelCase, final_dropout=lowerCamelCase)
# let chunk size default to None
_lowercase : Union[str, Any] = None
_lowercase : str = 0
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase) -> Tuple:
"""simple docstring"""
_lowercase : Any = chunk_size
_lowercase : Union[str, Any] = dim
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, ) -> List[Any]:
"""simple docstring"""
if self.use_ada_layer_norm:
_lowercase : List[str] = self.norma(lowerCamelCase, lowerCamelCase)
elif self.use_ada_layer_norm_zero:
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase : Any = self.norma(
lowerCamelCase, lowerCamelCase, lowerCamelCase, hidden_dtype=hidden_states.dtype)
else:
_lowercase : List[Any] = self.norma(lowerCamelCase)
_lowercase : List[Any] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
_lowercase : Any = self.attna(
lowerCamelCase, encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, attention_mask=lowerCamelCase, **lowerCamelCase, )
if self.use_ada_layer_norm_zero:
_lowercase : List[Any] = gate_msa.unsqueeze(1) * attn_output
_lowercase : List[Any] = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
_lowercase : List[Any] = (
self.norma(lowerCamelCase, lowerCamelCase) if self.use_ada_layer_norm else self.norma(lowerCamelCase)
)
_lowercase : Any = self.attna(
lowerCamelCase, encoder_hidden_states=lowerCamelCase, attention_mask=lowerCamelCase, **lowerCamelCase, )
_lowercase : Optional[int] = attn_output + hidden_states
# 3. Feed-forward
_lowercase : List[str] = self.norma(lowerCamelCase)
if self.use_ada_layer_norm_zero:
_lowercase : str = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.''')
_lowercase : Optional[Any] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
_lowercase : Optional[Any] = torch.cat(
[self.ff(lowerCamelCase) for hid_slice in norm_hidden_states.chunk(lowerCamelCase, dim=self._chunk_dim)], dim=self._chunk_dim, )
else:
_lowercase : Union[str, Any] = self.ff(lowerCamelCase)
if self.use_ada_layer_norm_zero:
_lowercase : List[str] = gate_mlp.unsqueeze(1) * ff_output
_lowercase : Tuple = ff_output + hidden_states
return hidden_states
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = 4, lowerCamelCase = 0.0, lowerCamelCase = "geglu", lowerCamelCase = False, ) -> Tuple:
"""simple docstring"""
super().__init__()
_lowercase : Optional[Any] = int(dim * mult)
_lowercase : Optional[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
_lowercase : Union[str, Any] = GELU(lowerCamelCase, lowerCamelCase)
if activation_fn == "gelu-approximate":
_lowercase : Optional[Any] = GELU(lowerCamelCase, lowerCamelCase, approximate='tanh')
elif activation_fn == "geglu":
_lowercase : str = GEGLU(lowerCamelCase, lowerCamelCase)
elif activation_fn == "geglu-approximate":
_lowercase : Union[str, Any] = ApproximateGELU(lowerCamelCase, lowerCamelCase)
_lowercase : List[Any] = nn.ModuleList([])
# project in
self.net.append(lowerCamelCase)
# project dropout
self.net.append(nn.Dropout(lowerCamelCase))
# project out
self.net.append(nn.Linear(lowerCamelCase, lowerCamelCase))
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(lowerCamelCase))
def UpperCamelCase ( self, lowerCamelCase) -> Optional[int]:
"""simple docstring"""
for module in self.net:
_lowercase : Union[str, Any] = module(lowerCamelCase)
return hidden_states
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = "none") -> Optional[int]:
"""simple docstring"""
super().__init__()
_lowercase : Union[str, Any] = nn.Linear(lowerCamelCase, lowerCamelCase)
_lowercase : List[Any] = approximate
def UpperCamelCase ( self, lowerCamelCase) -> Tuple:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(lowerCamelCase, approximate=self.approximate)
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa), approximate=self.approximate).to(dtype=gate.dtype)
def UpperCamelCase ( self, lowerCamelCase) -> Tuple:
"""simple docstring"""
_lowercase : Optional[int] = self.proj(lowerCamelCase)
_lowercase : Union[str, Any] = self.gelu(lowerCamelCase)
return hidden_states
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase) -> int:
"""simple docstring"""
super().__init__()
_lowercase : Optional[Any] = nn.Linear(lowerCamelCase, dim_out * 2)
def UpperCamelCase ( self, lowerCamelCase) -> Any:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(lowerCamelCase)
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa)).to(dtype=gate.dtype)
def UpperCamelCase ( self, lowerCamelCase) -> Optional[int]:
"""simple docstring"""
_lowercase , _lowercase : Tuple = self.proj(lowerCamelCase).chunk(2, dim=-1)
return hidden_states * self.gelu(lowerCamelCase)
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase) -> Any:
"""simple docstring"""
super().__init__()
_lowercase : str = nn.Linear(lowerCamelCase, lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Optional[int] = self.proj(lowerCamelCase)
return x * torch.sigmoid(1.7_0_2 * x)
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase) -> Tuple:
"""simple docstring"""
super().__init__()
_lowercase : int = nn.Embedding(lowerCamelCase, lowerCamelCase)
_lowercase : List[Any] = nn.SiLU()
_lowercase : Optional[Any] = nn.Linear(lowerCamelCase, embedding_dim * 2)
_lowercase : List[Any] = nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase) -> Optional[Any]:
"""simple docstring"""
_lowercase : List[str] = self.linear(self.silu(self.emb(lowerCamelCase)))
_lowercase , _lowercase : int = torch.chunk(lowerCamelCase, 2)
_lowercase : Optional[Any] = self.norm(lowerCamelCase) * (1 + scale) + shift
return x
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
_lowercase : List[Any] = CombinedTimestepLabelEmbeddings(lowerCamelCase, lowerCamelCase)
_lowercase : Tuple = nn.SiLU()
_lowercase : Any = nn.Linear(lowerCamelCase, 6 * embedding_dim, bias=lowerCamelCase)
_lowercase : List[str] = nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase, eps=1E-6)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None) -> Dict:
"""simple docstring"""
_lowercase : Optional[int] = self.linear(self.silu(self.emb(lowerCamelCase, lowerCamelCase, hidden_dtype=lowerCamelCase)))
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase : List[str] = emb.chunk(6, dim=1)
_lowercase : Dict = self.norm(lowerCamelCase) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class _lowerCamelCase( nn.Module ):
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = 1E-5) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
_lowercase : Optional[Any] = num_groups
_lowercase : Any = eps
if act_fn is None:
_lowercase : Optional[Any] = None
else:
_lowercase : Any = get_activation(lowerCamelCase)
_lowercase : int = nn.Linear(lowerCamelCase, out_dim * 2)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase) -> Optional[int]:
"""simple docstring"""
if self.act:
_lowercase : Optional[int] = self.act(lowerCamelCase)
_lowercase : Union[str, Any] = self.linear(lowerCamelCase)
_lowercase : Optional[Any] = emb[:, :, None, None]
_lowercase , _lowercase : Optional[Any] = emb.chunk(2, dim=1)
_lowercase : Any = F.group_norm(lowerCamelCase, self.num_groups, eps=self.eps)
_lowercase : Optional[Any] = x * (1 + scale) + shift
return x
| 89 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> Any:
_lowercase : str = 10
_lowercase : List[str] = datasets.Features(
{
'tokens': datasets.Sequence(datasets.Value('string' ) ),
'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive'] ) ),
'answers': datasets.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
'id': datasets.Value('int64' ),
} )
_lowercase : Union[str, Any] = datasets.Dataset.from_dict(
{
'tokens': [['foo'] * 5] * n,
'labels': [[1] * 5] * n,
'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
'id': list(range(lowerCamelCase_ ) ),
} , features=lowerCamelCase_ , )
return dataset
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> int:
_lowercase : int = str(tmp_path_factory.mktemp('data' ) / 'file.arrow' )
dataset.map(cache_file_name=lowerCamelCase_ )
return filename
# FILE_CONTENT + files
SCREAMING_SNAKE_CASE : str = "\\n Text data.\n Second line of data."
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> List[Any]:
_lowercase : str = tmp_path_factory.mktemp('data' ) / 'file.txt'
_lowercase : List[str] = FILE_CONTENT
with open(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ )
return filename
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Tuple:
import bza
_lowercase : Any = tmp_path_factory.mktemp('data' ) / 'file.txt.bz2'
_lowercase : Optional[Any] = bytes(lowerCamelCase_ , 'utf-8' )
with bza.open(lowerCamelCase_ , 'wb' ) as f:
f.write(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> List[Any]:
import gzip
_lowercase : Optional[int] = str(tmp_path_factory.mktemp('data' ) / 'file.txt.gz' )
_lowercase : Optional[int] = bytes(lowerCamelCase_ , 'utf-8' )
with gzip.open(lowerCamelCase_ , 'wb' ) as f:
f.write(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> str:
if datasets.config.LZ4_AVAILABLE:
import lza.frame
_lowercase : Any = tmp_path_factory.mktemp('data' ) / 'file.txt.lz4'
_lowercase : Optional[Any] = bytes(lowerCamelCase_ , 'utf-8' )
with lza.frame.open(lowerCamelCase_ , 'wb' ) as f:
f.write(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> str:
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
_lowercase : int = tmp_path_factory.mktemp('data' ) / 'file.txt.7z'
with pyazr.SevenZipFile(lowerCamelCase_ , 'w' ) as archive:
archive.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
import tarfile
_lowercase : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'file.txt.tar'
with tarfile.TarFile(lowerCamelCase_ , 'w' ) as f:
f.add(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> str:
import lzma
_lowercase : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'file.txt.xz'
_lowercase : int = bytes(lowerCamelCase_ , 'utf-8' )
with lzma.open(lowerCamelCase_ , 'wb' ) as f:
f.write(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> str:
import zipfile
_lowercase : Dict = tmp_path_factory.mktemp('data' ) / 'file.txt.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[Any]:
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
_lowercase : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'file.txt.zst'
_lowercase : Dict = bytes(lowerCamelCase_ , 'utf-8' )
with zstd.open(lowerCamelCase_ , 'wb' ) as f:
f.write(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> str:
_lowercase : Union[str, Any] = tmp_path_factory.mktemp('data' ) / 'file.xml'
_lowercase : Optional[Any] = textwrap.dedent(
'\\n <?xml version="1.0" encoding="UTF-8" ?>\n <tmx version="1.4">\n <header segtype="sentence" srclang="ca" />\n <body>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>' )
with open(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ )
return filename
SCREAMING_SNAKE_CASE : Dict = [
{"col_1": "0", "col_2": 0, "col_3": 0.0},
{"col_1": "1", "col_2": 1, "col_3": 1.0},
{"col_1": "2", "col_2": 2, "col_3": 2.0},
{"col_1": "3", "col_2": 3, "col_3": 3.0},
]
SCREAMING_SNAKE_CASE : Dict = [
{"col_1": "4", "col_2": 4, "col_3": 4.0},
{"col_1": "5", "col_2": 5, "col_3": 5.0},
]
SCREAMING_SNAKE_CASE : Optional[Any] = {
"col_1": ["0", "1", "2", "3"],
"col_2": [0, 1, 2, 3],
"col_3": [0.0, 1.0, 2.0, 3.0],
}
SCREAMING_SNAKE_CASE : Tuple = [
{"col_3": 0.0, "col_1": "0", "col_2": 0},
{"col_3": 1.0, "col_1": "1", "col_2": 1},
]
SCREAMING_SNAKE_CASE : Any = [
{"col_1": "s0", "col_2": 0, "col_3": 0.0},
{"col_1": "s1", "col_2": 1, "col_3": 1.0},
{"col_1": "s2", "col_2": 2, "col_3": 2.0},
{"col_1": "s3", "col_2": 3, "col_3": 3.0},
]
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> List[str]:
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Dict:
_lowercase : Optional[int] = datasets.Dataset.from_dict(lowerCamelCase_ )
_lowercase : List[Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset.arrow' )
dataset.map(cache_file_name=lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> str:
_lowercase : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset.sqlite' )
with contextlib.closing(sqlitea.connect(lowerCamelCase_ ) ) as con:
_lowercase : Union[str, Any] = con.cursor()
cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)' )
for item in DATA:
cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)' , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Dict:
_lowercase : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset.csv' )
with open(lowerCamelCase_ , 'w' , newline='' ) as f:
_lowercase : Tuple = csv.DictWriter(lowerCamelCase_ , fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> List[Any]:
_lowercase : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset2.csv' )
with open(lowerCamelCase_ , 'w' , newline='' ) as f:
_lowercase : str = csv.DictWriter(lowerCamelCase_ , fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Any:
import bza
_lowercase : int = tmp_path_factory.mktemp('data' ) / 'dataset.csv.bz2'
with open(lowerCamelCase_ , 'rb' ) as f:
_lowercase : int = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(lowerCamelCase_ , 'wb' ) as f:
f.write(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
_lowercase : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[Any]:
_lowercase : str = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.basename(csv_path.replace('.csv' , '.CSV' ) ) )
f.write(lowerCamelCase_ , arcname=os.path.basename(csva_path.replace('.csv' , '.CSV' ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
_lowercase : Union[str, Any] = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.csv.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(lowerCamelCase_ ) ) )
f.write(lowerCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(lowerCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : Optional[int] = str(tmp_path_factory.mktemp('data' ) / 'dataset.parquet' )
_lowercase : Optional[Any] = pa.schema(
{
'col_1': pa.string(),
'col_2': pa.intaa(),
'col_3': pa.floataa(),
} )
with open(lowerCamelCase_ , 'wb' ) as f:
_lowercase : List[str] = pq.ParquetWriter(lowerCamelCase_ , schema=lowerCamelCase_ )
_lowercase : Any = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowerCamelCase_ ) )] for k in DATA[0]} , schema=lowerCamelCase_ )
writer.write_table(lowerCamelCase_ )
writer.close()
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[Any]:
_lowercase : Optional[Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
_lowercase : List[Any] = {'data': DATA}
with open(lowerCamelCase_ , 'w' ) as f:
json.dump(lowerCamelCase_ , lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Any:
_lowercase : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
_lowercase : Optional[Any] = {'data': DATA_DICT_OF_LISTS}
with open(lowerCamelCase_ , 'w' ) as f:
json.dump(lowerCamelCase_ , lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Union[str, Any]:
_lowercase : Any = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl' )
with open(lowerCamelCase_ , 'w' ) as f:
for item in DATA:
f.write(json.dumps(lowerCamelCase_ ) + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Dict:
_lowercase : Dict = str(tmp_path_factory.mktemp('data' ) / 'dataset2.jsonl' )
with open(lowerCamelCase_ , 'w' ) as f:
for item in DATA:
f.write(json.dumps(lowerCamelCase_ ) + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> List[str]:
_lowercase : Optional[Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset_312.jsonl' )
with open(lowerCamelCase_ , 'w' ) as f:
for item in DATA_312:
f.write(json.dumps(lowerCamelCase_ ) + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> List[Any]:
_lowercase : str = str(tmp_path_factory.mktemp('data' ) / 'dataset-str.jsonl' )
with open(lowerCamelCase_ , 'w' ) as f:
for item in DATA_STR:
f.write(json.dumps(lowerCamelCase_ ) + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Optional[Any]:
import gzip
_lowercase : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt.gz' )
with open(lowerCamelCase_ , 'rb' ) as orig_file:
with gzip.open(lowerCamelCase_ , 'wb' ) as zipped_file:
zipped_file.writelines(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Dict:
import gzip
_lowercase : Optional[int] = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.gz' )
with open(lowerCamelCase_ , 'rb' ) as orig_file:
with gzip.open(lowerCamelCase_ , 'wb' ) as zipped_file:
zipped_file.writelines(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
_lowercase : Any = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> str:
_lowercase : str = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.join('nested' , os.path.basename(lowerCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
_lowercase : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.jsonl.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(lowerCamelCase_ ) ) )
f.write(lowerCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(lowerCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
_lowercase : Tuple = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.tar'
with tarfile.TarFile(lowerCamelCase_ , 'w' ) as f:
f.add(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
f.add(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Any:
_lowercase : str = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.tar'
with tarfile.TarFile(lowerCamelCase_ , 'w' ) as f:
f.add(lowerCamelCase_ , arcname=os.path.join('nested' , os.path.basename(lowerCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Any:
_lowercase : Optional[int] = ['0', '1', '2', '3']
_lowercase : str = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt' )
with open(lowerCamelCase_ , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Union[str, Any]:
_lowercase : str = ['0', '1', '2', '3']
_lowercase : List[Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.txt' )
with open(lowerCamelCase_ , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> List[str]:
_lowercase : List[Any] = ['0', '1', '2', '3']
_lowercase : Optional[int] = tmp_path_factory.mktemp('data' ) / 'dataset.abc'
with open(lowerCamelCase_ , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Any:
_lowercase : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.text.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Dict:
_lowercase : List[Any] = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.text.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(lowerCamelCase_ ) ) )
f.write(lowerCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(lowerCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> str:
_lowercase : Any = tmp_path_factory.mktemp('data' ) / 'dataset.ext.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.basename('unsupported.ext' ) )
f.write(lowerCamelCase_ , arcname=os.path.basename('unsupported_2.ext' ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : List[str] = '\n'.join(['First', 'Second\u2029with Unicode new line', 'Third'] )
_lowercase : Any = str(tmp_path_factory.mktemp('data' ) / 'dataset_with_unicode_new_lines.txt' )
with open(lowerCamelCase_ , 'w' , encoding='utf-8' ) as f:
f.write(lowerCamelCase_ )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> Dict:
return os.path.join('tests' , 'features' , 'data' , 'test_image_rgb.jpg' )
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> int:
return os.path.join('tests' , 'features' , 'data' , 'test_audio_44100.wav' )
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Any:
_lowercase : Dict = tmp_path_factory.mktemp('data' ) / 'dataset.img.zip'
with zipfile.ZipFile(lowerCamelCase_ , 'w' ) as f:
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ) )
f.write(lowerCamelCase_ , arcname=os.path.basename(lowerCamelCase_ ).replace('.jpg' , '2.jpg' ) )
return path
@pytest.fixture(scope='session' )
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[Any]:
_lowercase : str = tmp_path_factory.mktemp('data_dir' )
(data_dir / "subdir").mkdir()
with open(data_dir / 'subdir' / 'train.txt' , 'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / 'subdir' / 'test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
# hidden file
with open(data_dir / 'subdir' / '.test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '.subdir' / 'train.txt' , 'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / '.subdir' / 'test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
return data_dir
| 89 | 1 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ =CLIPConfig
lowerCamelCase__ =['CLIPEncoderLayer']
def __init__(self , a_ ):
'''simple docstring'''
super().__init__(a_ )
__snake_case : Dict = CLIPVisionModelWithProjection(config.vision_config )
__snake_case : str = nn.Linear(config.vision_config.projection_dim , 1 )
__snake_case : Tuple = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def SCREAMING_SNAKE_CASE (self , a_ , a_ , a_=0.5 , a_=0.5 ):
'''simple docstring'''
__snake_case : str = self.vision_model(a_ )[0]
__snake_case : Optional[int] = self.p_head(a_ )
__snake_case : Optional[Any] = nsfw_detected.flatten()
__snake_case : Optional[int] = nsfw_detected > p_threshold
__snake_case : Optional[int] = nsfw_detected.tolist()
if any(a_ ):
logger.warning(
'''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
''' Try again with a different prompt and/or seed.''' )
for idx, nsfw_detected_ in enumerate(a_ ):
if nsfw_detected_:
__snake_case : Tuple = np.zeros(images[idx].shape )
__snake_case : Any = self.w_head(a_ )
__snake_case : Tuple = watermark_detected.flatten()
__snake_case : str = watermark_detected > w_threshold
__snake_case : Dict = watermark_detected.tolist()
if any(a_ ):
logger.warning(
'''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
''' Try again with a different prompt and/or seed.''' )
for idx, watermark_detected_ in enumerate(a_ ):
if watermark_detected_:
__snake_case : List[Any] = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 229 |
"""simple docstring"""
def lowercase ( _snake_case : list[list] ) ->list[list]:
"""simple docstring"""
__snake_case : Dict = current_set.copy()
for row_index, row in enumerate(_snake_case ):
__snake_case : str = row[0]
for column_index, column in enumerate(_snake_case ):
if magnitude == 0:
__snake_case : str = column
continue
__snake_case : str = column / magnitude
# Subtract to cancel term
__snake_case : Union[str, Any] = current_set[0]
__snake_case : int = [first_row]
__snake_case : Tuple = current_set[1::]
for row in current_set:
__snake_case : Optional[int] = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(_snake_case )
continue
for column_index in range(len(_snake_case ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(_snake_case )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
__snake_case : Tuple = final_set[0]
__snake_case : Tuple = []
__snake_case : List[str] = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
__snake_case : str = simplify(_snake_case )
for i in range(len(_snake_case ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , _snake_case )
__snake_case : List[Any] = resultant
return final_set
def lowercase ( _snake_case : list[list] ) ->list:
"""simple docstring"""
if len(_snake_case ) == 0:
raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
__snake_case : Optional[int] = len(_snake_case ) + 1
if any(len(_snake_case ) != _length for item in equations ):
raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
for row in equations:
if any(not isinstance(_snake_case , (int, float) ) for column in row ):
raise ValueError('''solve_simultaneous() requires lists of integers''' )
if len(_snake_case ) == 1:
return [equations[0][-1] / equations[0][0]]
__snake_case : int = equations.copy()
if any(0 in row for row in data_set ):
__snake_case : List[Any] = data_set.copy()
__snake_case : int = []
for row_index, row in enumerate(_snake_case ):
if 0 not in row:
__snake_case : Tuple = data_set.pop(_snake_case )
break
if not full_row:
raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
data_set.insert(0 , _snake_case )
__snake_case : Tuple = data_set.copy()
__snake_case : Dict = simplify(_snake_case )
__snake_case : Optional[int] = simplified[::-1]
__snake_case : list = []
for row in simplified:
__snake_case : Union[str, Any] = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
__snake_case : Any = row.copy()[: len(_snake_case ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(_snake_case ) == 0:
solutions.append(0 )
continue
__snake_case : Tuple = temp_row[1::]
__snake_case : List[str] = temp_row[::-1]
for column_index, column in enumerate(_snake_case ):
current_solution -= column * solutions[column_index]
solutions.append(_snake_case )
__snake_case : Optional[Any] = []
for item in solutions:
final.append(float(round(_snake_case , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : Tuple = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 229 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : List[str] = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
__magic_name__ : List[Any] = """xlm-roberta-xl"""
def __init__( self : Any , UpperCamelCase__ : Optional[Any]=250880 , UpperCamelCase__ : Any=2560 , UpperCamelCase__ : str=36 , UpperCamelCase__ : List[Any]=32 , UpperCamelCase__ : Tuple=10240 , UpperCamelCase__ : Dict="gelu" , UpperCamelCase__ : List[str]=0.1 , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : int=514 , UpperCamelCase__ : Tuple=1 , UpperCamelCase__ : List[Any]=0.02 , UpperCamelCase__ : Union[str, Any]=1E-05 , UpperCamelCase__ : int=1 , UpperCamelCase__ : Dict=0 , UpperCamelCase__ : List[str]=2 , UpperCamelCase__ : List[Any]="absolute" , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : Dict=None , **UpperCamelCase__ : Optional[int] , ):
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
A__ : Optional[Any] =vocab_size
A__ : List[Any] =hidden_size
A__ : Any =num_hidden_layers
A__ : Dict =num_attention_heads
A__ : int =hidden_act
A__ : str =intermediate_size
A__ : str =hidden_dropout_prob
A__ : List[str] =attention_probs_dropout_prob
A__ : Any =max_position_embeddings
A__ : Tuple =type_vocab_size
A__ : Optional[Any] =initializer_range
A__ : List[str] =layer_norm_eps
A__ : Any =position_embedding_type
A__ : int =use_cache
A__ : Optional[int] =classifier_dropout
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
@property
def _UpperCAmelCase ( self : str ):
if self.task == "multiple-choice":
A__ : List[str] ={0: "batch", 1: "choice", 2: "sequence"}
else:
A__ : Optional[Any] ={0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : int = logging.get_logger(__name__)
def lowercase ( UpperCamelCase : Any ):
"""simple docstring"""
A__ : str =OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
A__ : Dict =key.replace("module.encoder" , "glpn.encoder" )
if key.startswith("module.decoder" ):
A__ : Optional[int] =key.replace("module.decoder" , "decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
A__ : Tuple =key[key.find("patch_embed" ) + len("patch_embed" )]
A__ : Optional[Any] =key.replace(F'''patch_embed{idx}''' , F'''patch_embeddings.{int(UpperCamelCase )-1}''' )
if "norm" in key:
A__ : Dict =key.replace("norm" , "layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
A__ : Any =key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
A__ : Tuple =key.replace(F'''layer_norm{idx}''' , F'''layer_norm.{int(UpperCamelCase )-1}''' )
if "layer_norm1" in key:
A__ : List[Any] =key.replace("layer_norm1" , "layer_norm_1" )
if "layer_norm2" in key:
A__ : Optional[int] =key.replace("layer_norm2" , "layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
A__ : int =key[key.find("block" ) + len("block" )]
A__ : Optional[Any] =key.replace(F'''block{idx}''' , F'''block.{int(UpperCamelCase )-1}''' )
if "attn.q" in key:
A__ : Optional[Any] =key.replace("attn.q" , "attention.self.query" )
if "attn.proj" in key:
A__ : Union[str, Any] =key.replace("attn.proj" , "attention.output.dense" )
if "attn" in key:
A__ : str =key.replace("attn" , "attention.self" )
if "fc1" in key:
A__ : Dict =key.replace("fc1" , "dense1" )
if "fc2" in key:
A__ : str =key.replace("fc2" , "dense2" )
if "linear_pred" in key:
A__ : List[Any] =key.replace("linear_pred" , "classifier" )
if "linear_fuse" in key:
A__ : List[str] =key.replace("linear_fuse.conv" , "linear_fuse" )
A__ : Any =key.replace("linear_fuse.bn" , "batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
A__ : str =key[key.find("linear_c" ) + len("linear_c" )]
A__ : Dict =key.replace(F'''linear_c{idx}''' , F'''linear_c.{int(UpperCamelCase )-1}''' )
if "bot_conv" in key:
A__ : Union[str, Any] =key.replace("bot_conv" , "0.convolution" )
if "skip_conv1" in key:
A__ : List[Any] =key.replace("skip_conv1" , "1.convolution" )
if "skip_conv2" in key:
A__ : int =key.replace("skip_conv2" , "2.convolution" )
if "fusion1" in key:
A__ : Optional[Any] =key.replace("fusion1" , "1.fusion" )
if "fusion2" in key:
A__ : Optional[Any] =key.replace("fusion2" , "2.fusion" )
if "fusion3" in key:
A__ : int =key.replace("fusion3" , "3.fusion" )
if "fusion" in key and "conv" in key:
A__ : List[str] =key.replace("conv" , "convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
A__ : Tuple =key.replace("module.last_layer_depth" , "head.head" )
A__ : int =value
return new_state_dict
def lowercase ( UpperCamelCase : Union[str, Any] , UpperCamelCase : Dict ):
"""simple docstring"""
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
A__ : int =state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
A__ : str =state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias''' )
# next, add keys and values (in that order) to the state dict
A__ : List[str] =kv_weight[
: config.hidden_sizes[i], :
]
A__ : Dict =kv_bias[: config.hidden_sizes[i]]
A__ : Any =kv_weight[
config.hidden_sizes[i] :, :
]
A__ : Any =kv_bias[config.hidden_sizes[i] :]
def lowercase ( ):
"""simple docstring"""
A__ : Optional[Any] ="http://images.cocodataset.org/val2017/000000039769.jpg"
A__ : List[Any] =Image.open(requests.get(UpperCamelCase , stream=UpperCamelCase ).raw )
return image
@torch.no_grad()
def lowercase ( UpperCamelCase : str , UpperCamelCase : Tuple , UpperCamelCase : List[str]=False , UpperCamelCase : str=None ):
"""simple docstring"""
A__ : List[str] =GLPNConfig(hidden_sizes=[64, 128, 320, 512] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
A__ : str =GLPNImageProcessor()
# prepare image
A__ : Any =prepare_img()
A__ : Optional[int] =image_processor(images=UpperCamelCase , return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
A__ : int =torch.load(UpperCamelCase , map_location=torch.device("cpu" ) )
# rename keys
A__ : Union[str, Any] =rename_keys(UpperCamelCase )
# key and value matrices need special treatment
read_in_k_v(UpperCamelCase , UpperCamelCase )
# create HuggingFace model and load state dict
A__ : Optional[int] =GLPNForDepthEstimation(UpperCamelCase )
model.load_state_dict(UpperCamelCase )
model.eval()
# forward pass
A__ : int =model(UpperCamelCase )
A__ : Optional[Any] =outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
A__ : List[Any] =torch.tensor(
[[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]] )
elif "kitti" in model_name:
A__ : Tuple =torch.tensor(
[[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]] )
else:
raise ValueError(F'''Unknown model name: {model_name}''' )
A__ : str =torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , UpperCamelCase , atol=1E-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the hub..." )
model.push_to_hub(
repo_path_or_name=Path(UpperCamelCase , UpperCamelCase ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=UpperCamelCase , )
image_processor.push_to_hub(
repo_path_or_name=Path(UpperCamelCase , UpperCamelCase ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=UpperCamelCase , )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
default=None,
type=str,
help="Path to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
parser.add_argument(
"--model_name",
default="glpn-kitti",
type=str,
help="Name of the model in case you're pushing to the hub.",
)
__A : Any = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 656 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = 42
snake_case_ = 42
class _a ( lowercase__ , lowercase__ ):
"""simple docstring"""
snake_case_ = 1
@register_to_config
def __init__( self : Dict , a : int = 20_00 , a : float = 0.15 , a : float = 0.01 , a : float = 1348.0 , a : float = 1E-5 , a : int = 1 , ) ->int:
# standard deviation of the initial noise distribution
SCREAMING_SNAKE_CASE__ : Any = sigma_max
# setable values
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.set_sigmas(a , a , a , a )
def A_ ( self : Any , a : torch.FloatTensor , a : Optional[int] = None ) ->torch.FloatTensor:
return sample
def A_ ( self : Tuple , a : int , a : float = None , a : Union[str, torch.device] = None ) ->str:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sampling_eps if sampling_eps is not None else self.config.sampling_eps
SCREAMING_SNAKE_CASE__ : str = torch.linspace(1 , a , a , device=a )
def A_ ( self : Optional[Any] , a : int , a : float = None , a : float = None , a : float = None ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] = sigma_min if sigma_min is not None else self.config.sigma_min
SCREAMING_SNAKE_CASE__ : List[str] = sigma_max if sigma_max is not None else self.config.sigma_max
SCREAMING_SNAKE_CASE__ : Tuple = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(a , a )
SCREAMING_SNAKE_CASE__ : Tuple = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.exp(torch.linspace(math.log(a ) , math.log(a ) , a ) )
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def A_ ( self : Optional[Any] , a : str , a : Tuple ) ->Dict:
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def A_ ( self : Optional[int] , a : torch.FloatTensor , a : int , a : torch.FloatTensor , a : Optional[torch.Generator] = None , a : bool = True , ) ->Union[SdeVeOutput, Tuple]:
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
SCREAMING_SNAKE_CASE__ : Tuple = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
SCREAMING_SNAKE_CASE__ : str = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
SCREAMING_SNAKE_CASE__ : List[Any] = timesteps.to(self.discrete_sigmas.device )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.discrete_sigmas[timesteps].to(sample.device )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_adjacent_sigma(a , a ).to(sample.device )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.zeros_like(a )
SCREAMING_SNAKE_CASE__ : List[str] = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
SCREAMING_SNAKE_CASE__ : Optional[Any] = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
SCREAMING_SNAKE_CASE__ : Any = diffusion.unsqueeze(-1 )
SCREAMING_SNAKE_CASE__ : Any = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
SCREAMING_SNAKE_CASE__ : Tuple = randn_tensor(
sample.shape , layout=sample.layout , generator=a , device=sample.device , dtype=sample.dtype )
SCREAMING_SNAKE_CASE__ : str = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
SCREAMING_SNAKE_CASE__ : str = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=a , prev_sample_mean=a )
def A_ ( self : Optional[Any] , a : torch.FloatTensor , a : torch.FloatTensor , a : Optional[torch.Generator] = None , a : bool = True , ) ->Union[SchedulerOutput, Tuple]:
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
SCREAMING_SNAKE_CASE__ : str = randn_tensor(sample.shape , layout=sample.layout , generator=a ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
SCREAMING_SNAKE_CASE__ : int = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
SCREAMING_SNAKE_CASE__ : Any = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
SCREAMING_SNAKE_CASE__ : Tuple = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
SCREAMING_SNAKE_CASE__ : Optional[int] = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
SCREAMING_SNAKE_CASE__ : Tuple = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
SCREAMING_SNAKE_CASE__ : Tuple = step_size.unsqueeze(-1 )
SCREAMING_SNAKE_CASE__ : List[str] = sample + step_size * model_output
SCREAMING_SNAKE_CASE__ : int = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=a )
def A_ ( self : Tuple , a : torch.FloatTensor , a : torch.FloatTensor , a : torch.FloatTensor , ) ->torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
SCREAMING_SNAKE_CASE__ : Optional[int] = timesteps.to(original_samples.device )
SCREAMING_SNAKE_CASE__ : Dict = self.discrete_sigmas.to(original_samples.device )[timesteps]
SCREAMING_SNAKE_CASE__ : Any = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(a ) * sigmas[:, None, None, None]
)
SCREAMING_SNAKE_CASE__ : int = noise + original_samples
return noisy_samples
def __len__( self : Dict ) ->Any:
return self.config.num_train_timesteps | 700 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
__lowercase :List[str] = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
__lowercase :str = get_tests_dir("fixtures/vocab.json")
__lowercase :Optional[int] = get_tests_dir("fixtures")
class _a ( unittest.TestCase ):
"""simple docstring"""
snake_case_ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
def A_ ( self : Optional[Any] ) ->int:
SCREAMING_SNAKE_CASE__ : Dict = 0
def A_ ( self : Any ) ->Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
self.assertIsInstance(a , a )
def A_ ( self : Union[str, Any] ) ->List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Dict = WavaVecaConfig()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
# save in new folder
model_config.save_pretrained(a )
processor.save_pretrained(a )
SCREAMING_SNAKE_CASE__ : str = AutoProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
def A_ ( self : int ) ->List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(a , os.path.join(a , a ) )
copyfile(a , os.path.join(a , "vocab.json" ) )
SCREAMING_SNAKE_CASE__ : List[Any] = AutoProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
def A_ ( self : List[Any] ) ->Tuple:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Optional[Any] = WavaVecaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : Tuple = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
SCREAMING_SNAKE_CASE__ : Any = WavaVecaProcessor(a , a )
# save in new folder
processor.save_pretrained(a )
# drop `processor_class` in tokenizer
with open(os.path.join(a , a ) , "r" ) as f:
SCREAMING_SNAKE_CASE__ : Optional[int] = json.load(a )
config_dict.pop("processor_class" )
with open(os.path.join(a , a ) , "w" ) as f:
f.write(json.dumps(a ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = AutoProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
def A_ ( self : List[str] ) ->Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Tuple = WavaVecaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
SCREAMING_SNAKE_CASE__ : Optional[int] = WavaVecaProcessor(a , a )
# save in new folder
processor.save_pretrained(a )
# drop `processor_class` in feature extractor
with open(os.path.join(a , a ) , "r" ) as f:
SCREAMING_SNAKE_CASE__ : List[Any] = json.load(a )
config_dict.pop("processor_class" )
with open(os.path.join(a , a ) , "w" ) as f:
f.write(json.dumps(a ) )
SCREAMING_SNAKE_CASE__ : List[Any] = AutoProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
def A_ ( self : Union[str, Any] ) ->str:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : List[Any] = WavaVecaConfig(processor_class="Wav2Vec2Processor" )
model_config.save_pretrained(a )
# copy relevant files
copyfile(a , os.path.join(a , "vocab.json" ) )
# create emtpy sample processor
with open(os.path.join(a , a ) , "w" ) as f:
f.write("{}" )
SCREAMING_SNAKE_CASE__ : Tuple = AutoProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
def A_ ( self : Optional[Any] ) ->Optional[int]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(a ):
SCREAMING_SNAKE_CASE__ : Optional[int] = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(a ):
SCREAMING_SNAKE_CASE__ : Any = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=a )
SCREAMING_SNAKE_CASE__ : List[Any] = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" , trust_remote_code=a )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
SCREAMING_SNAKE_CASE__ : Dict = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
SCREAMING_SNAKE_CASE__ : int = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=a , use_fast=a )
SCREAMING_SNAKE_CASE__ : List[Any] = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , "NewTokenizer" )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
def A_ ( self : Tuple ) ->List[Any]:
try:
AutoConfig.register("custom" , a )
AutoFeatureExtractor.register(a , a )
AutoTokenizer.register(a , slow_tokenizer_class=a )
AutoProcessor.register(a , a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(a ):
AutoProcessor.register(a , a )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE__ : List[str] = CustomFeatureExtractor.from_pretrained(a )
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE__ : int = os.path.join(a , "vocab.txt" )
with open(a , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = CustomTokenizer(a )
SCREAMING_SNAKE_CASE__ : List[Any] = CustomProcessor(a , a )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(a )
SCREAMING_SNAKE_CASE__ : Any = AutoProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def A_ ( self : Union[str, Any] ) ->int:
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = False
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = False
class _a ( lowercase__ ):
"""simple docstring"""
snake_case_ = "AutoFeatureExtractor"
snake_case_ = "AutoTokenizer"
snake_case_ = False
try:
AutoConfig.register("custom" , a )
AutoFeatureExtractor.register(a , a )
AutoTokenizer.register(a , slow_tokenizer_class=a )
AutoProcessor.register(a , a )
# If remote code is not set, the default is to use local classes.
SCREAMING_SNAKE_CASE__ : Optional[int] = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
SCREAMING_SNAKE_CASE__ : Tuple = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=a )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
SCREAMING_SNAKE_CASE__ : Any = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=a )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def A_ ( self : Optional[Any] ) ->Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(processor.__class__.__name__ , "BertTokenizerFast" )
def A_ ( self : Dict ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext" )
self.assertEqual(processor.__class__.__name__ , "ConvNextImageProcessor" )
@is_staging_test
class _a ( unittest.TestCase ):
"""simple docstring"""
snake_case_ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def A_ ( cls : List[str] ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int = TOKEN
HfFolder.save_token(a )
@classmethod
def A_ ( cls : List[str] ) ->Optional[int]:
try:
delete_repo(token=cls._token , repo_id="test-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-processor" )
except HTTPError:
pass
def A_ ( self : Dict ) ->Dict:
SCREAMING_SNAKE_CASE__ : Tuple = WavaVecaProcessor.from_pretrained(a )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(a , "test-processor" ) , push_to_hub=a , use_auth_token=self._token )
SCREAMING_SNAKE_CASE__ : Optional[int] = WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(a , getattr(new_processor.feature_extractor , a ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def A_ ( self : List[str] ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = WavaVecaProcessor.from_pretrained(a )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(a , "test-processor-org" ) , push_to_hub=a , use_auth_token=self._token , organization="valid_org" , )
SCREAMING_SNAKE_CASE__ : Dict = WavaVecaProcessor.from_pretrained("valid_org/test-processor-org" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(a , getattr(new_processor.feature_extractor , a ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def A_ ( self : Any ) ->int:
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
SCREAMING_SNAKE_CASE__ : Any = CustomFeatureExtractor.from_pretrained(a )
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(a , "vocab.txt" )
with open(a , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : str = CustomTokenizer(a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = CustomProcessor(a , a )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(f"""{USER}/test-dynamic-processor""" , token=self._token )
SCREAMING_SNAKE_CASE__ : str = Repository(a , clone_from=f"""{USER}/test-dynamic-processor""" , token=self._token )
processor.save_pretrained(a )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(a , "tokenizer_config.json" ) ) as f:
SCREAMING_SNAKE_CASE__ : str = json.load(a )
self.assertDictEqual(
tokenizer_config["auto_map"] , {
"AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(a , "custom_feature_extraction.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(a , "custom_tokenization.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(a , "custom_processing.py" ) ) )
repo.push_to_hub()
SCREAMING_SNAKE_CASE__ : List[Any] = AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor""" , trust_remote_code=a )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , "CustomProcessor" ) | 26 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : List[str] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
A__ : Dict = XGLMTokenizer
A__ : Optional[int] = XGLMTokenizerFast
A__ : int = True
A__ : Optional[Any] = True
def snake_case_ ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
__lowercase : Optional[Any] = XGLMTokenizer(_snake_case , keep_accents=_snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case_ ( self : List[Any] ):
__lowercase : int = '''<pad>'''
__lowercase : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case ) , _snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case ) , _snake_case )
def snake_case_ ( self : Dict ):
__lowercase : Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(len(_snake_case ) , 1008 )
def snake_case_ ( self : List[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1008 )
def snake_case_ ( self : Dict ):
__lowercase : List[str] = XGLMTokenizer(_snake_case , keep_accents=_snake_case )
__lowercase : Union[str, Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_snake_case , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowercase : Optional[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_snake_case , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__lowercase : Tuple = tokenizer.convert_tokens_to_ids(_snake_case )
self.assertListEqual(
_snake_case , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__lowercase : Union[str, Any] = tokenizer.convert_ids_to_tokens(_snake_case )
self.assertListEqual(
_snake_case , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def snake_case_ ( self : List[str] ):
return XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
def snake_case_ ( self : Any ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(_snake_case , f.name )
__lowercase : Union[str, Any] = XGLMTokenizer(f.name , keep_accents=_snake_case )
__lowercase : List[str] = pickle.dumps(_snake_case )
pickle.loads(_snake_case )
def snake_case_ ( self : str ):
if not self.test_rust_tokenizer:
return
__lowercase : Tuple = self.get_tokenizer()
__lowercase : Optional[int] = self.get_rust_tokenizer()
__lowercase : Dict = '''I was born in 92000, and this is falsé.'''
__lowercase : int = tokenizer.tokenize(_snake_case )
__lowercase : int = rust_tokenizer.tokenize(_snake_case )
self.assertListEqual(_snake_case , _snake_case )
__lowercase : Dict = tokenizer.encode(_snake_case , add_special_tokens=_snake_case )
__lowercase : Tuple = rust_tokenizer.encode(_snake_case , add_special_tokens=_snake_case )
self.assertListEqual(_snake_case , _snake_case )
__lowercase : Any = self.get_rust_tokenizer()
__lowercase : List[str] = tokenizer.encode(_snake_case )
__lowercase : Union[str, Any] = rust_tokenizer.encode(_snake_case )
self.assertListEqual(_snake_case , _snake_case )
@slow
def snake_case_ ( self : Union[str, Any] ):
__lowercase : Optional[Any] = '''Hello World!'''
__lowercase : int = [2, 3_1227, 4447, 35]
self.assertListEqual(_snake_case , self.big_tokenizer.encode(_snake_case ) )
@slow
def snake_case_ ( self : Union[str, Any] ):
__lowercase : Optional[int] = (
'''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'''
)
# fmt: off
__lowercase : Any = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(_snake_case , self.big_tokenizer.encode(_snake_case ) )
@slow
def snake_case_ ( self : Union[str, Any] ):
# fmt: off
__lowercase : Optional[Any] = {
'''input_ids''': [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
'''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_snake_case , model_name='''facebook/xglm-564M''' , padding=_snake_case , )
| 509 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] = {
"facebook/data2vec-vision-base-ft": (
"https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
),
}
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Optional[Any] = '''data2vec-vision'''
def __init__( self : str , _snake_case : str=768 , _snake_case : Tuple=12 , _snake_case : Any=12 , _snake_case : Optional[int]=3072 , _snake_case : Tuple="gelu" , _snake_case : Dict=0.0 , _snake_case : Any=0.0 , _snake_case : Tuple=0.02 , _snake_case : List[Any]=1E-1_2 , _snake_case : int=224 , _snake_case : List[str]=16 , _snake_case : List[str]=3 , _snake_case : Optional[int]=False , _snake_case : str=False , _snake_case : Tuple=False , _snake_case : Tuple=False , _snake_case : Any=0.1 , _snake_case : Any=0.1 , _snake_case : List[Any]=True , _snake_case : List[Any]=[3, 5, 7, 11] , _snake_case : List[Any]=[1, 2, 3, 6] , _snake_case : Tuple=True , _snake_case : str=0.4 , _snake_case : Any=256 , _snake_case : Any=1 , _snake_case : str=False , _snake_case : str=255 , **_snake_case : Dict , ):
super().__init__(**_snake_case )
__lowercase : int = hidden_size
__lowercase : Optional[int] = num_hidden_layers
__lowercase : str = num_attention_heads
__lowercase : List[Any] = intermediate_size
__lowercase : Union[str, Any] = hidden_act
__lowercase : Optional[int] = hidden_dropout_prob
__lowercase : Tuple = attention_probs_dropout_prob
__lowercase : Dict = initializer_range
__lowercase : List[str] = layer_norm_eps
__lowercase : str = image_size
__lowercase : List[str] = patch_size
__lowercase : Dict = num_channels
__lowercase : Optional[Any] = use_mask_token
__lowercase : Optional[int] = use_absolute_position_embeddings
__lowercase : Tuple = use_relative_position_bias
__lowercase : Dict = use_shared_relative_position_bias
__lowercase : List[Any] = layer_scale_init_value
__lowercase : Union[str, Any] = drop_path_rate
__lowercase : Tuple = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowercase : List[str] = out_indices
__lowercase : Tuple = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowercase : Dict = use_auxiliary_head
__lowercase : str = auxiliary_loss_weight
__lowercase : Union[str, Any] = auxiliary_channels
__lowercase : Dict = auxiliary_num_convs
__lowercase : Dict = auxiliary_concat_input
__lowercase : Dict = semantic_loss_ignore_index
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : int = version.parse('''1.11''' )
@property
def snake_case_ ( self : str ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def snake_case_ ( self : Tuple ):
return 1E-4
| 509 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase_ = logging.getLogger(__name__)
lowerCAmelCase_ = tf.data.AUTOTUNE
def snake_case ( ):
A = argparse.ArgumentParser(description='Train a masked language model on TPU.' )
parser.add_argument(
'--pretrained_model_config', type=UpperCAmelCase__, default='roberta-base', help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!', )
parser.add_argument(
'--tokenizer', type=UpperCAmelCase__, default='unigram-tokenizer-wikitext', help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.', )
parser.add_argument(
'--per_replica_batch_size', type=UpperCAmelCase__, default=8, help='Batch size per TPU core.', )
parser.add_argument(
'--no_tpu', action='store_true', help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.', )
parser.add_argument(
'--tpu_name', type=UpperCAmelCase__, help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.', default='local', )
parser.add_argument(
'--tpu_zone', type=UpperCAmelCase__, help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.', )
parser.add_argument(
'--gcp_project', type=UpperCAmelCase__, help='Google cloud project name. Only used for non-Colab TPU nodes.' )
parser.add_argument(
'--bfloat16', action='store_true', help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.', )
parser.add_argument(
'--train_dataset', type=UpperCAmelCase__, help='Path to training dataset to load. If the path begins with `gs://`'
' then the dataset will be loaded from a Google Cloud Storage bucket.', )
parser.add_argument(
'--shuffle_buffer_size', type=UpperCAmelCase__, default=2**18, help='Size of the shuffle buffer (in samples)', )
parser.add_argument(
'--eval_dataset', type=UpperCAmelCase__, help='Path to evaluation dataset to load. If the path begins with `gs://`'
' then the dataset will be loaded from a Google Cloud Storage bucket.', )
parser.add_argument(
'--num_epochs', type=UpperCAmelCase__, default=1, help='Number of epochs to train for.', )
parser.add_argument(
'--learning_rate', type=UpperCAmelCase__, default=1E-4, help='Learning rate to use for training.', )
parser.add_argument(
'--weight_decay_rate', type=UpperCAmelCase__, default=1E-3, help='Weight decay rate to use for training.', )
parser.add_argument(
'--max_length', type=UpperCAmelCase__, default=5_12, help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py', )
parser.add_argument(
'--mlm_probability', type=UpperCAmelCase__, default=0.15, help='Fraction of tokens to mask during training.', )
parser.add_argument('--output_dir', type=UpperCAmelCase__, required=UpperCAmelCase__, help='Path to save model checkpoints to.' )
parser.add_argument('--hub_model_id', type=UpperCAmelCase__, help='Model ID to upload to on the Hugging Face Hub.' )
A = parser.parse_args()
return args
def snake_case ( UpperCAmelCase : Tuple ):
try:
if args.tpu_name:
A = tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name, zone=args.tpu_zone, project=args.gcp_project )
else:
A = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
'--gcp_project. When running on a TPU VM, use --tpu_name local.' )
tf.config.experimental_connect_to_cluster(UpperCAmelCase__ )
tf.tpu.experimental.initialize_tpu_system(UpperCAmelCase__ )
return tpu
def snake_case ( UpperCAmelCase : int ):
A = 0
for file in file_list:
A = file.split('/' )[-1]
A = re.search(r'-\d+-(\d+)\.tfrecord', UpperCAmelCase__ ).group(1 )
A = int(UpperCAmelCase__ )
num_samples += sample_count
return num_samples
def snake_case ( UpperCAmelCase : Tuple, UpperCAmelCase : List[str], UpperCAmelCase : str, UpperCAmelCase : Optional[int], UpperCAmelCase : List[str], UpperCAmelCase : Optional[int]=None ):
A = count_samples(UpperCAmelCase__ )
A = tf.data.Dataset.from_tensor_slices(UpperCAmelCase__ )
if shuffle:
A = dataset.shuffle(len(UpperCAmelCase__ ) )
A = tf.data.TFRecordDataset(UpperCAmelCase__, num_parallel_reads=UpperCAmelCase__ )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
A = dataset.apply(tf.data.experimental.assert_cardinality(UpperCAmelCase__ ) )
A = dataset.map(UpperCAmelCase__, num_parallel_calls=UpperCAmelCase__ )
if shuffle:
assert shuffle_buffer_size is not None
A = dataset.shuffle(args.shuffle_buffer_size )
A = dataset.batch(UpperCAmelCase__, drop_remainder=UpperCAmelCase__ )
A = dataset.map(UpperCAmelCase__, num_parallel_calls=UpperCAmelCase__ )
A = dataset.prefetch(UpperCAmelCase__ )
return dataset
def snake_case ( UpperCAmelCase : List[str] ):
if not args.no_tpu:
A = initialize_tpu(UpperCAmelCase__ )
A = tf.distribute.TPUStrategy(UpperCAmelCase__ )
else:
A = tf.distribute.OneDeviceStrategy(device='/gpu:0' )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy('mixed_bfloat16' )
A = AutoTokenizer.from_pretrained(args.tokenizer )
A = AutoConfig.from_pretrained(args.pretrained_model_config )
A = tokenizer.vocab_size
A = tf.io.gfile.glob(os.path.join(args.train_dataset, '*.tfrecord' ) )
if not training_records:
raise ValueError(f'No .tfrecord files found in {args.train_dataset}.' )
A = tf.io.gfile.glob(os.path.join(args.eval_dataset, '*.tfrecord' ) )
if not eval_records:
raise ValueError(f'No .tfrecord files found in {args.eval_dataset}.' )
A = count_samples(UpperCAmelCase__ )
A = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
A = steps_per_epoch * args.num_epochs
with strategy.scope():
A = TFAutoModelForMaskedLM.from_config(UpperCAmelCase__ )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
A = create_optimizer(
num_train_steps=UpperCAmelCase__, num_warmup_steps=total_train_steps // 20, init_lr=args.learning_rate, weight_decay_rate=args.weight_decay_rate, )
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=UpperCAmelCase__, metrics=['accuracy'] )
def decode_fn(UpperCAmelCase : Union[str, Any] ):
A = {
"""input_ids""": tf.io.FixedLenFeature(dtype=tf.intaa, shape=(args.max_length,) ),
"""attention_mask""": tf.io.FixedLenFeature(dtype=tf.intaa, shape=(args.max_length,) ),
}
return tf.io.parse_single_example(UpperCAmelCase__, UpperCAmelCase__ )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
A = DataCollatorForLanguageModeling(
tokenizer=UpperCAmelCase__, mlm_probability=args.mlm_probability, mlm=UpperCAmelCase__, return_tensors='tf' )
def mask_with_collator(UpperCAmelCase : List[str] ):
# TF really needs an isin() function
A = (
~tf.cast(batch['attention_mask'], tf.bool )
| (batch["""input_ids"""] == tokenizer.cls_token_id)
| (batch["""input_ids"""] == tokenizer.sep_token_id)
)
A = data_collator.tf_mask_tokens(
batch['input_ids'], vocab_size=len(UpperCAmelCase__ ), mask_token_id=tokenizer.mask_token_id, special_tokens_mask=UpperCAmelCase__, )
return batch
A = args.per_replica_batch_size * strategy.num_replicas_in_sync
A = prepare_dataset(
UpperCAmelCase__, decode_fn=UpperCAmelCase__, mask_fn=UpperCAmelCase__, batch_size=UpperCAmelCase__, shuffle=UpperCAmelCase__, shuffle_buffer_size=args.shuffle_buffer_size, )
A = prepare_dataset(
UpperCAmelCase__, decode_fn=UpperCAmelCase__, mask_fn=UpperCAmelCase__, batch_size=UpperCAmelCase__, shuffle=UpperCAmelCase__, )
A = []
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir, hub_model_id=args.hub_model_id, tokenizer=UpperCAmelCase__ ) )
model.fit(
UpperCAmelCase__, validation_data=UpperCAmelCase__, epochs=args.num_epochs, callbacks=UpperCAmelCase__, )
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
lowerCAmelCase_ = parse_args()
main(args)
| 708 |
from collections.abc import Callable
class UpperCamelCase :
"""simple docstring"""
def __init__( self : Tuple ,_SCREAMING_SNAKE_CASE : Callable | None = None ) -> None:
'''simple docstring'''
# Stores actual heap items.
A = []
# Stores indexes of each item for supporting updates and deletion.
A = {}
# Stores current size of heap.
A = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
A = key or (lambda _SCREAMING_SNAKE_CASE : x)
def A( self : Tuple ,_SCREAMING_SNAKE_CASE : int ) -> int | None:
'''simple docstring'''
return int((i - 1) / 2 ) if i > 0 else None
def A( self : Union[str, Any] ,_SCREAMING_SNAKE_CASE : int ) -> int | None:
'''simple docstring'''
A = int(2 * i + 1 )
return left if 0 < left < self.size else None
def A( self : Union[str, Any] ,_SCREAMING_SNAKE_CASE : int ) -> int | None:
'''simple docstring'''
A = int(2 * i + 2 )
return right if 0 < right < self.size else None
def A( self : List[str] ,_SCREAMING_SNAKE_CASE : int ,_SCREAMING_SNAKE_CASE : int ) -> None:
'''simple docstring'''
A , A = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
A , A = self.arr[j], self.arr[i]
def A( self : List[str] ,_SCREAMING_SNAKE_CASE : int ,_SCREAMING_SNAKE_CASE : int ) -> bool:
'''simple docstring'''
return self.arr[i][1] < self.arr[j][1]
def A( self : int ,_SCREAMING_SNAKE_CASE : int ) -> int:
'''simple docstring'''
A = self._left(_SCREAMING_SNAKE_CASE )
A = self._right(_SCREAMING_SNAKE_CASE )
A = i
if left is not None and not self._cmp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
A = left
if right is not None and not self._cmp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
A = right
return valid_parent
def A( self : Any ,_SCREAMING_SNAKE_CASE : int ) -> None:
'''simple docstring'''
A = 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 )
A , A = parent, self._parent(_SCREAMING_SNAKE_CASE )
def A( self : List[Any] ,_SCREAMING_SNAKE_CASE : int ) -> None:
'''simple docstring'''
A = self._get_valid_parent(_SCREAMING_SNAKE_CASE )
while valid_parent != index:
self._swap(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
A , A = valid_parent, self._get_valid_parent(_SCREAMING_SNAKE_CASE )
def A( self : Optional[Any] ,_SCREAMING_SNAKE_CASE : int ,_SCREAMING_SNAKE_CASE : int ) -> None:
'''simple docstring'''
if item not in self.pos_map:
return
A = self.pos_map[item]
A = [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 A( self : int ,_SCREAMING_SNAKE_CASE : int ) -> None:
'''simple docstring'''
if item not in self.pos_map:
return
A = self.pos_map[item]
del self.pos_map[item]
A = self.arr[self.size - 1]
A = 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 A( self : Optional[Any] ,_SCREAMING_SNAKE_CASE : int ,_SCREAMING_SNAKE_CASE : int ) -> None:
'''simple docstring'''
A = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(_SCREAMING_SNAKE_CASE )] )
else:
A = [item, self.key(_SCREAMING_SNAKE_CASE )]
A = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def A( self : str ) -> tuple | None:
'''simple docstring'''
return self.arr[0] if self.size else None
def A( self : Any ) -> tuple | None:
'''simple docstring'''
A = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def snake_case ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 110 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : int = 10**12 ):
__UpperCAmelCase : Optional[Any] = 1
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Any = 1
__UpperCAmelCase : int = 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() = }""")
| 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
i += 1
def lowerCamelCase__ ( __lowerCamelCase : int ):
return seive[n]
def lowerCamelCase__ ( __lowerCamelCase : int ):
return any(digit in """02468""" for digit in str(__lowerCamelCase ) )
def lowerCamelCase__ ( __lowerCamelCase : int = 1000000 ):
__UpperCAmelCase : Optional[Any] = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(__lowerCamelCase ) and not contains_an_even_digit(__lowerCamelCase ):
__UpperCAmelCase : Tuple = str(__lowerCamelCase )
__UpperCAmelCase : List[Any] = [int(str_num[j:] + str_num[:j] ) for j in range(len(__lowerCamelCase ) )]
if all(is_prime(__lowerCamelCase ) for i in list_nums ):
result.append(__lowerCamelCase )
return result
def lowerCamelCase__ ( ):
return len(find_circular_primes() )
if __name__ == "__main__":
print(f"""{len(find_circular_primes()) = }""")
| 63 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowerCAmelCase__ ( a__ , a__ ):
"""simple docstring"""
@register_to_config
def __init__( self : Optional[Any] , A__ : Tuple = 1_2_8 , A__ : str = 2_5_6 , A__ : str = 2_0_0_0.0 , A__ : Optional[Any] = 7_6_8 , A__ : Tuple = 1_2 , A__ : int = 1_2 , A__ : List[Any] = 6_4 , A__ : List[str] = 2_0_4_8 , A__ : List[Any] = 0.1 , ) -> Tuple:
'''simple docstring'''
super().__init__()
a__ : int = nn.Sequential(
nn.Linear(_A , d_model * 4 , bias=_A ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=_A ) , nn.SiLU() , )
a__ : Any = nn.Embedding(_A , _A )
a__ : Tuple = False
a__ : Union[str, Any] = nn.Linear(_A , _A , bias=_A )
a__ : int = nn.Dropout(p=_A )
a__ : int = nn.ModuleList()
for lyr_num in range(_A ):
# FiLM conditional T5 decoder
a__ : Any = DecoderLayer(d_model=_A , d_kv=_A , num_heads=_A , d_ff=_A , dropout_rate=_A )
self.decoders.append(_A )
a__ : Optional[Any] = TaLayerNorm(_A )
a__ : List[str] = nn.Dropout(p=_A )
a__ : Optional[Any] = nn.Linear(_A , _A , bias=_A )
def __lowerCAmelCase ( self : int , A__ : Union[str, Any] , A__ : Optional[Any] ) -> str:
'''simple docstring'''
a__ : Dict = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def __lowerCAmelCase ( self : List[Any] , A__ : str , A__ : int , A__ : Optional[Any] ) -> str:
'''simple docstring'''
a__ : Dict = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
a__ : Any = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
a__ : Union[str, Any] = self.conditioning_emb(_A ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
a__ : str = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
a__ : Union[str, Any] = torch.broadcast_to(
torch.arange(_A , device=decoder_input_tokens.device ) , (batch, seq_length) , )
a__ : Any = self.position_encoding(_A )
a__ : str = self.continuous_inputs_projection(_A )
inputs += position_encodings
a__ : int = self.dropout(_A )
# decoder: No padding present.
a__ : Union[str, Any] = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
a__ : Optional[Any] = [(x, self.encoder_decoder_mask(_A , _A )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
a__ : Dict = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
a__ : Tuple = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
a__ : Tuple = lyr(
_A , conditioning_emb=_A , encoder_hidden_states=_A , encoder_attention_mask=_A , )[0]
a__ : Any = self.decoder_norm(_A )
a__ : List[Any] = self.post_dropout(_A )
a__ : int = self.spec_out(_A )
return spec_out
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : str , A__ : Any , A__ : Optional[int] , A__ : Optional[int] , A__ : Tuple , A__ : Any , A__ : Optional[int]=1E-6 ) -> int:
'''simple docstring'''
super().__init__()
a__ : Optional[Any] = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=_A , d_kv=_A , num_heads=_A , dropout_rate=_A ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=_A , d_kv=_A , num_heads=_A , dropout_rate=_A , layer_norm_epsilon=_A , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=_A , d_ff=_A , dropout_rate=_A , layer_norm_epsilon=_A ) )
def __lowerCAmelCase ( self : Union[str, Any] , A__ : Tuple , A__ : List[str]=None , A__ : List[Any]=None , A__ : List[Any]=None , A__ : Any=None , A__ : Tuple=None , ) -> Optional[Any]:
'''simple docstring'''
a__ : Any = self.layer[0](
_A , conditioning_emb=_A , attention_mask=_A , )
if encoder_hidden_states is not None:
a__ : Any = torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
a__ : str = self.layer[1](
_A , key_value_states=_A , attention_mask=_A , )
# Apply Film Conditional Feed Forward layer
a__ : Optional[Any] = self.layer[-1](_A , _A )
return (hidden_states,)
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[Any] , A__ : Optional[Any] , A__ : List[Any] , A__ : List[Any] , A__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
super().__init__()
a__ : Union[str, Any] = TaLayerNorm(_A )
a__ : Any = TaFiLMLayer(in_features=d_model * 4 , out_features=_A )
a__ : Dict = Attention(query_dim=_A , heads=_A , dim_head=_A , out_bias=_A , scale_qk=_A )
a__ : Tuple = nn.Dropout(_A )
def __lowerCAmelCase ( self : Dict , A__ : Tuple , A__ : List[Any]=None , A__ : List[Any]=None , ) -> List[str]:
'''simple docstring'''
a__ : int = self.layer_norm(_A )
if conditioning_emb is not None:
a__ : Union[str, Any] = self.FiLMLayer(_A , _A )
# Self-attention block
a__ : Union[str, Any] = self.attention(_A )
a__ : Optional[Any] = hidden_states + self.dropout(_A )
return hidden_states
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Any , A__ : Tuple , A__ : List[Any] , A__ : Any , A__ : List[Any] , A__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
super().__init__()
a__ : List[str] = Attention(query_dim=_A , heads=_A , dim_head=_A , out_bias=_A , scale_qk=_A )
a__ : Optional[int] = TaLayerNorm(_A , eps=_A )
a__ : Tuple = nn.Dropout(_A )
def __lowerCAmelCase ( self : Union[str, Any] , A__ : int , A__ : Optional[Any]=None , A__ : Union[str, Any]=None , ) -> Optional[int]:
'''simple docstring'''
a__ : Union[str, Any] = self.layer_norm(_A )
a__ : str = self.attention(
_A , encoder_hidden_states=_A , attention_mask=attention_mask.squeeze(1 ) , )
a__ : Any = hidden_states + self.dropout(_A )
return layer_output
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Any , A__ : Optional[Any] , A__ : int , A__ : List[Any] , A__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
a__ : Optional[int] = TaDenseGatedActDense(d_model=_A , d_ff=_A , dropout_rate=_A )
a__ : Tuple = TaFiLMLayer(in_features=d_model * 4 , out_features=_A )
a__ : Any = TaLayerNorm(_A , eps=_A )
a__ : Union[str, Any] = nn.Dropout(_A )
def __lowerCAmelCase ( self : List[str] , A__ : Tuple , A__ : int=None ) -> List[str]:
'''simple docstring'''
a__ : int = self.layer_norm(_A )
if conditioning_emb is not None:
a__ : Union[str, Any] = self.film(_A , _A )
a__ : str = self.DenseReluDense(_A )
a__ : Tuple = hidden_states + self.dropout(_A )
return hidden_states
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[Any] , A__ : List[str] , A__ : Optional[int] , A__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
super().__init__()
a__ : Union[str, Any] = nn.Linear(_A , _A , bias=_A )
a__ : Any = nn.Linear(_A , _A , bias=_A )
a__ : Union[str, Any] = nn.Linear(_A , _A , bias=_A )
a__ : Union[str, Any] = nn.Dropout(_A )
a__ : int = NewGELUActivation()
def __lowerCAmelCase ( self : str , A__ : List[Any] ) -> int:
'''simple docstring'''
a__ : Tuple = self.act(self.wi_a(_A ) )
a__ : Optional[int] = self.wi_a(_A )
a__ : Union[str, Any] = hidden_gelu * hidden_linear
a__ : Dict = self.dropout(_A )
a__ : Dict = self.wo(_A )
return hidden_states
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , A__ : Optional[int] , A__ : Dict=1E-6 ) -> Dict:
'''simple docstring'''
super().__init__()
a__ : Union[str, Any] = nn.Parameter(torch.ones(_A ) )
a__ : Optional[int] = eps
def __lowerCAmelCase ( self : List[Any] , A__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
a__ : Optional[int] = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=_A )
a__ : List[Any] = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
a__ : Optional[int] = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __lowerCAmelCase ( self : Tuple , A__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044_715 * torch.pow(_A , 3.0 )) ))
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , A__ : Optional[Any] , A__ : Tuple ) -> Dict:
'''simple docstring'''
super().__init__()
a__ : List[str] = nn.Linear(_A , out_features * 2 , bias=_A )
def __lowerCAmelCase ( self : Any , A__ : int , A__ : Any ) -> Union[str, Any]:
'''simple docstring'''
a__ : List[Any] = self.scale_bias(_A )
a__ : List[Any] = torch.chunk(_A , 2 , -1 )
a__ : List[Any] = x * (1 + scale) + shift
return x
| 716 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list ):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
a__ : List[str] = grid[0]
for row_n in range(1 , len(lowerCAmelCase__ ) ):
a__ : Tuple = grid[row_n]
a__ : Union[str, Any] = fill_row(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : Optional[Any] = grid[row_n]
return grid[-1][-1]
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : list ):
current_row[0] += row_above[0]
for cell_n in range(1 , len(lowerCAmelCase__ ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 340 | 0 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _a ( lowercase__ : Optional[Any] , lowercase__ : Any , lowercase__ : List[Any] ):
'''simple docstring'''
if openai_config_file == "":
SCREAMING_SNAKE_CASE__ : Optional[int] = OpenAIGPTConfig()
else:
SCREAMING_SNAKE_CASE__ : int = OpenAIGPTConfig.from_json_file(lowercase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = OpenAIGPTModel(lowercase__ )
# Load weights from numpy
load_tf_weights_in_openai_gpt(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
SCREAMING_SNAKE_CASE__ : str = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
SCREAMING_SNAKE_CASE__ : Dict = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(model.state_dict() , lowercase__ )
print(f'''Save configuration file to {pytorch_config_dump_path}''' )
with open(lowercase__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--openai_checkpoint_folder_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--openai_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture."
),
)
SCREAMING_SNAKE_CASE__ : List[str] = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = ["FNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[str] = ["FNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = [
"FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FNetForMaskedLM",
"FNetForMultipleChoice",
"FNetForNextSentencePrediction",
"FNetForPreTraining",
"FNetForQuestionAnswering",
"FNetForSequenceClassification",
"FNetForTokenClassification",
"FNetLayer",
"FNetModel",
"FNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 85 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'RagModel',
'RagPreTrainedModel',
'RagSequenceForGeneration',
'RagTokenForGeneration',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'TFRagModel',
'TFRagPreTrainedModel',
'TFRagSequenceForGeneration',
'TFRagTokenForGeneration',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 714 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class lowerCamelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self , UpperCAmelCase__ = "▁" , UpperCAmelCase__ = True , UpperCAmelCase__ = "<unk>" , UpperCAmelCase__ = "</s>" , UpperCAmelCase__ = "<pad>" , ):
SCREAMING_SNAKE_CASE__ = {
"pad": {"id": 0, "token": pad_token},
"eos": {"id": 1, "token": eos_token},
"unk": {"id": 2, "token": unk_token},
}
SCREAMING_SNAKE_CASE__ = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
SCREAMING_SNAKE_CASE__ = token_dict["token"]
SCREAMING_SNAKE_CASE__ = Tokenizer(Unigram() )
SCREAMING_SNAKE_CASE__ = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}" ) , " " ),
normalizers.Lowercase(),
] )
SCREAMING_SNAKE_CASE__ = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=UpperCAmelCase__ , add_prefix_space=UpperCAmelCase__ ),
pre_tokenizers.Digits(individual_digits=UpperCAmelCase__ ),
pre_tokenizers.Punctuation(),
] )
SCREAMING_SNAKE_CASE__ = decoders.Metaspace(replacement=UpperCAmelCase__ , add_prefix_space=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ = TemplateProcessing(
single=f'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])] , )
SCREAMING_SNAKE_CASE__ = {
"model": "SentencePieceUnigram",
"replacement": replacement,
"add_prefix_space": add_prefix_space,
}
super().__init__(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCAmelCase__ ( self , UpperCAmelCase__ , UpperCAmelCase__ = 8000 , UpperCAmelCase__ = True , ):
SCREAMING_SNAKE_CASE__ = trainers.UnigramTrainer(
vocab_size=UpperCAmelCase__ , special_tokens=self.special_tokens_list , show_progress=UpperCAmelCase__ , )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE__ = [files]
self._tokenizer.train(UpperCAmelCase__ , trainer=UpperCAmelCase__ )
self.add_unk_id()
def lowerCAmelCase__ ( self , UpperCAmelCase__ , UpperCAmelCase__ = 8000 , UpperCAmelCase__ = True , ):
SCREAMING_SNAKE_CASE__ = trainers.UnigramTrainer(
vocab_size=UpperCAmelCase__ , special_tokens=self.special_tokens_list , show_progress=UpperCAmelCase__ , )
self._tokenizer.train_from_iterator(UpperCAmelCase__ , trainer=UpperCAmelCase__ )
self.add_unk_id()
def lowerCAmelCase__ ( self ):
SCREAMING_SNAKE_CASE__ = json.loads(self._tokenizer.to_str() )
SCREAMING_SNAKE_CASE__ = self.special_tokens["unk"]["id"]
SCREAMING_SNAKE_CASE__ = Tokenizer.from_str(json.dumps(UpperCAmelCase__ ) )
| 112 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_name__ : Tuple =sorted(numsa + numsa )
__magic_name__ , __magic_name__ : Optional[Any] =divmod(len(lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Union[str, Any] = [float(x) for x in input("Enter the elements of first array: ").split()]
UpperCAmelCase_ : Dict = [float(x) for x in input("Enter the elements of second array: ").split()]
print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
| 21 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__UpperCAmelCase : Optional[Any] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
__UpperCAmelCase : Any = parser.parse_args()
__UpperCAmelCase : str = "cpu"
__UpperCAmelCase : str = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"
__UpperCAmelCase : Optional[Any] = "path-to-your-trained-model"
__UpperCAmelCase : Dict = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__UpperCAmelCase : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__UpperCAmelCase : Optional[Any] = pipe.to(device)
# to channels last
__UpperCAmelCase : Optional[int] = pipe.unet.to(memory_format=torch.channels_last)
__UpperCAmelCase : List[Any] = pipe.vae.to(memory_format=torch.channels_last)
__UpperCAmelCase : int = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__UpperCAmelCase : Union[str, Any] = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__UpperCAmelCase : List[str] = torch.randn(2, 4, 6_4, 6_4)
__UpperCAmelCase : Optional[int] = torch.rand(1) * 9_9_9
__UpperCAmelCase : Any = torch.randn(2, 7_7, 7_6_8)
__UpperCAmelCase : List[Any] = (sample, timestep, encoder_hidden_status)
try:
__UpperCAmelCase : Optional[int] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__UpperCAmelCase : Tuple = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__UpperCAmelCase : List[str] = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__UpperCAmelCase : Union[str, Any] = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__UpperCAmelCase : str = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__UpperCAmelCase : Dict = 6_6_6
__UpperCAmelCase : List[Any] = torch.Generator(device).manual_seed(seed)
__UpperCAmelCase : List[str] = {"generator": generator}
if args.steps is not None:
__UpperCAmelCase : Union[str, Any] = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__UpperCAmelCase : int = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save("generated.png") | 241 | 0 |
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
UpperCamelCase : List[Any] = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase : Optional[Any] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCamelCase : int = [mem.copy() for i in range(6 )]
UpperCamelCase : Tuple = [mem.copy() for i in range(6 )]
UpperCamelCase : Tuple = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
UpperCamelCase : Optional[Any] = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
UpperCamelCase : Any = VGroup(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
UpperCamelCase : Any = Text("CPU" , font_size=24 )
UpperCamelCase : Optional[int] = Group(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCamelCase )
UpperCamelCase : Optional[int] = [mem.copy() for i in range(4 )]
UpperCamelCase : Optional[Any] = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
UpperCamelCase : int = Text("GPU" , font_size=24 )
UpperCamelCase : Any = Group(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(lowerCamelCase )
UpperCamelCase : Any = [mem.copy() for i in range(6 )]
UpperCamelCase : Tuple = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
UpperCamelCase : Tuple = Text("Model" , font_size=24 )
UpperCamelCase : Optional[Any] = Group(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.add(lowerCamelCase )
UpperCamelCase : Optional[int] = []
for i, rect in enumerate(lowerCamelCase ):
rect.set_stroke(lowerCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
UpperCamelCase : str = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(lowerCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=lowerCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=lowerCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=lowerCamelCase , buff=0.0 )
self.add(lowerCamelCase )
cpu_targs.append(lowerCamelCase )
UpperCamelCase : int = [mem.copy() for i in range(6 )]
UpperCamelCase : List[Any] = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
UpperCamelCase : Any = Text("Loaded Checkpoint" , font_size=24 )
UpperCamelCase : Dict = Group(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , aligned_edge=lowerCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
UpperCamelCase : Dict = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCamelCase : Dict = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowerCamelCase , lowerCamelCase )
UpperCamelCase : Dict = MarkupText(
f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
UpperCamelCase : List[Any] = MarkupText(
f'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCamelCase ) , Write(lowerCamelCase ) )
self.play(Write(lowerCamelCase , run_time=1 ) , Create(lowerCamelCase , run_time=1 ) )
UpperCamelCase : int = []
UpperCamelCase : Optional[Any] = []
for i, rect in enumerate(lowerCamelCase ):
UpperCamelCase : Dict = fill.copy().set_fill(lowerCamelCase , opacity=0.7 )
target.move_to(lowerCamelCase )
first_animations.append(GrowFromCenter(lowerCamelCase , run_time=1 ) )
UpperCamelCase : Tuple = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(lowerCamelCase , run_time=1.5 ) )
self.play(*lowerCamelCase )
self.play(*lowerCamelCase )
self.wait()
| 435 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class UpperCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = RoCBertTokenizer
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = filter_non_english
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
UpperCamelCase : Optional[int] = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
UpperCamelCase : Union[str, Any] = {}
UpperCamelCase : List[Any] = {}
for i, value in enumerate(lowerCamelCase ):
UpperCamelCase : Any = i
UpperCamelCase : List[Any] = i
UpperCamelCase : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCamelCase : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_shape_file"] )
UpperCamelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_pronunciation_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
with open(self.word_shape_file , "w" , encoding="utf-8" ) as word_shape_writer:
json.dump(lowerCamelCase , lowerCamelCase , ensure_ascii=lowerCamelCase )
with open(self.word_pronunciation_file , "w" , encoding="utf-8" ) as word_pronunciation_writer:
json.dump(lowerCamelCase , lowerCamelCase , ensure_ascii=lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCamelCase : List[str] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
UpperCamelCase : Dict = tokenizer.tokenize("你好[SEP]你是谁" )
self.assertListEqual(lowerCamelCase , ["你", "好", "[SEP]", "你", "是", "谁"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase : Dict = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
UpperCamelCase : Tuple = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
UpperCamelCase : int = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
UpperCamelCase : Any = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
UpperCamelCase : List[Any] = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCamelCase : Dict = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase : Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
UpperCamelCase : List[str] = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase : Tuple = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
UpperCamelCase : Optional[int] = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
UpperCamelCase : List[str] = {}
for i, token in enumerate(lowerCamelCase ):
UpperCamelCase : str = i
UpperCamelCase : int = RoCBertWordpieceTokenizer(vocab=lowerCamelCase , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowerCamelCase ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
if self.test_rust_tokenizer:
UpperCamelCase : List[str] = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(lowerCamelCase ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
UpperCamelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
UpperCamelCase : List[str] = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
UpperCamelCase : Tuple = tokenizer_r.encode_plus(
lowerCamelCase , return_attention_mask=lowerCamelCase , return_token_type_ids=lowerCamelCase , return_offsets_mapping=lowerCamelCase , add_special_tokens=lowerCamelCase , )
UpperCamelCase : int = tokenizer_r.do_lower_case if hasattr(lowerCamelCase , "do_lower_case" ) else False
UpperCamelCase : int = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase : int = ["的", "人", "有"]
UpperCamelCase : Any = "".join(lowerCamelCase )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
UpperCamelCase : Union[str, Any] = True
UpperCamelCase : Union[str, Any] = self.tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
UpperCamelCase : List[str] = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
UpperCamelCase : List[str] = tokenizer_p.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
UpperCamelCase : str = tokenizer_r.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
UpperCamelCase : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(lowerCamelCase )
UpperCamelCase : Optional[int] = tokenizer_p.convert_ids_to_tokens(lowerCamelCase )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowerCamelCase , lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
UpperCamelCase : Any = False
UpperCamelCase : Optional[int] = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
UpperCamelCase : Optional[int] = self.tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
UpperCamelCase : List[str] = tokenizer_r.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
UpperCamelCase : Any = tokenizer_p.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
UpperCamelCase : Tuple = tokenizer_r.convert_ids_to_tokens(lowerCamelCase )
UpperCamelCase : List[str] = tokenizer_p.convert_ids_to_tokens(lowerCamelCase )
# it is expected that only the first Chinese character is not preceded by "##".
UpperCamelCase : List[str] = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(lowerCamelCase )
]
self.assertListEqual(lowerCamelCase , lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
UpperCamelCase : Tuple = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
UpperCamelCase : Optional[Any] = tokenizer.encode("你好" , add_special_tokens=lowerCamelCase )
UpperCamelCase : Any = tokenizer.encode("你是谁" , add_special_tokens=lowerCamelCase )
UpperCamelCase : List[Any] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase )
UpperCamelCase : Optional[int] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase , lowerCamelCase )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
UpperCamelCase : Union[str, Any] = self.get_tokenizers(do_lower_case=lowerCamelCase )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
UpperCamelCase : List[Any] = "你好，你是谁"
UpperCamelCase : Union[str, Any] = tokenizer.tokenize(lowerCamelCase )
UpperCamelCase : int = tokenizer.convert_tokens_to_ids(lowerCamelCase )
UpperCamelCase : Optional[int] = tokenizer.convert_tokens_to_shape_ids(lowerCamelCase )
UpperCamelCase : Any = tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase )
UpperCamelCase : Optional[int] = tokenizer.prepare_for_model(
lowerCamelCase , lowerCamelCase , lowerCamelCase , add_special_tokens=lowerCamelCase )
UpperCamelCase : List[Any] = tokenizer.encode_plus(lowerCamelCase , add_special_tokens=lowerCamelCase )
self.assertEqual(lowerCamelCase , lowerCamelCase )
| 435 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _a ( _lowerCamelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
__snake_case , __snake_case : Optional[Any] = np.shape(_lowerCamelCase )
if rows != columns:
__snake_case : int = (
"""'table' has to be of square shaped array but got a """
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(_lowerCamelCase )
__snake_case : Any = np.zeros((rows, columns) )
__snake_case : List[Any] = np.zeros((rows, columns) )
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
__snake_case : Optional[Any] = sum(lower[i][k] * upper[k][j] for k in range(_lowerCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
__snake_case : Optional[Any] = (table[i][j] - total) / upper[j][j]
__snake_case : List[str] = 1
for j in range(_lowerCamelCase , _lowerCamelCase ):
__snake_case : Tuple = sum(lower[i][k] * upper[k][j] for k in range(_lowerCamelCase ) )
__snake_case : Optional[int] = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 26 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class snake_case__ ( unittest.TestCase):
def __init__( self : Optional[Any] , _A : int , _A : List[Any]=7 , _A : Tuple=3 , _A : int=18 , _A : Union[str, Any]=30 , _A : Any=4_00 , _A : List[Any]=True , _A : Optional[int]=None , _A : Optional[Any]=True , _A : Union[str, Any]=None , ) -> Optional[int]:
UpperCAmelCase_ : Optional[Any] = size if size is not None else {'''shortest_edge''': 20}
UpperCAmelCase_ : Dict = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
UpperCAmelCase_ : List[Any] = parent
UpperCAmelCase_ : List[Any] = batch_size
UpperCAmelCase_ : Optional[Any] = num_channels
UpperCAmelCase_ : Optional[Any] = image_size
UpperCAmelCase_ : Union[str, Any] = min_resolution
UpperCAmelCase_ : List[str] = max_resolution
UpperCAmelCase_ : Union[str, Any] = do_resize
UpperCAmelCase_ : Any = size
UpperCAmelCase_ : Union[str, Any] = do_center_crop
UpperCAmelCase_ : Any = crop_size
def A ( self : List[Any] ) -> List[Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class snake_case__ ( UpperCamelCase , unittest.TestCase):
a_ = MobileNetVaImageProcessor if is_vision_available() else None
def A ( self : List[Any] ) -> List[str]:
UpperCAmelCase_ : Dict = MobileNetVaImageProcessingTester(self )
@property
def A ( self : Optional[Any] ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : List[str] ) -> Dict:
UpperCAmelCase_ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , '''do_resize''' ) )
self.assertTrue(hasattr(_A , '''size''' ) )
self.assertTrue(hasattr(_A , '''do_center_crop''' ) )
self.assertTrue(hasattr(_A , '''crop_size''' ) )
def A ( self : Tuple ) -> Optional[int]:
UpperCAmelCase_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
UpperCAmelCase_ : List[str] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def A ( self : int ) -> List[str]:
pass
def A ( self : List[Any] ) -> Optional[Any]:
# Initialize image_processing
UpperCAmelCase_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A , Image.Image )
# Test not batched input
UpperCAmelCase_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ : List[Any] = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def A ( self : str ) -> List[str]:
# Initialize image_processing
UpperCAmelCase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , numpify=_A )
for image in image_inputs:
self.assertIsInstance(_A , np.ndarray )
# Test not batched input
UpperCAmelCase_ : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ : List[Any] = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def A ( self : Optional[int] ) -> Dict:
# Initialize image_processing
UpperCAmelCase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
# Test not batched input
UpperCAmelCase_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ : List[str] = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 541 | 0 |
def __lowerCAmelCase ()-> Union[str, Any]:
"""simple docstring"""
snake_case_ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
snake_case_ = 6
snake_case_ = 1
snake_case_ = 1901
snake_case_ = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
snake_case_ = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
snake_case_ = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
snake_case_ = day - days_per_month[month - 2]
if month > 12:
year += 1
snake_case_ = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution()) | 531 |
import copy
import re
class lowerCAmelCase_ :
'''simple docstring'''
__snake_case = "hp"
__snake_case = {}
__snake_case = None
@classmethod
def UpperCamelCase__ ( cls , _UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = prefix
snake_case_ = defaults
cls.build_naming_info()
@staticmethod
def UpperCamelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
if len(_UpperCAmelCase ) == 0:
return ""
snake_case_ = None
if any(char.isdigit() for char in word ):
raise Exception(F'''Parameters should not contain numbers: \'{word}\' contains a number''' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(_UpperCAmelCase ) + 1 ):
snake_case_ = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
snake_case_ = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(_UpperCAmelCase ):
snake_case_ = ''''''
while integer != 0:
snake_case_ = chr(ord('''A''' ) + integer % 10 ) + s
integer //= 10
return s
snake_case_ = 0
while True:
snake_case_ = word + '''#''' + int_to_alphabetic(_UpperCAmelCase )
if sword in info["reverse_short_word"]:
continue
else:
snake_case_ = sword
break
snake_case_ = short_word
snake_case_ = word
return short_word
@staticmethod
def UpperCamelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = param_name.split('''_''' )
snake_case_ = [TrialShortNamer.shortname_for_word(_UpperCAmelCase , _UpperCAmelCase ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
snake_case_ = ['''''', '''_''']
for separator in separators:
snake_case_ = separator.join(_UpperCAmelCase )
if shortname not in info["reverse_short_param"]:
snake_case_ = shortname
snake_case_ = param_name
return shortname
return param_name
@staticmethod
def UpperCamelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = TrialShortNamer.shortname_for_key(_UpperCAmelCase , _UpperCAmelCase )
snake_case_ = short_name
snake_case_ = param_name
@classmethod
def UpperCamelCase__ ( cls ):
if cls.NAMING_INFO is not None:
return
snake_case_ = {
'''short_word''': {},
'''reverse_short_word''': {},
'''short_param''': {},
'''reverse_short_param''': {},
}
snake_case_ = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(_UpperCAmelCase , _UpperCAmelCase )
snake_case_ = info
@classmethod
def UpperCamelCase__ ( cls , _UpperCAmelCase ):
cls.build_naming_info()
assert cls.PREFIX is not None
snake_case_ = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(F'''You should provide a default value for the param name {k} with value {v}''' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
snake_case_ = cls.NAMING_INFO['''short_param'''][k]
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = 1 if v else 0
snake_case_ = '''''' if isinstance(_UpperCAmelCase , (int, float) ) else '''-'''
snake_case_ = F'''{key}{sep}{v}'''
name.append(_UpperCAmelCase )
return "_".join(_UpperCAmelCase )
@classmethod
def UpperCamelCase__ ( cls , _UpperCAmelCase ):
snake_case_ = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
snake_case_ = []
else:
snake_case_ = repr.split('''_''' )
snake_case_ = {}
for value in values:
if "-" in value:
snake_case_ , snake_case_ = value.split('''-''' )
else:
snake_case_ = re.sub('''[0-9.]''' , '''''' , _UpperCAmelCase )
snake_case_ = float(re.sub('''[^0-9.]''' , '''''' , _UpperCAmelCase ) )
snake_case_ = cls.NAMING_INFO['''reverse_short_param'''][p_k]
snake_case_ = p_v
for k in cls.DEFAULTS:
if k not in parameters:
snake_case_ = cls.DEFAULTS[k]
return parameters | 531 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __a ( _snake_case ):
__UpperCamelCase : str = ['image_processor', 'feature_extractor']
__UpperCamelCase : List[str] = 'TvltImageProcessor'
__UpperCamelCase : int = 'TvltFeatureExtractor'
def __init__( self : List[str] ,lowerCamelCase : List[Any] ,lowerCamelCase : Any ):
'''simple docstring'''
super().__init__(image_processor=lowerCamelCase ,feature_extractor=lowerCamelCase )
__SCREAMING_SNAKE_CASE = image_processor
__SCREAMING_SNAKE_CASE = feature_extractor
def __call__( self : Union[str, Any] ,lowerCamelCase : Union[str, Any]=None ,lowerCamelCase : Optional[Any]=None ,lowerCamelCase : Any=None ,lowerCamelCase : int=None ,lowerCamelCase : Optional[int]=False ,lowerCamelCase : List[Any]=False ,*lowerCamelCase : List[str] ,**lowerCamelCase : Optional[int] ,):
'''simple docstring'''
if images is None and audio is None:
raise ValueError("""You need to specify either an `images` or `audio` input to process.""" )
__SCREAMING_SNAKE_CASE = None
if images is not None:
__SCREAMING_SNAKE_CASE = self.image_processor(lowerCamelCase ,mask_pixel=lowerCamelCase ,*lowerCamelCase ,**lowerCamelCase )
if images_mixed is not None:
__SCREAMING_SNAKE_CASE = self.image_processor(lowerCamelCase ,is_mixed=lowerCamelCase ,*lowerCamelCase ,**lowerCamelCase )
if audio is not None:
__SCREAMING_SNAKE_CASE = self.feature_extractor(
lowerCamelCase ,*lowerCamelCase ,sampling_rate=lowerCamelCase ,mask_audio=lowerCamelCase ,**lowerCamelCase )
__SCREAMING_SNAKE_CASE = {}
if audio is not None:
output_dict.update(lowerCamelCase )
if images is not None:
output_dict.update(lowerCamelCase )
if images_mixed_dict is not None:
output_dict.update(lowerCamelCase )
return output_dict
@property
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
__SCREAMING_SNAKE_CASE = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 109 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = 0
__lowerCAmelCase = False
__lowerCAmelCase = 3.0
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'''a''': 2} )
self.assertDictEqual(MockClass(a=2 , b=_UpperCAmelCase ).to_kwargs() , {'''a''': 2, '''b''': True} )
self.assertDictEqual(MockClass(a=2 , c=2.2_5 ).to_kwargs() , {'''a''': 2, '''c''': 2.2_5} )
@require_cuda
def _lowerCamelCase ( self ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
__a : List[Any] = GradScalerKwargs(init_scale=1024 , growth_factor=2 )
AcceleratorState._reset_state()
__a : int = Accelerator(mixed_precision='''fp16''' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
__a : Optional[Any] = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 1_0_2_4.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2000 )
self.assertEqual(scaler._enabled , _UpperCAmelCase )
@require_multi_gpu
def _lowerCamelCase ( self ):
__a : Dict = ['''torchrun''', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
execute_subprocess_async(_UpperCAmelCase , env=os.environ.copy() )
if __name__ == "__main__":
A = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
A = Accelerator(kwargs_handlers=[ddp_scaler])
A = torch.nn.Linear(100, 200)
A = accelerator.prepare(model)
# Check the values changed in kwargs
A = ''''''
A = model.bucket_bytes_cap // (1_024 * 1_024)
if observed_bucket_cap_map != 15:
error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg) | 52 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCAmelCase__ ( A_ , unittest.TestCase ):
__a = KandinskyImgaImgPipeline
__a = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
__a = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
__a = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
__a = False
@property
def lowercase ( self : List[str] ):
return 32
@property
def lowercase ( self : List[Any] ):
return 32
@property
def lowercase ( self : List[str] ):
return self.time_input_dim
@property
def lowercase ( self : Dict ):
return self.time_input_dim * 4
@property
def lowercase ( self : Union[str, Any] ):
return 100
@property
def lowercase ( self : Union[str, Any] ):
_snake_case = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def lowercase ( self : Tuple ):
torch.manual_seed(0 )
_snake_case = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
_snake_case = MultilingualCLIP(_lowerCamelCase )
_snake_case = text_encoder.eval()
return text_encoder
@property
def lowercase ( self : str ):
torch.manual_seed(0 )
_snake_case = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_snake_case = UNetaDConditionModel(**_lowerCamelCase )
return model
@property
def lowercase ( self : Optional[Any] ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowercase ( self : str ):
torch.manual_seed(0 )
_snake_case = VQModel(**self.dummy_movq_kwargs )
return model
def lowercase ( self : Optional[Any] ):
_snake_case = self.dummy_text_encoder
_snake_case = self.dummy_tokenizer
_snake_case = self.dummy_unet
_snake_case = self.dummy_movq
_snake_case = {
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_0_0_8_5,
'''beta_end''': 0.0_1_2,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
_snake_case = DDIMScheduler(**_lowerCamelCase )
_snake_case = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def lowercase ( self : Optional[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Any=0 ):
_snake_case = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
_snake_case = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_lowerCamelCase )
# create init_image
_snake_case = floats_tensor((1, 3, 64, 64) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
_snake_case = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_snake_case = Image.fromarray(np.uinta(_lowerCamelCase ) ).convert('''RGB''' ).resize((256, 256) )
if str(_lowerCamelCase ).startswith('''mps''' ):
_snake_case = torch.manual_seed(_lowerCamelCase )
else:
_snake_case = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
_snake_case = {
'''prompt''': '''horse''',
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def lowercase ( self : Optional[Any] ):
_snake_case = '''cpu'''
_snake_case = self.get_dummy_components()
_snake_case = self.pipeline_class(**_lowerCamelCase )
_snake_case = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
_snake_case = pipe(**self.get_dummy_inputs(_lowerCamelCase ) )
_snake_case = output.images
_snake_case = pipe(
**self.get_dummy_inputs(_lowerCamelCase ) , return_dict=_lowerCamelCase , )[0]
_snake_case = image[0, -3:, -3:, -1]
_snake_case = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_snake_case = np.array(
[0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
def lowercase ( self : int ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self : Union[str, Any] ):
_snake_case = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_img2img_frog.npy''' )
_snake_case = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_snake_case = '''A red cartoon frog, 4k'''
_snake_case = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_lowerCamelCase )
_snake_case = KandinskyImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1''' , torch_dtype=torch.floataa )
_snake_case = pipeline.to(_lowerCamelCase )
pipeline.set_progress_bar_config(disable=_lowerCamelCase )
_snake_case = torch.Generator(device='''cpu''' ).manual_seed(0 )
_snake_case , _snake_case = pipe_prior(
_lowerCamelCase , generator=_lowerCamelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_snake_case = pipeline(
_lowerCamelCase , image=_lowerCamelCase , image_embeds=_lowerCamelCase , negative_image_embeds=_lowerCamelCase , generator=_lowerCamelCase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
_snake_case = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCamelCase , _lowerCamelCase )
| 702 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int ) -> bool:
if num < 0:
return False
_snake_case = num
_snake_case = 0
while num > 0:
_snake_case = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 430 | 0 |
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
lowerCamelCase_ = str(bin(lowercase ) )[2:] # remove the leading "0b"
lowerCamelCase_ = str(bin(lowercase ) )[2:]
lowerCamelCase_ = max(len(lowercase ) , len(lowercase ) )
return "0b" + "".join(
str(int('1' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(lowercase ) , b_binary.zfill(lowercase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 70 |
import argparse
import json
import subprocess
def _SCREAMING_SNAKE_CASE ( lowercase : Dict , lowercase : List[str] ):
'''simple docstring'''
lowerCamelCase_ = []
lowerCamelCase_ = (
f"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
' https://api.github.com/repos/huggingface/transformers/actions/runners'
)
lowerCamelCase_ = subprocess.run(lowercase , shell=lowercase , stdout=subprocess.PIPE )
lowerCamelCase_ = output.stdout.decode('utf-8' )
lowerCamelCase_ = json.loads(lowercase )
lowerCamelCase_ = status['runners']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(lowercase )
# save the result so we can report them on Slack
with open('offline_runners.txt' , 'w' ) as fp:
fp.write(json.dumps(lowercase ) )
if len(lowercase ) > 0:
lowerCamelCase_ = '\n'.join([x['name'] for x in offline_runners] )
raise ValueError(f"""The following runners are offline:\n{failed}""" )
if __name__ == "__main__":
def _SCREAMING_SNAKE_CASE ( lowercase : List[str] ):
'''simple docstring'''
return values.split(',' )
lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--target_runners",
default=None,
type=list_str,
required=True,
help="Comma-separated list of runners to check status.",
)
parser.add_argument(
"--token", default=None, type=str, required=True, help="A token that has actions:read permission."
)
lowerCamelCase : Optional[int] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 70 | 1 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def UpperCAmelCase ( A__: List[str] , A__: Tuple ) -> int:
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
__lowerCamelCase : Dict = flax_key_tuple[:-1] + ('weight',)
__lowerCamelCase : Optional[int] = torch.permute(A__ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(A__ ):
# linear layer
__lowerCamelCase : int = flax_key_tuple[:-1] + ('weight',)
__lowerCamelCase : Tuple = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
__lowerCamelCase : List[str] = flax_key_tuple[:-1] + ('weight',)
return flax_key_tuple, flax_tensor
def UpperCAmelCase ( A__: Union[str, Any] , A__: Union[str, Any] , A__: Union[str, Any] ) -> List[Any]:
if "metadata" in layer:
__lowerCamelCase : Dict = layer.split('metadata' )
__lowerCamelCase : Dict = ''.join(split_layer[0] )[:-1]
__lowerCamelCase : Tuple = [tuple(('metadata' + split_layer[1]).split('/' ) )]
elif "kvstore" in layer:
__lowerCamelCase : Optional[int] = layer.split('kvstore' )
__lowerCamelCase : str = ''.join(split_layer[0] )[:-1]
__lowerCamelCase : Tuple = [tuple(('kvstore' + split_layer[1]).split('/' ) )]
else:
__lowerCamelCase : str = layer.split('/' )
__lowerCamelCase : Optional[Any] = '/'.join(split_layer[:-1] )
__lowerCamelCase : str = (split_layer[-1],)
if "kvstore/path" in layer:
__lowerCamelCase : Union[str, Any] = f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
elif "kvstore/driver" in layer:
__lowerCamelCase : int = 'file'
else:
__lowerCamelCase : Any = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def UpperCAmelCase ( A__: List[Any] , A__: int ) -> Union[str, Any]:
__lowerCamelCase : List[Any] = rename_keys(A__ )
__lowerCamelCase : Optional[Any] = {}
for k, v in current_block.items():
__lowerCamelCase : List[Any] = v
__lowerCamelCase : Any = new_current_block
torch.save(A__ , A__ )
def UpperCAmelCase ( A__: List[str] , A__: Tuple , A__: Union[str, Any] , A__: int , A__: str = WEIGHTS_NAME ) -> List[Any]:
__lowerCamelCase : Any = convert_file_size_to_int(A__ )
__lowerCamelCase : Tuple = []
__lowerCamelCase : List[str] = {}
__lowerCamelCase : Tuple = 0
__lowerCamelCase : str = 0
os.makedirs(A__ , exist_ok=A__ )
with gfile.GFile(switch_checkpoint_path + '/checkpoint' , 'rb' ) as fp:
__lowerCamelCase : Dict = serialization.msgpack_restore(fp.read() )['optimizer']['target']
__lowerCamelCase : Optional[Any] = flatten_dict(A__ , sep='/' )
__lowerCamelCase : Any = {}
for layer in checkpoint_info.keys():
__lowerCamelCase : Dict = get_key_and_tensorstore_dict(
A__ , A__ , A__ )
if curr_real_layer_name in all_layers:
__lowerCamelCase : Optional[int] = content
else:
__lowerCamelCase : Optional[Any] = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
__lowerCamelCase : Dict = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
__lowerCamelCase : str = torch.tensor(A__ )
__lowerCamelCase : List[str] = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
__lowerCamelCase : List[Any] = rename_base_flax_keys(tuple(key.split('/' ) ) , A__ )
__lowerCamelCase : str = '/'.join(A__ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
__lowerCamelCase : Tuple = os.path.join(
A__ , weights_name.replace('.bin' , f'''-{len(A__ )+1:05d}-of-???.bin''' ) )
rename_and_save_block(A__ , A__ )
sharded_state_dicts.append(current_block.keys() )
del current_block
__lowerCamelCase : Optional[Any] = {}
__lowerCamelCase : Optional[Any] = 0
__lowerCamelCase : Union[str, Any] = raw_weights.to(getattr(A__ , A__ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
__lowerCamelCase : List[str] = os.path.join(A__ , weights_name.replace('.bin' , f'''-{len(A__ )+1:05d}-of-???.bin''' ) )
rename_and_save_block(A__ , A__ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(A__ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
__lowerCamelCase : Union[str, Any] = {}
__lowerCamelCase : int = {}
for idx, shard in enumerate(A__ ):
__lowerCamelCase : Tuple = weights_name.replace(
'.bin' , f'''-{idx+1:05d}-of-{len(A__ ):05d}.bin''' ) # len(sharded_state_dicts):05d}
__lowerCamelCase : Union[str, Any] = os.path.join(A__ , weights_name.replace('.bin' , f'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(A__ , os.path.join(A__ , A__ ) )
__lowerCamelCase : Dict = shard
for key in shard:
__lowerCamelCase : str = shard_file
# Add the metadata
__lowerCamelCase : List[str] = {'total_size': total_size}
__lowerCamelCase : str = {'metadata': metadata, 'weight_map': weight_map}
with open(os.path.join(A__ , A__ ) , 'w' , encoding='utf-8' ) as f:
__lowerCamelCase : Union[str, Any] = json.dumps(A__ , indent=2 , sort_keys=A__ ) + '\n'
f.write(A__ )
return metadata, index
if __name__ == "__main__":
a_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--switch_t5x_checkpoint_path''',
default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
type=str,
required=False,
help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
)
parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
type=str,
required=False,
help='''Path to the output pytorch model.''',
)
a_ : Optional[Any] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def UpperCAmelCase ( ) -> List[Any]:
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
__lowerCamelCase : str = SwitchTransformersConfig.from_pretrained('google/switch-base-8' )
config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted' )
__lowerCamelCase : str = SwitchTransformersForConditionalGeneration.from_pretrained(
'/home/arthur_huggingface_co/transformers/switch_converted' , device_map='auto' )
__lowerCamelCase : Dict = TaTokenizer.from_pretrained('t5-small' )
__lowerCamelCase : List[Any] = 'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'
__lowerCamelCase : Any = tokenizer(A__ , return_tensors='pt' ).input_ids
__lowerCamelCase : List[Any] = model.generate(A__ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 710 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
a_ : Tuple = logging.get_logger(__name__)
# General docstring
a_ : List[str] = '''PoolFormerConfig'''
# Base docstring
a_ : Optional[Any] = '''sail/poolformer_s12'''
a_ : List[Any] = [1, 5_12, 7, 7]
# Image classification docstring
a_ : Any = '''sail/poolformer_s12'''
a_ : Optional[int] = '''tabby, tabby cat'''
a_ : Optional[Any] = [
'''sail/poolformer_s12''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def UpperCAmelCase ( A__: Optional[Any] , A__: float = 0.0 , A__: bool = False ) -> Tuple:
if drop_prob == 0.0 or not training:
return input
__lowerCamelCase : Dict = 1 - drop_prob
__lowerCamelCase : List[Any] = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
__lowerCamelCase : List[Any] = keep_prob + torch.rand(A__ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
__lowerCamelCase : Any = input.div(A__ ) * random_tensor
return output
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a = None ):
super().__init__()
__lowerCamelCase : int = drop_prob
def snake_case_ ( self , __a ):
return drop_path(__a , self.drop_prob , self.training )
def snake_case_ ( self ):
return "p={}".format(self.drop_prob )
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a , __a , __a , __a , __a , __a=None ):
super().__init__()
__lowerCamelCase : int = patch_size if isinstance(__a , collections.abc.Iterable ) else (patch_size, patch_size)
__lowerCamelCase : int = stride if isinstance(__a , collections.abc.Iterable ) else (stride, stride)
__lowerCamelCase : Optional[int] = padding if isinstance(__a , collections.abc.Iterable ) else (padding, padding)
__lowerCamelCase : Optional[Any] = nn.Convad(__a , __a , kernel_size=__a , stride=__a , padding=__a )
__lowerCamelCase : List[str] = norm_layer(__a ) if norm_layer else nn.Identity()
def snake_case_ ( self , __a ):
__lowerCamelCase : List[Any] = self.projection(__a )
__lowerCamelCase : Dict = self.norm(__a )
return embeddings
class __lowercase( nn.GroupNorm ):
'''simple docstring'''
def __init__( self , __a , **__a ):
super().__init__(1 , __a , **__a )
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__()
__lowerCamelCase : str = nn.AvgPoolad(__a , stride=1 , padding=pool_size // 2 , count_include_pad=__a )
def snake_case_ ( self , __a ):
return self.pool(__a ) - hidden_states
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a , __a , __a , __a ):
super().__init__()
__lowerCamelCase : Any = nn.Convad(__a , __a , 1 )
__lowerCamelCase : Dict = nn.Convad(__a , __a , 1 )
__lowerCamelCase : List[Any] = PoolFormerDropPath(__a )
if isinstance(config.hidden_act , __a ):
__lowerCamelCase : List[str] = ACTaFN[config.hidden_act]
else:
__lowerCamelCase : str = config.hidden_act
def snake_case_ ( self , __a ):
__lowerCamelCase : int = self.conva(__a )
__lowerCamelCase : Dict = self.act_fn(__a )
__lowerCamelCase : List[str] = self.drop(__a )
__lowerCamelCase : int = self.conva(__a )
__lowerCamelCase : str = self.drop(__a )
return hidden_states
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a , __a , __a , __a , __a , __a ):
super().__init__()
__lowerCamelCase : Tuple = PoolFormerPooling(__a )
__lowerCamelCase : Union[str, Any] = PoolFormerOutput(__a , __a , __a , __a )
__lowerCamelCase : List[Any] = PoolFormerGroupNorm(__a )
__lowerCamelCase : List[Any] = PoolFormerGroupNorm(__a )
# Useful for training neural nets
__lowerCamelCase : Any = PoolFormerDropPath(__a ) if drop_path > 0.0 else nn.Identity()
__lowerCamelCase : Tuple = config.use_layer_scale
if config.use_layer_scale:
__lowerCamelCase : List[str] = nn.Parameter(
config.layer_scale_init_value * torch.ones((__a) ) , requires_grad=__a )
__lowerCamelCase : Optional[int] = nn.Parameter(
config.layer_scale_init_value * torch.ones((__a) ) , requires_grad=__a )
def snake_case_ ( self , __a ):
if self.use_layer_scale:
__lowerCamelCase : Union[str, Any] = self.pooling(self.before_norm(__a ) )
__lowerCamelCase : Any = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
__lowerCamelCase : Optional[Any] = hidden_states + self.drop_path(__a )
__lowerCamelCase : Tuple = ()
__lowerCamelCase : Optional[Any] = self.output(self.after_norm(__a ) )
__lowerCamelCase : List[Any] = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
__lowerCamelCase : List[Any] = hidden_states + self.drop_path(__a )
__lowerCamelCase : Optional[Any] = (output,) + outputs
return outputs
else:
__lowerCamelCase : Tuple = self.drop_path(self.pooling(self.before_norm(__a ) ) )
# First residual connection
__lowerCamelCase : List[str] = pooling_output + hidden_states
__lowerCamelCase : int = ()
# Second residual connection inside the PoolFormerOutput block
__lowerCamelCase : List[str] = self.drop_path(self.output(self.after_norm(__a ) ) )
__lowerCamelCase : str = hidden_states + layer_output
__lowerCamelCase : int = (output,) + outputs
return outputs
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__()
__lowerCamelCase : int = config
# stochastic depth decay rule
__lowerCamelCase : int = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
__lowerCamelCase : List[str] = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
__lowerCamelCase : Optional[int] = nn.ModuleList(__a )
# Transformer blocks
__lowerCamelCase : Any = []
__lowerCamelCase : int = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
__lowerCamelCase : Optional[int] = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
__a , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(__a ) )
__lowerCamelCase : str = nn.ModuleList(__a )
def snake_case_ ( self , __a , __a=False , __a=True ):
__lowerCamelCase : Union[str, Any] = () if output_hidden_states else None
__lowerCamelCase : int = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
__lowerCamelCase , __lowerCamelCase : Any = layers
# Get patch embeddings from hidden_states
__lowerCamelCase : Any = embedding_layer(__a )
# Send the embeddings through the blocks
for _, blk in enumerate(__a ):
__lowerCamelCase : Optional[int] = blk(__a )
__lowerCamelCase : Tuple = layer_outputs[0]
if output_hidden_states:
__lowerCamelCase : Union[str, Any] = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=__a , hidden_states=__a )
class __lowercase( lowercase__ ):
'''simple docstring'''
__a : Tuple = PoolFormerConfig
__a : Tuple = 'poolformer'
__a : Optional[int] = 'pixel_values'
__a : Optional[Any] = True
def snake_case_ ( self , __a ):
if isinstance(__a , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__a , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def snake_case_ ( self , __a , __a=False ):
if isinstance(__a , __a ):
__lowerCamelCase : Union[str, Any] = value
a_ : Union[str, Any] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
a_ : List[str] = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
'''
@add_start_docstrings(
'The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.' , lowercase__ , )
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__(__a )
__lowerCamelCase : Optional[Any] = config
__lowerCamelCase : Any = PoolFormerEncoder(__a )
# Initialize weights and apply final processing
self.post_init()
def snake_case_ ( self ):
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__a , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def snake_case_ ( self , __a = None , __a = None , __a = None , ):
__lowerCamelCase : Union[str, Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCamelCase : Dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
__lowerCamelCase : Any = self.encoder(
__a , output_hidden_states=__a , return_dict=__a , )
__lowerCamelCase : int = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=__a , hidden_states=encoder_outputs.hidden_states , )
class __lowercase( nn.Module ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__()
__lowerCamelCase : Optional[Any] = nn.Linear(config.hidden_size , config.hidden_size )
def snake_case_ ( self , __a ):
__lowerCamelCase : List[Any] = self.dense(__a )
return output
@add_start_docstrings(
'\n PoolFormer Model transformer with an image classification head on top\n ' , lowercase__ , )
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__(__a )
__lowerCamelCase : str = config.num_labels
__lowerCamelCase : Optional[Any] = PoolFormerModel(__a )
# Final norm
__lowerCamelCase : str = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
__lowerCamelCase : Optional[Any] = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__a , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def snake_case_ ( self , __a = None , __a = None , __a = None , __a = None , ):
__lowerCamelCase : Any = return_dict if return_dict is not None else self.config.use_return_dict
__lowerCamelCase : Tuple = self.poolformer(
__a , output_hidden_states=__a , return_dict=__a , )
__lowerCamelCase : int = outputs[0]
__lowerCamelCase : Optional[int] = self.classifier(self.norm(__a ).mean([-2, -1] ) )
__lowerCamelCase : Union[str, Any] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__lowerCamelCase : Any = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__lowerCamelCase : Any = 'single_label_classification'
else:
__lowerCamelCase : Optional[Any] = 'multi_label_classification'
if self.config.problem_type == "regression":
__lowerCamelCase : int = MSELoss()
if self.num_labels == 1:
__lowerCamelCase : Optional[Any] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__lowerCamelCase : Optional[Any] = loss_fct(__a , __a )
elif self.config.problem_type == "single_label_classification":
__lowerCamelCase : Tuple = CrossEntropyLoss()
__lowerCamelCase : int = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__lowerCamelCase : List[Any] = BCEWithLogitsLoss()
__lowerCamelCase : Optional[Any] = loss_fct(__a , __a )
if not return_dict:
__lowerCamelCase : Optional[Any] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=__a , logits=__a , hidden_states=outputs.hidden_states )
| 263 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__ :
'''simple docstring'''
def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Optional[int]=13 , _SCREAMING_SNAKE_CASE: Optional[Any]=32 , _SCREAMING_SNAKE_CASE: int=2 , _SCREAMING_SNAKE_CASE: Union[str, Any]=3 , _SCREAMING_SNAKE_CASE: List[str]=16 , _SCREAMING_SNAKE_CASE: Union[str, Any]=[32, 64, 128] , _SCREAMING_SNAKE_CASE: Dict=[1, 2, 1] , _SCREAMING_SNAKE_CASE: Optional[Any]=[2, 2, 4] , _SCREAMING_SNAKE_CASE: str=2 , _SCREAMING_SNAKE_CASE: Optional[int]=2.0 , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , _SCREAMING_SNAKE_CASE: Dict=0.0 , _SCREAMING_SNAKE_CASE: Optional[int]=0.0 , _SCREAMING_SNAKE_CASE: Dict=0.1 , _SCREAMING_SNAKE_CASE: Dict="gelu" , _SCREAMING_SNAKE_CASE: int=False , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , _SCREAMING_SNAKE_CASE: List[str]=0.02 , _SCREAMING_SNAKE_CASE: Union[str, Any]=1e-5 , _SCREAMING_SNAKE_CASE: str=True , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: str=True , _SCREAMING_SNAKE_CASE: Optional[int]=10 , _SCREAMING_SNAKE_CASE: Optional[int]=8 , _SCREAMING_SNAKE_CASE: List[str]=["stage1", "stage2"] , _SCREAMING_SNAKE_CASE: Optional[int]=[1, 2] , ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = parent
__lowerCAmelCase : Any = batch_size
__lowerCAmelCase : Union[str, Any] = image_size
__lowerCAmelCase : str = patch_size
__lowerCAmelCase : List[str] = num_channels
__lowerCAmelCase : List[str] = embed_dim
__lowerCAmelCase : int = hidden_sizes
__lowerCAmelCase : str = depths
__lowerCAmelCase : Union[str, Any] = num_heads
__lowerCAmelCase : Tuple = window_size
__lowerCAmelCase : str = mlp_ratio
__lowerCAmelCase : Any = qkv_bias
__lowerCAmelCase : int = hidden_dropout_prob
__lowerCAmelCase : List[str] = attention_probs_dropout_prob
__lowerCAmelCase : Optional[int] = drop_path_rate
__lowerCAmelCase : Any = hidden_act
__lowerCAmelCase : int = use_absolute_embeddings
__lowerCAmelCase : Union[str, Any] = patch_norm
__lowerCAmelCase : Tuple = layer_norm_eps
__lowerCAmelCase : Optional[int] = initializer_range
__lowerCAmelCase : Optional[Any] = is_training
__lowerCAmelCase : Union[str, Any] = scope
__lowerCAmelCase : Any = use_labels
__lowerCAmelCase : Optional[Any] = type_sequence_label_size
__lowerCAmelCase : Tuple = encoder_stride
__lowerCAmelCase : Optional[Any] = out_features
__lowerCAmelCase : List[Any] = out_indices
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__lowerCAmelCase : List[Any] = None
if self.use_labels:
__lowerCAmelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__lowerCAmelCase : Dict = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> Union[str, Any]:
"""simple docstring"""
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Tuple) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[str] = FocalNetModel(config=_lowerCamelCase)
model.to(_lowerCamelCase)
model.eval()
__lowerCAmelCase : Union[str, Any] = model(_lowerCamelCase)
__lowerCAmelCase : int = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
__lowerCAmelCase : Dict = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim))
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: List[Any]) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = FocalNetBackbone(config=_lowerCamelCase)
model.to(_lowerCamelCase)
model.eval()
__lowerCAmelCase : str = model(_lowerCamelCase)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.image_size, 8, 8])
# verify channels
self.parent.assertEqual(len(model.channels) , len(config.out_features))
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1])
# verify backbone works with out_features=None
__lowerCAmelCase : Optional[Any] = None
__lowerCAmelCase : Optional[int] = FocalNetBackbone(config=_lowerCamelCase)
model.to(_lowerCamelCase)
model.eval()
__lowerCAmelCase : int = model(_lowerCamelCase)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.image_size * 2, 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels) , 1)
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]])
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: int) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = FocalNetForMaskedImageModeling(config=_lowerCamelCase)
model.to(_lowerCamelCase)
model.eval()
__lowerCAmelCase : List[str] = model(_lowerCamelCase)
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
__lowerCAmelCase : Optional[int] = 1
__lowerCAmelCase : List[Any] = FocalNetForMaskedImageModeling(_lowerCamelCase)
model.to(_lowerCamelCase)
model.eval()
__lowerCAmelCase : Tuple = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
__lowerCAmelCase : Tuple = model(_lowerCamelCase)
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size))
def _SCREAMING_SNAKE_CASE ( self: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Any) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Any = self.type_sequence_label_size
__lowerCAmelCase : Tuple = FocalNetForImageClassification(_lowerCamelCase)
model.to(_lowerCamelCase)
model.eval()
__lowerCAmelCase : Union[str, Any] = model(_lowerCamelCase , labels=_lowerCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
__lowerCAmelCase : Tuple = 1
__lowerCAmelCase : Optional[Any] = FocalNetForImageClassification(_lowerCamelCase)
model.to(_lowerCamelCase)
model.eval()
__lowerCAmelCase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
__lowerCAmelCase : Tuple = model(_lowerCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Tuple = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[Any] = config_and_inputs
__lowerCAmelCase : List[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class A__ ( __a , __a , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Any = FocalNetModelTester(self)
__lowerCAmelCase : str = ConfigTester(self , config_class=_lowerCamelCase , embed_dim=37 , has_text_modality=_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( self: Any) -> Optional[int]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _SCREAMING_SNAKE_CASE ( self: int) -> List[str]:
"""simple docstring"""
return
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> str:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( self: Any) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase)
@unittest.skip(reason="FocalNet does not use inputs_embeds")
def _SCREAMING_SNAKE_CASE ( self: Any) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking")
def _SCREAMING_SNAKE_CASE ( self: int) -> str:
"""simple docstring"""
pass
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__lowerCAmelCase : List[str] = model_class(_lowerCamelCase)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
__lowerCAmelCase : str = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCamelCase , nn.Linear))
def _SCREAMING_SNAKE_CASE ( self: int) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__lowerCAmelCase : Optional[Any] = model_class(_lowerCamelCase)
__lowerCAmelCase : Any = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase : Tuple = [*signature.parameters.keys()]
__lowerCAmelCase : str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( self: List[Any] , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Tuple) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = model_class(_lowerCamelCase)
model.to(_lowerCamelCase)
model.eval()
with torch.no_grad():
__lowerCAmelCase : str = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase))
__lowerCAmelCase : List[str] = outputs.hidden_states
__lowerCAmelCase : List[Any] = getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths) + 1)
self.assertEqual(len(_lowerCamelCase) , _lowerCamelCase)
# FocalNet has a different seq_length
__lowerCAmelCase : Any = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
__lowerCAmelCase : Optional[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [num_patches, self.model_tester.embed_dim] , )
__lowerCAmelCase : Optional[int] = outputs.reshaped_hidden_states
self.assertEqual(len(_lowerCamelCase) , _lowerCamelCase)
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[Any] = reshaped_hidden_states[0].shape
__lowerCAmelCase : Union[str, Any] = (
reshaped_hidden_states[0].view(_lowerCamelCase , _lowerCamelCase , height * width).permute(0 , 2 , 1)
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:]) , [num_patches, self.model_tester.embed_dim] , )
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> Any:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Dict = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
__lowerCAmelCase : Optional[int] = True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCAmelCase : Any = True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( self: Any) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Optional[int] = 3
__lowerCAmelCase : str = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowerCAmelCase : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
__lowerCAmelCase : Optional[int] = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowerCAmelCase : Optional[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
__lowerCAmelCase : Union[str, Any] = True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , (padded_height, padded_width))
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCAmelCase : Union[str, Any] = True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , (padded_height, padded_width))
@slow
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Any:
"""simple docstring"""
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase : Tuple = FocalNetModel.from_pretrained(_lowerCamelCase)
self.assertIsNotNone(_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( self: int) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Tuple = _config_zero_init(_lowerCamelCase)
for model_class in self.all_model_classes:
__lowerCAmelCase : Tuple = model_class(config=_lowerCamelCase)
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class A__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Any:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny") if is_vision_available() else None
@slow
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> str:
"""simple docstring"""
__lowerCAmelCase : str = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny").to(_lowerCamelCase)
__lowerCAmelCase : Dict = self.default_image_processor
__lowerCAmelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
__lowerCAmelCase : str = image_processor(images=_lowerCamelCase , return_tensors="pt").to(_lowerCamelCase)
# forward pass
with torch.no_grad():
__lowerCAmelCase : Union[str, Any] = model(**_lowerCamelCase)
# verify the logits
__lowerCAmelCase : Any = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape , _lowerCamelCase)
__lowerCAmelCase : int = torch.tensor([0.2166, -0.4368, 0.2191]).to(_lowerCamelCase)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1e-4))
self.assertTrue(outputs.logits.argmax(dim=-1).item() , 281)
@require_torch
class A__ ( __a , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (FocalNetBackbone,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = FocalNetConfig
SCREAMING_SNAKE_CASE = False
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Any:
"""simple docstring"""
__lowerCAmelCase : Dict = FocalNetModelTester(self) | 293 | '''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class __UpperCAmelCase :
def __init__( self ):
lowerCAmelCase_ = psutil.Process()
lowerCAmelCase_ = False
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = -1
while True:
lowerCAmelCase_ = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = True
lowerCAmelCase_ = threading.Thread(target=self.peak_monitor )
lowerCAmelCase_ = True
self.thread.start()
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = False
self.thread.join()
return self.cpu_memory_peak
A_ : List[str] =PeakCPUMemory()
def snake_case_ ( ) -> Tuple:
# Time
lowerCAmelCase_ = {'''time''': time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
lowerCAmelCase_ = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count()):
lowerCAmelCase_ = torch.cuda.memory_allocated(__snake_case)
torch.cuda.reset_peak_memory_stats()
return measures
def snake_case_ ( __snake_case : Any) -> List[str]:
# Time
lowerCAmelCase_ = {'''time''': time.time() - start_measures['''time''']}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
lowerCAmelCase_ = (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
lowerCAmelCase_ = (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20
# GPU mem
for i in range(torch.cuda.device_count()):
lowerCAmelCase_ = (torch.cuda.memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
lowerCAmelCase_ = (torch.cuda.max_memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
return measures
def snake_case_ ( __snake_case : Dict , __snake_case : Optional[int]) -> Dict:
print(F'''{description}:''')
print(F'''- Time: {measures['time']:.2f}s''')
for i in range(torch.cuda.device_count()):
print(F'''- GPU {i} allocated: {measures[str(__snake_case)]:.2f}MiB''')
lowerCAmelCase_ = measures[F'''{i}-peak''']
print(F'''- GPU {i} peak: {peak:.2f}MiB''')
print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''')
print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''')
| 274 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCamelCase_ = datasets.utils.logging.get_logger(__name__)
class __a ( folder_based_builder.FolderBasedBuilderConfig ):
"""simple docstring"""
_A : bool = None
_A : bool = None
class __a ( folder_based_builder.FolderBasedBuilder ):
"""simple docstring"""
_A : List[str] = datasets.Audio()
_A : Dict = "audio"
_A : Union[str, Any] = AudioFolderConfig
_A : List[str] # definition at the bottom of the script
_A : Optional[Any] = AudioClassification(audio_column="audio" , label_column="label" )
lowerCamelCase_ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
lowerCamelCase_ = AUDIO_EXTENSIONS
| 588 |
import warnings
from functools import wraps
from typing import Callable
def UpperCAmelCase_ ( __UpperCamelCase ):
@wraps(__UpperCamelCase )
def _inner_fn(*__UpperCamelCase, **__UpperCamelCase ):
warnings.warn(
(f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future."""), __UpperCamelCase, )
return fn(*__UpperCamelCase, **__UpperCamelCase )
return _inner_fn
| 588 | 1 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowercase__( __SCREAMING_SNAKE_CASE : Optional[int] ):
return getitem, k
def lowercase__( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Dict ):
return setitem, k, v
def lowercase__( __SCREAMING_SNAKE_CASE : List[str] ):
return delitem, k
def lowercase__( __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] , *__SCREAMING_SNAKE_CASE : str ):
try:
return fun(A__ , *A__ ), None
except Exception as e:
return None, e
__SCREAMING_SNAKE_CASE =(
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__SCREAMING_SNAKE_CASE =[
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__SCREAMING_SNAKE_CASE =[
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__SCREAMING_SNAKE_CASE =[
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__SCREAMING_SNAKE_CASE =[
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__SCREAMING_SNAKE_CASE =[
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
'operations' , (
pytest.param(_add_items , id='add items' ),
pytest.param(_overwrite_items , id='overwrite items' ),
pytest.param(_delete_items , id='delete items' ),
pytest.param(_access_absent_items , id='access absent items' ),
pytest.param(_add_with_resize_up , id='add with resize up' ),
pytest.param(_add_with_resize_down , id='add with resize down' ),
) , )
def lowercase__( __SCREAMING_SNAKE_CASE : Union[str, Any] ):
lowercase_ : List[Any] = HashMap(initial_block_size=4 )
lowercase_ : List[Any] = {}
for _, (fun, *args) in enumerate(A__ ):
lowercase_ : Optional[Any] = _run_operation(A__ , A__ , *A__ )
lowercase_ : Optional[int] = _run_operation(A__ , A__ , *A__ )
assert my_res == py_res
assert str(A__ ) == str(A__ )
assert set(A__ ) == set(A__ )
assert len(A__ ) == len(A__ )
assert set(my.items() ) == set(py.items() )
def lowercase__( ):
def is_public(__SCREAMING_SNAKE_CASE : Optional[int] ) -> bool:
return not name.startswith('_' )
lowercase_ : int = {name for name in dir({} ) if is_public(A__ )}
lowercase_ : Any = {name for name in dir(HashMap() ) if is_public(A__ )}
assert dict_public_names > hash_public_names
| 425 |
"""simple docstring"""
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = torch.device('''cpu''')
def snake_case ( ):
UpperCAmelCase_ : str = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ : str = Image.open(requests.get(A__ ,stream=A__ ).raw )
return im
def snake_case ( A__ ):
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02] )
def snake_case ( A__ ,A__ ,A__ ):
UpperCAmelCase_ : Tuple = dct.pop(A__ )
UpperCAmelCase_ : Optional[Any] = val
def snake_case ( A__ ):
UpperCAmelCase_ : List[str] = []
for k in state_dict.keys():
UpperCAmelCase_ : Union[str, Any] = k
if ".pwconv" in k:
UpperCAmelCase_ : Dict = k_new.replace(".pwconv" ,".point_wise_conv" )
if ".dwconv" in k:
UpperCAmelCase_ : Any = k_new.replace(".dwconv" ,".depth_wise_conv" )
if ".Proj." in k:
UpperCAmelCase_ : Dict = k_new.replace(".Proj." ,".proj." )
if "patch_embed" in k_new:
UpperCAmelCase_ : Tuple = k_new.replace("patch_embed" ,"swiftformer.patch_embed.patch_embedding" )
if "network" in k_new:
UpperCAmelCase_ : List[Any] = k_new.split("." )
if ls[2].isdigit():
UpperCAmelCase_ : Tuple = "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:] )
else:
UpperCAmelCase_ : Optional[Any] = k_new.replace("network" ,"swiftformer.encoder.network" )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def snake_case ( A__ ,A__ ,A__ ):
UpperCAmelCase_ : Optional[int] = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
UpperCAmelCase_ : Optional[Any] = 10_00
UpperCAmelCase_ : str = "huggingface/label-files"
UpperCAmelCase_ : str = "imagenet-1k-id2label.json"
UpperCAmelCase_ : List[str] = json.load(open(hf_hub_download(A__ ,A__ ,repo_type="dataset" ) ,"r" ) )
UpperCAmelCase_ : Tuple = {int(A__ ): v for k, v in idalabel.items()}
UpperCAmelCase_ : List[Any] = idalabel
UpperCAmelCase_ : Optional[Any] = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
UpperCAmelCase_ : Tuple = [3, 3, 6, 4]
UpperCAmelCase_ : str = [48, 56, 1_12, 2_20]
elif swiftformer_name == "swiftformer_s":
UpperCAmelCase_ : Optional[Any] = [3, 3, 9, 6]
UpperCAmelCase_ : Optional[Any] = [48, 64, 1_68, 2_24]
elif swiftformer_name == "swiftformer_l1":
UpperCAmelCase_ : int = [4, 3, 10, 5]
UpperCAmelCase_ : Union[str, Any] = [48, 96, 1_92, 3_84]
elif swiftformer_name == "swiftformer_l3":
UpperCAmelCase_ : Dict = [4, 4, 12, 6]
UpperCAmelCase_ : Optional[int] = [64, 1_28, 3_20, 5_12]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith("https" ):
UpperCAmelCase_ : List[Any] = torch.hub.load_state_dict_from_url(A__ ,map_location="cpu" ,check_hash=A__ )
else:
UpperCAmelCase_ : Any = torch.load(A__ ,map_location="cpu" )
UpperCAmelCase_ : List[str] = checkpoint
UpperCAmelCase_ : Dict = create_rename_keys(A__ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(A__ ,A__ ,A__ )
# load HuggingFace model
UpperCAmelCase_ : Optional[int] = SwiftFormerForImageClassification(A__ ).eval()
hf_model.load_state_dict(A__ )
# prepare test inputs
UpperCAmelCase_ : Tuple = prepare_img()
UpperCAmelCase_ : int = ViTImageProcessor.from_pretrained("preprocessor_config" )
UpperCAmelCase_ : int = processor(images=A__ ,return_tensors="pt" )
# compare outputs from both models
UpperCAmelCase_ : List[Any] = get_expected_output(A__ )
UpperCAmelCase_ : int = hf_model(inputs["pixel_values"] ).logits
assert hf_logits.shape == torch.Size([1, 10_00] )
assert torch.allclose(hf_logits[0, 0:5] ,A__ ,atol=1e-3 )
Path(A__ ).mkdir(exist_ok=A__ )
print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""" )
hf_model.save_pretrained(A__ )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swiftformer_name''',
default='''swiftformer_xs''',
choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
type=str,
help='''Name of the SwiftFormer model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''./converted_outputs/''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')
lowerCamelCase_ = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 95 | 0 |
from pathlib import Path
import fire
def SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : str , snake_case_ : int ):
snake_case__ : Tuple = Path(snake_case_ )
snake_case__ : Optional[Any] = Path(snake_case_ )
dest_dir.mkdir(exist_ok=snake_case_ )
for path in src_dir.iterdir():
snake_case__ : Tuple = [x.rstrip() for x in list(path.open().readlines() )][:n]
snake_case__ : Optional[int] = dest_dir.joinpath(path.name )
print(snake_case_ )
dest_path.open("w" ).write("\n".join(snake_case_ ) )
if __name__ == "__main__":
fire.Fire(minify)
| 25 |
def SCREAMING_SNAKE_CASE ( snake_case_ : list ):
if len(snake_case_ ) <= 1:
return lst
snake_case__ : List[Any] = 1
while i < len(snake_case_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
snake_case__, snake_case__ : Tuple = lst[i], lst[i - 1]
i -= 1
if i == 0:
snake_case__ : Union[str, Any] = 1
return lst
if __name__ == "__main__":
__lowerCamelCase : Dict = input("""Enter numbers separated by a comma:\n""").strip()
__lowerCamelCase : Tuple = [int(item) for item in user_input.split(""",""")]
print(gnome_sort(unsorted))
| 25 | 1 |
def _lowerCamelCase( __snake_case , __snake_case ) -> str:
if not (isinstance(__snake_case , __snake_case ) and isinstance(__snake_case , __snake_case )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
__snake_case = len(__snake_case )
__snake_case = len(__snake_case )
__snake_case = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
__snake_case = 0
__snake_case = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
__snake_case = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
__snake_case = i
__snake_case = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 524 | # limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'pipelines_utils',
'0.22.0',
'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.',
standard_warn=False,
stacklevel=3,
)
| 524 | 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
UpperCamelCase_ = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
UpperCamelCase_ = logging.WARNING
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : str = os.getenv("""DATASETS_VERBOSITY""" , _a )
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 lowerCamelCase_ ( ):
'''simple docstring'''
return __name__.split(""".""" )[0]
def lowerCamelCase_ ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : Any = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def lowerCamelCase_ ( _a : Optional[str] = None ):
'''simple docstring'''
if name is None:
UpperCAmelCase_ : List[Any] = _get_library_name()
return logging.getLogger(_a )
def lowerCamelCase_ ( ):
'''simple docstring'''
return _get_library_root_logger().getEffectiveLevel()
def lowerCamelCase_ ( _a : int ):
'''simple docstring'''
_get_library_root_logger().setLevel(_a )
def lowerCamelCase_ ( ):
'''simple docstring'''
return set_verbosity(_a )
def lowerCamelCase_ ( ):
'''simple docstring'''
return set_verbosity(_a )
def lowerCamelCase_ ( ):
'''simple docstring'''
return set_verbosity(_a )
def lowerCamelCase_ ( ):
'''simple docstring'''
return set_verbosity(_a )
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = False
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class _snake_case :
'''simple docstring'''
def __init__( self: Optional[int] ,*lowerCamelCase_: Optional[int] ,**lowerCamelCase_: Optional[int] ) -> Any: # pylint: disable=unused-argument
UpperCAmelCase_ : Any = args[0] if args else None
def __iter__( self: Any ) -> Union[str, Any]:
return iter(self._iterator )
def __getattr__( self: int ,lowerCamelCase_: Optional[Any] ) -> List[Any]:
def empty_fn(*lowerCamelCase_: str ,**lowerCamelCase_: str ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self: Tuple ) -> Union[str, Any]:
return self
def __exit__( self: List[str] ,lowerCamelCase_: List[Any] ,lowerCamelCase_: Optional[Any] ,lowerCamelCase_: Any ) -> Optional[Any]:
return
UpperCamelCase_ = True
class _snake_case :
'''simple docstring'''
def __call__( self: str ,*lowerCamelCase_: Tuple ,lowerCamelCase_: List[Any]=False ,**lowerCamelCase_: Optional[int] ) -> int:
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*lowerCamelCase_ ,**lowerCamelCase_ )
else:
return EmptyTqdm(*lowerCamelCase_ ,**lowerCamelCase_ )
def A__ ( self: Tuple ,*lowerCamelCase_: Union[str, Any] ,**lowerCamelCase_: Union[str, Any] ) -> Optional[int]:
UpperCAmelCase_ : Optional[int] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*lowerCamelCase_ ,**lowerCamelCase_ )
def A__ ( self: Tuple ) -> Union[str, Any]:
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
UpperCamelCase_ = _tqdm_cls()
def lowerCamelCase_ ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def lowerCamelCase_ ( ):
'''simple docstring'''
global _tqdm_active
UpperCAmelCase_ : Any = True
def lowerCamelCase_ ( ):
'''simple docstring'''
global _tqdm_active
UpperCAmelCase_ : Union[str, Any] = False
| 322 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def lowerCamelCase_ ( _a : Optional[int] ):
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F'''could not parse string as bool {string}''' )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
UpperCamelCase_ = parser.parse_args()
UpperCamelCase_ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 322 | 1 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a: List[str] = TypeVar('''_T''')
class SCREAMING_SNAKE_CASE__ ( Generic[_T] ):
'''simple docstring'''
def __init__( self : str , lowerCamelCase : Iterable[_T] | None = None ) -> None:
"""simple docstring"""
_UpperCAmelCase = list(iterable or [] )
_UpperCAmelCase = []
def __len__( self : int ) -> int:
"""simple docstring"""
return len(self._stacka ) + len(self._stacka )
def __repr__( self : Any ) -> str:
"""simple docstring"""
return f"""Queue({tuple(self._stacka[::-1] + self._stacka )})"""
def lowerCamelCase ( self : str , lowerCamelCase : _T ) -> None:
"""simple docstring"""
self._stacka.append(lowerCamelCase )
def lowerCamelCase ( self : str ) -> _T:
"""simple docstring"""
_UpperCAmelCase = self._stacka.pop
_UpperCAmelCase = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod() | 108 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class a__:
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=99 , _UpperCAmelCase=16 , _UpperCAmelCase=36 , _UpperCAmelCase=6 , _UpperCAmelCase=6 , _UpperCAmelCase=6 , _UpperCAmelCase=37 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=16 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , ) -> Dict:
snake_case__ =parent
snake_case__ =batch_size
snake_case__ =seq_length
snake_case__ =is_training
snake_case__ =use_input_mask
snake_case__ =use_token_type_ids
snake_case__ =use_labels
snake_case__ =vocab_size
snake_case__ =embedding_size
snake_case__ =hidden_size
snake_case__ =num_hidden_layers
snake_case__ =num_hidden_groups
snake_case__ =num_attention_heads
snake_case__ =intermediate_size
snake_case__ =hidden_act
snake_case__ =hidden_dropout_prob
snake_case__ =attention_probs_dropout_prob
snake_case__ =max_position_embeddings
snake_case__ =type_vocab_size
snake_case__ =type_sequence_label_size
snake_case__ =initializer_range
snake_case__ =num_labels
snake_case__ =num_choices
snake_case__ =scope
def _lowercase ( self ) -> Tuple:
snake_case__ =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case__ =None
if self.use_input_mask:
snake_case__ =random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ =None
if self.use_token_type_ids:
snake_case__ =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case__ =None
snake_case__ =None
snake_case__ =None
if self.use_labels:
snake_case__ =ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case__ =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case__ =ids_tensor([self.batch_size] , self.num_choices )
snake_case__ =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self ) -> Union[str, Any]:
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
snake_case__ =AlbertModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
snake_case__ =model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
snake_case__ =model(_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
snake_case__ =model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
snake_case__ =AlbertForPreTraining(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
snake_case__ =model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , sentence_order_label=_UpperCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
snake_case__ =AlbertForMaskedLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
snake_case__ =model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
snake_case__ =AlbertForQuestionAnswering(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
snake_case__ =model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
snake_case__ =self.num_labels
snake_case__ =AlbertForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
snake_case__ =model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Any:
snake_case__ =self.num_labels
snake_case__ =AlbertForTokenClassification(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
snake_case__ =model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
snake_case__ =self.num_choices
snake_case__ =AlbertForMultipleChoice(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
snake_case__ =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case__ =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case__ =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case__ =model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self ) -> List[Any]:
snake_case__ =self.prepare_config_and_inputs()
(
(
snake_case__
) , (
snake_case__
) , (
snake_case__
) , (
snake_case__
) , (
snake_case__
) , (
snake_case__
) , (
snake_case__
) ,
) =config_and_inputs
snake_case__ ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a__( snake_case__ , snake_case__ , unittest.TestCase ):
a_ : Optional[int] = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
a_ : List[str] = (
{
'''feature-extraction''': AlbertModel,
'''fill-mask''': AlbertForMaskedLM,
'''question-answering''': AlbertForQuestionAnswering,
'''text-classification''': AlbertForSequenceClassification,
'''token-classification''': AlbertForTokenClassification,
'''zero-shot''': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
a_ : int = True
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False ) -> Any:
snake_case__ =super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
if return_labels:
if model_class in get_values(_UpperCAmelCase ):
snake_case__ =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_UpperCAmelCase )
snake_case__ =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_UpperCAmelCase )
return inputs_dict
def _lowercase ( self ) -> int:
snake_case__ =AlbertModelTester(self )
snake_case__ =ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def _lowercase ( self ) -> Dict:
self.config_tester.run_common_tests()
def _lowercase ( self ) -> str:
snake_case__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def _lowercase ( self ) -> Optional[int]:
snake_case__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase )
def _lowercase ( self ) -> Optional[Any]:
snake_case__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def _lowercase ( self ) -> Tuple:
snake_case__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase )
def _lowercase ( self ) -> List[str]:
snake_case__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )
def _lowercase ( self ) -> Optional[int]:
snake_case__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )
def _lowercase ( self ) -> List[str]:
snake_case__ =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case__ =type
self.model_tester.create_and_check_model(*_UpperCAmelCase )
@slow
def _lowercase ( self ) -> Union[str, Any]:
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ =AlbertModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@require_torch
class a__( unittest.TestCase ):
@slow
def _lowercase ( self ) -> str:
snake_case__ =AlbertModel.from_pretrained('albert-base-v2' )
snake_case__ =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
snake_case__ =torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
snake_case__ =model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
snake_case__ =torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _UpperCAmelCase )
snake_case__ =torch.tensor(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )
| 538 | 0 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
lowerCAmelCase__ = '''1'''
lowerCAmelCase__ = '''0'''
lowerCAmelCase__ = '''1'''
lowerCAmelCase__ = ort.SessionOptions()
lowerCAmelCase__ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
lowerCAmelCase__ = ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
lowerCAmelCase__ = ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
lowerCAmelCase__ = ort.RunOptions()
lowerCAmelCase__ = 128
lowerCAmelCase__ = 1
lowerCAmelCase__ = np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase__ = np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase__ = np.ones((batch, sequence), dtype=np.intaa)
print('''Warm up phase...''')
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print('''Start inference...''')
lowerCAmelCase__ = time.time()
lowerCAmelCase__ = 2000
lowerCAmelCase__ = {}
for iter in range(max_iters):
lowerCAmelCase__ = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))
| 719 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ,A__ ):
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
UpperCAmelCase_ : List[Any] = ksize + 1
UpperCAmelCase_ : Optional[Any] = np.zeros((ksize, ksize) ,dtype=np.floataa )
# each value
for y in range(A__ ):
for x in range(A__ ):
# distance from center
UpperCAmelCase_ : Tuple = x - ksize // 2
UpperCAmelCase_ : Any = y - ksize // 2
# degree to radiant
UpperCAmelCase_ : int = theta / 1_80 * np.pi
UpperCAmelCase_ : Optional[int] = np.cos(_theta )
UpperCAmelCase_ : Union[str, Any] = np.sin(_theta )
# get kernel x
UpperCAmelCase_ : Tuple = cos_theta * px + sin_theta * py
# get kernel y
UpperCAmelCase_ : List[str] = -sin_theta * px + cos_theta * py
# fill kernel
UpperCAmelCase_ : Dict = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
lowerCamelCase_ = imread('''../image_data/lena.jpg''')
# turn image in gray scale value
lowerCamelCase_ = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
lowerCamelCase_ = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
lowerCamelCase_ = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
lowerCamelCase_ = out / out.max() * 255
lowerCamelCase_ = out.astype(np.uinta)
imshow('''Original''', gray)
imshow('''Gabor filter with 20x20 mask and 6 directions''', out)
waitKey(0)
| 463 | 0 |
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> Optional[Any]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_lowercase , int(b / 2)) * actual_power(_lowercase , int(b / 2))
else:
return a * actual_power(_lowercase , int(b / 2)) * actual_power(_lowercase , int(b / 2))
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> float:
"""simple docstring"""
if b < 0:
return 1 / actual_power(_lowercase , _lowercase)
return actual_power(_lowercase , _lowercase)
if __name__ == "__main__":
print(power(-2, -3))
| 280 |
"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
lowercase_ = True
from torch.cuda.amp import autocast
lowercase_ = logging.getLogger(__name__)
def UpperCAmelCase ( _lowercase : Optional[Any]=None , _lowercase : str=None ) -> List[str]:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=_lowercase )
@dataclass
class __a :
lowerCamelCase : str =field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCamelCase : Optional[str] =field(
default=__snake_case , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCamelCase : Optional[bool] =field(
default=__snake_case , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
lowerCamelCase : Optional[float] =field(
default=0.1 , metadata={'help': 'The dropout ratio for the attention probabilities.'} )
lowerCamelCase : Optional[float] =field(
default=0.1 , metadata={'help': 'The dropout ratio for activations inside the fully connected layer.'} )
lowerCamelCase : Optional[float] =field(
default=0.1 , metadata={
'help': 'The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'
} , )
lowerCamelCase : Optional[float] =field(
default=0.1 , metadata={'help': 'The dropout probabilitiy for all 1D convolutional layers in feature extractor.'} , )
lowerCamelCase : Optional[float] =field(
default=0.05 , metadata={
'help': (
'Propability of each feature vector along the time axis to be chosen as the start of the vector'
'span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature'
'vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``.'
)
} , )
lowerCamelCase : Optional[float] =field(default=0.0 , metadata={'help': 'The LayerDrop probability.'} )
@dataclass
class __a :
lowerCamelCase : Optional[str] =field(
default=__snake_case , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
lowerCamelCase : Optional[str] =field(
default='train+validation' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
lowerCamelCase : bool =field(
default=__snake_case , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
lowerCamelCase : Optional[int] =field(
default=__snake_case , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
lowerCamelCase : Optional[int] =field(
default=__snake_case , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
lowerCamelCase : Optional[int] =field(
default=__snake_case , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of validation examples to this '
'value if set.'
)
} , )
lowerCamelCase : List[str] =list_field(
default=[',', '?', '.', '!', '-', ';', ':', '""', '%', '\'', '"', '�'] , metadata={'help': 'A list of characters to remove from the transcripts.'} , )
@dataclass
class __a :
lowerCamelCase : WavaVecaProcessor
lowerCamelCase : Union[bool, str] =True
lowerCamelCase : Optional[int] =None
lowerCamelCase : Optional[int] =None
lowerCamelCase : Optional[int] =None
lowerCamelCase : Optional[int] =None
def __call__( self , UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = [{'''input_values''': feature['''input_values''']} for feature in features]
lowerCAmelCase_ = [{'''input_ids''': feature['''labels''']} for feature in features]
lowerCAmelCase_ = self.processor.pad(
UpperCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
lowerCAmelCase_ = self.processor.pad(
labels=UpperCAmelCase , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
lowerCAmelCase_ = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
lowerCAmelCase_ = labels
return batch
class __a ( __snake_case ):
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase ):
'''simple docstring'''
model.train()
lowerCAmelCase_ = self._prepare_inputs(UpperCAmelCase )
if self.use_amp:
with autocast():
lowerCAmelCase_ = self.compute_loss(UpperCAmelCase , UpperCAmelCase )
else:
lowerCAmelCase_ = self.compute_loss(UpperCAmelCase , UpperCAmelCase )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase_ = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase_ = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
lowerCAmelCase_ = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(UpperCAmelCase ).backward()
elif self.use_apex:
with amp.scale_loss(UpperCAmelCase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(UpperCAmelCase )
else:
loss.backward()
return loss.detach()
def UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
lowerCAmelCase_ = 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.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
lowerCAmelCase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCAmelCase_ = 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:
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.''' )
# 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 )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , _lowercase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
lowerCAmelCase_ = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
lowerCAmelCase_ = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
lowerCAmelCase_ = F"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(_lowercase : List[str] ):
lowerCAmelCase_ = re.sub(_lowercase , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
lowerCAmelCase_ = train_dataset.map(_lowercase , remove_columns=['''sentence'''] )
lowerCAmelCase_ = eval_dataset.map(_lowercase , remove_columns=['''sentence'''] )
def extract_all_chars(_lowercase : str ):
lowerCAmelCase_ = ''' '''.join(batch['''text'''] )
lowerCAmelCase_ = list(set(_lowercase ) )
return {"vocab": [vocab], "all_text": [all_text]}
lowerCAmelCase_ = train_dataset.map(
_lowercase , batched=_lowercase , batch_size=-1 , keep_in_memory=_lowercase , remove_columns=train_dataset.column_names , )
lowerCAmelCase_ = train_dataset.map(
_lowercase , batched=_lowercase , batch_size=-1 , keep_in_memory=_lowercase , remove_columns=eval_dataset.column_names , )
lowerCAmelCase_ = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
lowerCAmelCase_ = {v: k for k, v in enumerate(_lowercase )}
lowerCAmelCase_ = vocab_dict[''' ''']
del vocab_dict[" "]
lowerCAmelCase_ = len(_lowercase )
lowerCAmelCase_ = len(_lowercase )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(_lowercase , _lowercase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase_ = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
lowerCAmelCase_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0.0 , do_normalize=_lowercase , return_attention_mask=_lowercase )
lowerCAmelCase_ = WavaVecaProcessor(feature_extractor=_lowercase , tokenizer=_lowercase )
lowerCAmelCase_ = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
lowerCAmelCase_ = min(len(_lowercase ) , data_args.max_train_samples )
lowerCAmelCase_ = train_dataset.select(range(_lowercase ) )
if data_args.max_val_samples is not None:
lowerCAmelCase_ = eval_dataset.select(range(data_args.max_val_samples ) )
lowerCAmelCase_ = torchaudio.transforms.Resample(4_8_0_0_0 , 1_6_0_0_0 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(_lowercase : Union[str, Any] ):
lowerCAmelCase_ , lowerCAmelCase_ = torchaudio.load(batch['''path'''] )
lowerCAmelCase_ = resampler(_lowercase ).squeeze().numpy()
lowerCAmelCase_ = 1_6_0_0_0
lowerCAmelCase_ = batch['''text''']
return batch
lowerCAmelCase_ = train_dataset.map(
_lowercase , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
lowerCAmelCase_ = eval_dataset.map(
_lowercase , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(_lowercase : str ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), F"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
lowerCAmelCase_ = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(_lowercase )
return batch
lowerCAmelCase_ = train_dataset.map(
_lowercase , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=_lowercase , num_proc=data_args.preprocessing_num_workers , )
lowerCAmelCase_ = eval_dataset.map(
_lowercase , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=_lowercase , num_proc=data_args.preprocessing_num_workers , )
# Metric
lowerCAmelCase_ = datasets.load_metric('''wer''' )
def compute_metrics(_lowercase : Optional[int] ):
lowerCAmelCase_ = pred.predictions
lowerCAmelCase_ = np.argmax(_lowercase , axis=-1 )
lowerCAmelCase_ = processor.tokenizer.pad_token_id
lowerCAmelCase_ = processor.batch_decode(_lowercase )
# we do not want to group tokens when computing the metrics
lowerCAmelCase_ = processor.batch_decode(pred.label_ids , group_tokens=_lowercase )
lowerCAmelCase_ = wer_metric.compute(predictions=_lowercase , references=_lowercase )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
lowerCAmelCase_ = DataCollatorCTCWithPadding(processor=_lowercase , padding=_lowercase )
# Initialize our Trainer
lowerCAmelCase_ = CTCTrainer(
model=_lowercase , data_collator=_lowercase , args=_lowercase , compute_metrics=_lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
lowerCAmelCase_ = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
lowerCAmelCase_ = model_args.model_name_or_path
else:
lowerCAmelCase_ = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
lowerCAmelCase_ = trainer.train(resume_from_checkpoint=_lowercase )
trainer.save_model()
lowerCAmelCase_ = train_result.metrics
lowerCAmelCase_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowercase )
)
lowerCAmelCase_ = min(_lowercase , len(_lowercase ) )
trainer.log_metrics('''train''' , _lowercase )
trainer.save_metrics('''train''' , _lowercase )
trainer.save_state()
# Evaluation
lowerCAmelCase_ = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowerCAmelCase_ = trainer.evaluate()
lowerCAmelCase_ = data_args.max_val_samples if data_args.max_val_samples is not None else len(_lowercase )
lowerCAmelCase_ = min(_lowercase , len(_lowercase ) )
trainer.log_metrics('''eval''' , _lowercase )
trainer.save_metrics('''eval''' , _lowercase )
return results
if __name__ == "__main__":
main() | 552 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowerCAmelCase_ ( A_):
UpperCamelCase__: int = [2, 2, 6, 2] if "tiny" in model_name else [2, 2, 18, 2]
UpperCamelCase__: Tuple = True if "large" in model_name or "huge" in model_name else False
UpperCamelCase__: int = True if "large" in model_name or "huge" in model_name else False
UpperCamelCase__: Dict = True if "large" in model_name or "huge" in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
UpperCamelCase__: Optional[int] = [3, 3, 3, 3]
UpperCamelCase__: Optional[int] = [5, 5, 5, 5]
elif "fl4" in model_name:
UpperCamelCase__: str = [4, 4, 4, 4]
UpperCamelCase__: Optional[Any] = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
UpperCamelCase__: Dict = [3, 3, 3, 3]
if "lrf" in model_name:
UpperCamelCase__: List[str] = [3, 3, 3, 3]
else:
UpperCamelCase__: Optional[int] = [2, 2, 2, 2]
if "tiny" in model_name:
UpperCamelCase__: List[str] = 96
elif "small" in model_name:
UpperCamelCase__: List[str] = 96
elif "base" in model_name:
UpperCamelCase__: List[str] = 1_28
elif "large" in model_name:
UpperCamelCase__: Union[str, Any] = 1_92
elif "xlarge" in model_name:
UpperCamelCase__: List[Any] = 2_56
elif "huge" in model_name:
UpperCamelCase__: Union[str, Any] = 3_52
# set label information
UpperCamelCase__: str = "huggingface/label-files"
if "large" in model_name or "huge" in model_name:
UpperCamelCase__: List[Any] = "imagenet-22k-id2label.json"
else:
UpperCamelCase__: Dict = "imagenet-1k-id2label.json"
UpperCamelCase__: Optional[Any] = json.load(open(hf_hub_download(A_ ,A_ ,repo_type="dataset") ,"r"))
UpperCamelCase__: Tuple = {int(A_): v for k, v in idalabel.items()}
UpperCamelCase__: Dict = {v: k for k, v in idalabel.items()}
UpperCamelCase__: List[Any] = FocalNetConfig(
embed_dim=A_ ,depths=A_ ,focal_levels=A_ ,focal_windows=A_ ,use_conv_embed=A_ ,idalabel=A_ ,labelaid=A_ ,use_post_layernorm=A_ ,use_layerscale=A_ ,)
return config
def lowerCAmelCase_ ( A_):
if "patch_embed.proj" in name:
UpperCamelCase__: List[str] = name.replace("patch_embed.proj" ,"embeddings.patch_embeddings.projection")
if "patch_embed.norm" in name:
UpperCamelCase__: Optional[int] = name.replace("patch_embed.norm" ,"embeddings.norm")
if "layers" in name:
UpperCamelCase__: List[Any] = "encoder." + name
if "encoder.layers" in name:
UpperCamelCase__: Optional[Any] = name.replace("encoder.layers" ,"encoder.stages")
if "downsample.proj" in name:
UpperCamelCase__: Optional[int] = name.replace("downsample.proj" ,"downsample.projection")
if "blocks" in name:
UpperCamelCase__: Dict = name.replace("blocks" ,"layers")
if "modulation.f.weight" in name or "modulation.f.bias" in name:
UpperCamelCase__: Union[str, Any] = name.replace("modulation.f" ,"modulation.projection_in")
if "modulation.h.weight" in name or "modulation.h.bias" in name:
UpperCamelCase__: Tuple = name.replace("modulation.h" ,"modulation.projection_context")
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
UpperCamelCase__: Dict = name.replace("modulation.proj" ,"modulation.projection_out")
if name == "norm.weight":
UpperCamelCase__: Union[str, Any] = "layernorm.weight"
if name == "norm.bias":
UpperCamelCase__: int = "layernorm.bias"
if "head" in name:
UpperCamelCase__: Optional[Any] = name.replace("head" ,"classifier")
else:
UpperCamelCase__: str = "focalnet." + name
return name
def lowerCAmelCase_ ( A_ ,A_ ,A_=False):
UpperCamelCase__: Any = {
"focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
"focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
"focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
"focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
"focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
"focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
"focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
"focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
"focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
"focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
}
# fmt: on
UpperCamelCase__: Tuple = model_name_to_url[model_name]
print("Checkpoint URL: " ,A_)
UpperCamelCase__: Union[str, Any] = torch.hub.load_state_dict_from_url(A_ ,map_location="cpu")["model"]
# rename keys
for key in state_dict.copy().keys():
UpperCamelCase__: List[str] = state_dict.pop(A_)
UpperCamelCase__: int = val
UpperCamelCase__: Any = get_focalnet_config(A_)
UpperCamelCase__: Union[str, Any] = FocalNetForImageClassification(A_)
model.eval()
# load state dict
model.load_state_dict(A_)
# verify conversion
UpperCamelCase__: Any = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCamelCase__: List[Any] = BitImageProcessor(
do_resize=A_ ,size={"shortest_edge": 2_56} ,resample=PILImageResampling.BILINEAR ,do_center_crop=A_ ,crop_size=2_24 ,do_normalize=A_ ,image_mean=A_ ,image_std=A_ ,)
UpperCamelCase__: Optional[int] = Image.open(requests.get(A_ ,stream=A_).raw)
UpperCamelCase__: int = processor(images=A_ ,return_tensors="pt")
UpperCamelCase__: int = transforms.Compose(
[
transforms.Resize(2_56),
transforms.CenterCrop(2_24),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406] ,std=[0.229, 0.224, 0.225]),
])
UpperCamelCase__: Optional[Any] = image_transforms(A_).unsqueeze(0)
# verify pixel_values
assert torch.allclose(inputs.pixel_values ,A_ ,atol=1e-4)
UpperCamelCase__: Any = model(**A_)
UpperCamelCase__: List[Any] = outputs.logits.argmax(-1).item()
print("Predicted class:" ,model.config.idalabel[predicted_class_idx])
print("First values of logits:" ,outputs.logits[0, :3])
if model_name == "focalnet-tiny":
UpperCamelCase__: Any = torch.tensor([0.2166, -0.4368, 0.2191])
elif model_name == "focalnet-tiny-lrf":
UpperCamelCase__: List[str] = torch.tensor([1.1669, 0.0125, -0.1695])
elif model_name == "focalnet-small":
UpperCamelCase__: Any = torch.tensor([0.4917, -0.0430, 0.1341])
elif model_name == "focalnet-small-lrf":
UpperCamelCase__: Union[str, Any] = torch.tensor([-0.2588, -0.5342, -0.2331])
elif model_name == "focalnet-base":
UpperCamelCase__: Any = torch.tensor([-0.1655, -0.4090, -0.1730])
elif model_name == "focalnet-base-lrf":
UpperCamelCase__: Dict = torch.tensor([0.5306, -0.0483, -0.3928])
assert torch.allclose(outputs.logits[0, :3] ,A_ ,atol=1e-4)
print("Looks ok!")
if pytorch_dump_folder_path is not None:
print(F"Saving model and processor of {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(A_)
processor.save_pretrained(A_)
if push_to_hub:
print(F"Pushing model and processor of {model_name} to the hub...")
model.push_to_hub(F"{model_name}")
processor.push_to_hub(F"{model_name}")
if __name__ == "__main__":
A__: Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''focalnet-tiny''',
type=str,
help='''Name of the FocalNet model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub.''',
)
A__: Optional[int] = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 717 |
import math
import flax.linen as nn
import jax.numpy as jnp
def lowerCAmelCase_ ( A_ ,A_ ,A_ = 1 ,A_ = 1 ,A_ = 1.0e4 ,A_ = False ,A_ = 1.0 ,):
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, F"Embedding dimension {embedding_dim} should be even"
UpperCamelCase__: Tuple = float(embedding_dim // 2)
UpperCamelCase__: List[Any] = math.log(max_timescale / min_timescale) / (num_timescales - freq_shift)
UpperCamelCase__: Any = min_timescale * jnp.exp(jnp.arange(A_ ,dtype=jnp.floataa) * -log_timescale_increment)
UpperCamelCase__: Any = jnp.expand_dims(A_ ,1) * jnp.expand_dims(A_ ,0)
# scale embeddings
UpperCamelCase__: List[Any] = scale * emb
if flip_sin_to_cos:
UpperCamelCase__: List[Any] = jnp.concatenate([jnp.cos(A_), jnp.sin(A_)] ,axis=1)
else:
UpperCamelCase__: str = jnp.concatenate([jnp.sin(A_), jnp.cos(A_)] ,axis=1)
UpperCamelCase__: List[Any] = jnp.reshape(A_ ,[jnp.shape(A_)[0], embedding_dim])
return signal
class _a ( nn.Module):
"""simple docstring"""
UpperCamelCase__ = 32
UpperCamelCase__ = jnp.floataa
@nn.compact
def __call__( self: Dict , __lowerCamelCase: int ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = nn.Dense(self.time_embed_dim , dtype=self.dtype , name="linear_1" )(__lowerCamelCase )
UpperCamelCase__: List[str] = nn.silu(__lowerCamelCase )
UpperCamelCase__: Union[str, Any] = nn.Dense(self.time_embed_dim , dtype=self.dtype , name="linear_2" )(__lowerCamelCase )
return temb
class _a ( nn.Module):
"""simple docstring"""
UpperCamelCase__ = 32
UpperCamelCase__ = False
UpperCamelCase__ = 1
@nn.compact
def __call__( self: str , __lowerCamelCase: Dict ):
'''simple docstring'''
return get_sinusoidal_embeddings(
__lowerCamelCase , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 221 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
'''simple docstring'''
@register_to_config
def __init__( self : Any , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : float , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : str , lowerCamelCase : bool = False , ) -> Any:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Embedding(lowerCamelCase , lowerCamelCase )
_UpperCAmelCase = nn.Embedding(lowerCamelCase , lowerCamelCase )
_UpperCAmelCase = False
_UpperCAmelCase = nn.Dropout(p=lowerCamelCase )
_UpperCAmelCase = TaConfig(
vocab_size=lowerCamelCase , d_model=lowerCamelCase , num_heads=lowerCamelCase , d_kv=lowerCamelCase , d_ff=lowerCamelCase , dropout_rate=lowerCamelCase , feed_forward_proj=lowerCamelCase , is_decoder=lowerCamelCase , is_encoder_decoder=lowerCamelCase , )
_UpperCAmelCase = nn.ModuleList()
for lyr_num in range(lowerCamelCase ):
_UpperCAmelCase = TaBlock(lowerCamelCase )
self.encoders.append(lowerCamelCase )
_UpperCAmelCase = TaLayerNorm(lowerCamelCase )
_UpperCAmelCase = nn.Dropout(p=lowerCamelCase )
def lowerCamelCase ( self : List[str] , lowerCamelCase : int , lowerCamelCase : Tuple ) -> int:
"""simple docstring"""
_UpperCAmelCase = self.token_embedder(lowerCamelCase )
_UpperCAmelCase = encoder_input_tokens.shape[1]
_UpperCAmelCase = torch.arange(lowerCamelCase , device=encoder_input_tokens.device )
x += self.position_encoding(lowerCamelCase )
_UpperCAmelCase = self.dropout_pre(lowerCamelCase )
# inverted the attention mask
_UpperCAmelCase = encoder_input_tokens.size()
_UpperCAmelCase = self.get_extended_attention_mask(lowerCamelCase , lowerCamelCase )
for lyr in self.encoders:
_UpperCAmelCase = lyr(lowerCamelCase , lowerCamelCase )[0]
_UpperCAmelCase = self.layer_norm(lowerCamelCase )
return self.dropout_post(lowerCamelCase ), encoder_inputs_mask | 108 |
"""simple docstring"""
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 UpperCamelCase :
def __init__( self : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str=13 , UpperCAmelCase__ : int=7 , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : str=True , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : List[Any]=99 , UpperCAmelCase__ : str=32 , UpperCAmelCase__ : Dict=5 , UpperCAmelCase__ : str=4 , UpperCAmelCase__ : str=37 , UpperCAmelCase__ : List[Any]="gelu" , UpperCAmelCase__ : Tuple=0.1 , UpperCAmelCase__ : str=0.1 , UpperCAmelCase__ : List[str]=512 , UpperCAmelCase__ : Any=16 , UpperCAmelCase__ : Tuple=2 , UpperCAmelCase__ : Dict=0.0_2 , UpperCAmelCase__ : Optional[int]=3 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : Optional[Any]=None , ) -> Union[str, Any]:
_a : str = parent
_a : List[str] = batch_size
_a : List[str] = seq_length
_a : Any = is_training
_a : Any = use_token_type_ids
_a : Tuple = use_labels
_a : Optional[Any] = vocab_size
_a : Optional[int] = hidden_size
_a : Union[str, Any] = num_hidden_layers
_a : Optional[int] = num_attention_heads
_a : List[Any] = intermediate_size
_a : Any = hidden_act
_a : Optional[int] = hidden_dropout_prob
_a : Any = attention_probs_dropout_prob
_a : int = max_position_embeddings
_a : Tuple = type_vocab_size
_a : str = type_sequence_label_size
_a : Dict = initializer_range
_a : List[Any] = num_labels
_a : Union[str, Any] = num_choices
_a : List[Any] = scope
_a : List[Any] = self.vocab_size - 1
def _lowercase ( self : Any ) -> int:
_a : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a : Optional[Any] = None
if self.use_token_type_ids:
_a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a : List[Any] = None
_a : List[Any] = None
_a : Union[str, Any] = None
if self.use_labels:
_a : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
_a : Union[str, 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 , )
_a : Tuple = 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 _lowercase ( self : List[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] , *UpperCAmelCase__ : Tuple ) -> List[str]:
_a : Optional[int] = OpenAIGPTModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : Optional[Any] = model(UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , head_mask=UpperCAmelCase__ )
_a : List[str] = model(UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
_a : Optional[Any] = model(UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : Optional[int] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , *UpperCAmelCase__ : Optional[int] ) -> List[str]:
_a : str = OpenAIGPTLMHeadModel(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : Optional[int] = model(UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self : Tuple , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Any , UpperCAmelCase__ : Optional[Any] , *UpperCAmelCase__ : Union[str, Any] ) -> Any:
_a : int = OpenAIGPTDoubleHeadsModel(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : Tuple = model(UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self : Tuple , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[Any] , *UpperCAmelCase__ : Any ) -> List[Any]:
_a : Dict = self.num_labels
_a : str = OpenAIGPTForSequenceClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_a : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a : Optional[int] = model(UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self : List[str] ) -> Union[str, Any]:
_a : Optional[Any] = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) : Optional[int] = config_and_inputs
_a : Any = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""head_mask""": head_mask,
}
return config, inputs_dict
@require_torch
class UpperCamelCase ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
UpperCamelCase : Dict = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
UpperCamelCase : Dict = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
UpperCamelCase : Optional[Any] = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _lowercase ( self : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : str ) -> Optional[int]:
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 _lowercase ( self : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[int]=False ) -> List[Any]:
_a : Optional[Any] = super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
_a : List[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=UpperCAmelCase__ , )
_a : Dict = inputs_dict["""labels"""]
_a : List[Any] = inputs_dict["""labels"""]
_a : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=UpperCAmelCase__ , )
_a : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
return inputs_dict
def _lowercase ( self : Dict ) -> List[Any]:
_a : Dict = OpenAIGPTModelTester(self )
_a : Any = ConfigTester(self , config_class=UpperCAmelCase__ , n_embd=37 )
def _lowercase ( self : Tuple ) -> Optional[Any]:
self.config_tester.run_common_tests()
def _lowercase ( self : Optional[int] ) -> Any:
_a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*UpperCAmelCase__ )
def _lowercase ( self : List[Any] ) -> int:
_a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*UpperCAmelCase__ )
def _lowercase ( self : Optional[int] ) -> int:
_a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*UpperCAmelCase__ )
def _lowercase ( self : Dict ) -> Any:
_a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCAmelCase__ )
@slow
def _lowercase ( self : str ) -> List[str]:
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : str = OpenAIGPTModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@require_torch
class UpperCamelCase ( unittest.TestCase ):
@slow
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
_a : List[str] = OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
model.to(UpperCAmelCase__ )
_a : Any = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=UpperCAmelCase__ ) # the president is
_a : List[Any] = [
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
_a : List[str] = model.generate(UpperCAmelCase__ , do_sample=UpperCAmelCase__ )
self.assertListEqual(output_ids[0].tolist() , UpperCAmelCase__ )
| 389 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
lowerCAmelCase__ = 42
class __lowerCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , UpperCAmelCase=3 , UpperCAmelCase=3 , UpperCAmelCase=("DownEncoderBlock2D",) , UpperCAmelCase=(64,) , UpperCAmelCase=2 , UpperCAmelCase=32 , UpperCAmelCase="silu" , UpperCAmelCase=True , ) -> Tuple:
'''simple docstring'''
super().__init__()
lowercase_ = layers_per_block
lowercase_ = torch.nn.Convad(
UpperCAmelCase , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
lowercase_ = None
lowercase_ = nn.ModuleList([] )
# down
lowercase_ = block_out_channels[0]
for i, down_block_type in enumerate(UpperCAmelCase ):
lowercase_ = output_channel
lowercase_ = block_out_channels[i]
lowercase_ = i == len(UpperCAmelCase ) - 1
lowercase_ = get_down_block(
UpperCAmelCase , num_layers=self.layers_per_block , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=UpperCAmelCase , resnet_groups=UpperCAmelCase , attention_head_dim=UpperCAmelCase , temb_channels=UpperCAmelCase , )
self.down_blocks.append(UpperCAmelCase )
# mid
lowercase_ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=UpperCAmelCase , output_scale_factor=1 , resnet_time_scale_shift="default" , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCAmelCase , temb_channels=UpperCAmelCase , )
# out
lowercase_ = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=UpperCAmelCase , eps=1e-6 )
lowercase_ = nn.SiLU()
lowercase_ = 2 * out_channels if double_z else out_channels
lowercase_ = nn.Convad(block_out_channels[-1] , UpperCAmelCase , 3 , padding=1 )
lowercase_ = False
def A__ ( self , UpperCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = x
lowercase_ = self.conv_in(UpperCAmelCase )
if self.training and self.gradient_checkpointing:
def create_custom_forward(UpperCAmelCase ):
def custom_forward(*UpperCAmelCase ):
return module(*UpperCAmelCase )
return custom_forward
# down
if is_torch_version(">=" , "1.11.0" ):
for down_block in self.down_blocks:
lowercase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCAmelCase ) , UpperCAmelCase , use_reentrant=UpperCAmelCase )
# middle
lowercase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase , use_reentrant=UpperCAmelCase )
else:
for down_block in self.down_blocks:
lowercase_ = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase ) , UpperCAmelCase )
# middle
lowercase_ = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , UpperCAmelCase )
else:
# down
for down_block in self.down_blocks:
lowercase_ = down_block(UpperCAmelCase )
# middle
lowercase_ = self.mid_block(UpperCAmelCase )
# post-process
lowercase_ = self.conv_norm_out(UpperCAmelCase )
lowercase_ = self.conv_act(UpperCAmelCase )
lowercase_ = self.conv_out(UpperCAmelCase )
return sample
class __lowerCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , UpperCAmelCase=3 , UpperCAmelCase=3 , UpperCAmelCase=("UpDecoderBlock2D",) , UpperCAmelCase=(64,) , UpperCAmelCase=2 , UpperCAmelCase=32 , UpperCAmelCase="silu" , UpperCAmelCase="group" , ) -> int:
'''simple docstring'''
super().__init__()
lowercase_ = layers_per_block
lowercase_ = nn.Convad(
UpperCAmelCase , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
lowercase_ = None
lowercase_ = nn.ModuleList([] )
lowercase_ = in_channels if norm_type == "spatial" else None
# mid
lowercase_ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=UpperCAmelCase , output_scale_factor=1 , resnet_time_scale_shift="default" if norm_type == "group" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCAmelCase , temb_channels=UpperCAmelCase , )
# up
lowercase_ = list(reversed(UpperCAmelCase ) )
lowercase_ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(UpperCAmelCase ):
lowercase_ = output_channel
lowercase_ = reversed_block_out_channels[i]
lowercase_ = i == len(UpperCAmelCase ) - 1
lowercase_ = get_up_block(
UpperCAmelCase , num_layers=self.layers_per_block + 1 , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , prev_output_channel=UpperCAmelCase , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=UpperCAmelCase , resnet_groups=UpperCAmelCase , attention_head_dim=UpperCAmelCase , temb_channels=UpperCAmelCase , resnet_time_scale_shift=UpperCAmelCase , )
self.up_blocks.append(UpperCAmelCase )
lowercase_ = output_channel
# out
if norm_type == "spatial":
lowercase_ = SpatialNorm(block_out_channels[0] , UpperCAmelCase )
else:
lowercase_ = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=UpperCAmelCase , eps=1e-6 )
lowercase_ = nn.SiLU()
lowercase_ = nn.Convad(block_out_channels[0] , UpperCAmelCase , 3 , padding=1 )
lowercase_ = False
def A__ ( self , UpperCAmelCase , UpperCAmelCase=None ) -> Dict:
'''simple docstring'''
lowercase_ = z
lowercase_ = self.conv_in(UpperCAmelCase )
lowercase_ = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(UpperCAmelCase ):
def custom_forward(*UpperCAmelCase ):
return module(*UpperCAmelCase )
return custom_forward
if is_torch_version(">=" , "1.11.0" ):
# middle
lowercase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase , UpperCAmelCase , use_reentrant=UpperCAmelCase )
lowercase_ = sample.to(UpperCAmelCase )
# up
for up_block in self.up_blocks:
lowercase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCAmelCase ) , UpperCAmelCase , UpperCAmelCase , use_reentrant=UpperCAmelCase )
else:
# middle
lowercase_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase , UpperCAmelCase )
lowercase_ = sample.to(UpperCAmelCase )
# up
for up_block in self.up_blocks:
lowercase_ = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase ) , UpperCAmelCase , UpperCAmelCase )
else:
# middle
lowercase_ = self.mid_block(UpperCAmelCase , UpperCAmelCase )
lowercase_ = sample.to(UpperCAmelCase )
# up
for up_block in self.up_blocks:
lowercase_ = up_block(UpperCAmelCase , UpperCAmelCase )
# post-process
if latent_embeds is None:
lowercase_ = self.conv_norm_out(UpperCAmelCase )
else:
lowercase_ = self.conv_norm_out(UpperCAmelCase , UpperCAmelCase )
lowercase_ = self.conv_act(UpperCAmelCase )
lowercase_ = self.conv_out(UpperCAmelCase )
return sample
class __lowerCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase="random" , UpperCAmelCase=False , UpperCAmelCase=True ) -> str:
'''simple docstring'''
super().__init__()
lowercase_ = n_e
lowercase_ = vq_embed_dim
lowercase_ = beta
lowercase_ = legacy
lowercase_ = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
lowercase_ = remap
if self.remap is not None:
self.register_buffer("used" , torch.tensor(np.load(self.remap ) ) )
lowercase_ = self.used.shape[0]
lowercase_ = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
lowercase_ = self.re_embed
lowercase_ = self.re_embed + 1
print(
F'Remapping {self.n_e} indices to {self.re_embed} indices. '
F'Using {self.unknown_index} for unknown indices.' )
else:
lowercase_ = n_e
lowercase_ = sane_index_shape
def A__ ( self , UpperCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = inds.shape
assert len(UpperCAmelCase ) > 1
lowercase_ = inds.reshape(ishape[0] , -1 )
lowercase_ = self.used.to(UpperCAmelCase )
lowercase_ = (inds[:, :, None] == used[None, None, ...]).long()
lowercase_ = match.argmax(-1 )
lowercase_ = match.sum(2 ) < 1
if self.unknown_index == "random":
lowercase_ = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
lowercase_ = self.unknown_index
return new.reshape(UpperCAmelCase )
def A__ ( self , UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = inds.shape
assert len(UpperCAmelCase ) > 1
lowercase_ = inds.reshape(ishape[0] , -1 )
lowercase_ = self.used.to(UpperCAmelCase )
if self.re_embed > self.used.shape[0]: # extra token
lowercase_ = 0 # simply set to zero
lowercase_ = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , UpperCAmelCase )
return back.reshape(UpperCAmelCase )
def A__ ( self , UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = z.permute(0 , 2 , 3 , 1 ).contiguous()
lowercase_ = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
lowercase_ = torch.argmin(torch.cdist(UpperCAmelCase , self.embedding.weight ) , dim=1 )
lowercase_ = self.embedding(UpperCAmelCase ).view(z.shape )
lowercase_ = None
lowercase_ = None
# compute loss for embedding
if not self.legacy:
lowercase_ = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
lowercase_ = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
lowercase_ = z + (z_q - z).detach()
# reshape back to match original input shape
lowercase_ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
lowercase_ = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
lowercase_ = self.remap_to_used(UpperCAmelCase )
lowercase_ = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
lowercase_ = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def A__ ( self , UpperCAmelCase , UpperCAmelCase ) -> int:
'''simple docstring'''
if self.remap is not None:
lowercase_ = indices.reshape(shape[0] , -1 ) # add batch axis
lowercase_ = self.unmap_to_all(UpperCAmelCase )
lowercase_ = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
lowercase_ = self.embedding(UpperCAmelCase )
if shape is not None:
lowercase_ = z_q.view(UpperCAmelCase )
# reshape back to match original input shape
lowercase_ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase=False ) -> List[Any]:
'''simple docstring'''
lowercase_ = parameters
lowercase_ , lowercase_ = torch.chunk(UpperCAmelCase , 2 , dim=1 )
lowercase_ = torch.clamp(self.logvar , -30.0 , 20.0 )
lowercase_ = deterministic
lowercase_ = torch.exp(0.5 * self.logvar )
lowercase_ = torch.exp(self.logvar )
if self.deterministic:
lowercase_ = lowercase_ = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def A__ ( self , UpperCAmelCase = None ) -> torch.FloatTensor:
'''simple docstring'''
lowercase_ = randn_tensor(
self.mean.shape , generator=UpperCAmelCase , device=self.parameters.device , dtype=self.parameters.dtype )
lowercase_ = self.mean + self.std * sample
return x
def A__ ( self , UpperCAmelCase=None ) -> Tuple:
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def A__ ( self , UpperCAmelCase , UpperCAmelCase=[1, 2, 3] ) -> Tuple:
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
lowercase_ = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=UpperCAmelCase )
def A__ ( self ) -> Tuple:
'''simple docstring'''
return self.mean
| 601 |
import os
# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE__ = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(__lowerCamelCase ) )
lowercase_ = os.path.join(__lowerCamelCase , "words.txt" )
lowercase_ = ""
with open(__lowerCamelCase ) as f:
lowercase_ = f.readline()
lowercase_ = [word.strip("\"" ) for word in words.strip("\r\n" ).split("," )]
lowercase_ = [
word
for word in [sum(ord(__lowerCamelCase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 601 | 1 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class __lowerCAmelCase ( A_ ):
_UpperCamelCase : Any = """facebook/bart-large-mnli"""
_UpperCamelCase : Tuple = (
"""This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
"""should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
"""It returns the most likely label in the list of provided `labels` for the input text."""
)
_UpperCamelCase : List[Any] = """text_classifier"""
_UpperCamelCase : Any = AutoTokenizer
_UpperCamelCase : List[str] = AutoModelForSequenceClassification
_UpperCamelCase : Union[str, Any] = ["""text""", ["""text"""]]
_UpperCamelCase : Dict = ["""text"""]
def _snake_case ( self ) -> Union[str, Any]:
"""simple docstring"""
super().setup()
a__ : List[Any] = self.model.config
a__ : Union[str, Any] = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
a__ : Tuple = int(snake_case )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _snake_case ( self , snake_case , snake_case ) -> Any:
"""simple docstring"""
a__ : Union[str, Any] = labels
return self.pre_processor(
[text] * len(snake_case ) , [F"""This example is {label}""" for label in labels] , return_tensors="pt" , padding="max_length" , )
def _snake_case ( self , snake_case ) -> Any:
"""simple docstring"""
a__ : int = outputs.logits
a__ : List[Any] = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 112 |
"""simple docstring"""
def _snake_case ( __snake_case : str , __snake_case : str ):
"""simple docstring"""
_lowerCamelCase : str = len(__snake_case )
_lowerCamelCase : Union[str, Any] = len(__snake_case )
_lowerCamelCase : int = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_lowerCamelCase : Union[str, Any] = True
for i in range(__snake_case ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
_lowerCamelCase : Tuple = True
if a[i].islower():
_lowerCamelCase : Tuple = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCamelCase ( __lowercase : List[Any] ):
'''simple docstring'''
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def UpperCamelCase ( __lowercase : List[str] ,__lowercase : Optional[Any] ):
'''simple docstring'''
A_ : Dict = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
A_ : Optional[Any] = key.replace('heads.cmd.mim_head.cls.predictions' ,'mmm_image_head' )
A_ : Optional[Any] = key.replace('heads.cmd.mlm_head.cls.predictions' ,'mmm_text_head' )
A_ : Tuple = key.replace('heads.cmd.itm_head.cls' ,'itm_head' )
A_ : List[str] = key.replace('heads.cmd.itm_head.pooler' ,'itm_head.pooler' )
A_ : Optional[int] = key.replace('heads.cmd.clip_head.logit_scale' ,'flava.logit_scale' )
A_ : Union[str, Any] = key.replace('heads.fairseq_mlm.cls.predictions' ,'mlm_head' )
A_ : List[Any] = key.replace('heads.imagenet.mim_head.cls.predictions' ,'mim_head' )
A_ : List[str] = key.replace('mm_text_projection' ,'flava.text_to_mm_projection' )
A_ : Tuple = key.replace('mm_image_projection' ,'flava.image_to_mm_projection' )
A_ : Optional[int] = key.replace('image_encoder.module' ,'flava.image_model' )
A_ : Union[str, Any] = key.replace('text_encoder.module' ,'flava.text_model' )
A_ : Dict = key.replace('mm_encoder.module.encoder.cls_token' ,'flava.multimodal_model.cls_token' )
A_ : int = key.replace('mm_encoder.module' ,'flava.multimodal_model' )
A_ : List[Any] = key.replace('text_projection' ,'flava.text_projection' )
A_ : Optional[int] = key.replace('image_projection' ,'flava.image_projection' )
A_ : List[str] = value.float()
for key, value in codebook_state_dict.items():
A_ : int = value
return upgrade
@torch.no_grad()
def UpperCamelCase ( __lowercase : int ,__lowercase : Tuple ,__lowercase : Tuple ,__lowercase : Tuple=None ):
'''simple docstring'''
if config_path is not None:
A_ : List[str] = FlavaConfig.from_pretrained(__lowercase )
else:
A_ : Dict = FlavaConfig()
A_ : Union[str, Any] = FlavaForPreTraining(__lowercase ).eval()
A_ : int = convert_dalle_checkpoint(__lowercase ,__lowercase ,save_checkpoint=__lowercase )
if os.path.exists(__lowercase ):
A_ : str = torch.load(__lowercase ,map_location='cpu' )
else:
A_ : Optional[int] = torch.hub.load_state_dict_from_url(__lowercase ,map_location='cpu' )
A_ : Any = upgrade_state_dict(__lowercase ,__lowercase )
hf_model.load_state_dict(__lowercase )
A_ : Optional[int] = hf_model.state_dict()
A_ : List[str] = count_parameters(__lowercase )
A_ : int = count_parameters(__lowercase ) + count_parameters(__lowercase )
assert torch.allclose(__lowercase ,__lowercase ,atol=1e-3 )
hf_model.save_pretrained(__lowercase )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
_UpperCAmelCase = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 703 | def UpperCamelCase ( __lowercase : str ):
'''simple docstring'''
A_ : int = len(__lowercase )
A_ : List[Any] = sum(__lowercase )
A_ : List[str] = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 ,n + 1 ):
A_ : Optional[Any] = True
for i in range(1 ,s + 1 ):
A_ : Tuple = False
for i in range(1 ,n + 1 ):
for j in range(1 ,s + 1 ):
A_ : Dict = dp[i][j - 1]
if arr[i - 1] <= j:
A_ : Dict = 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:
A_ : List[Any] = s - 2 * j
break
return diff
| 70 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : Optional[Any] = {
"""xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
"""xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
"""xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
"""xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
"""xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
"""xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
"""xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
"""xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
"""xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
"""xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}
class lowerCAmelCase__ ( UpperCamelCase__ ):
A = "xlm"
A = {
"hidden_size": "emb_dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
"n_words": "vocab_size", # For backward compatibility
}
def __init__( self : str , UpperCamelCase_ : int=30_145 , UpperCamelCase_ : Dict=2_048 , UpperCamelCase_ : Optional[Any]=12 , UpperCamelCase_ : Optional[int]=16 , UpperCamelCase_ : Optional[int]=0.1 , UpperCamelCase_ : Optional[Any]=0.1 , UpperCamelCase_ : Optional[Any]=True , UpperCamelCase_ : Optional[Any]=False , UpperCamelCase_ : Any=False , UpperCamelCase_ : List[str]=False , UpperCamelCase_ : Optional[int]=1 , UpperCamelCase_ : Union[str, Any]=True , UpperCamelCase_ : Dict=512 , UpperCamelCase_ : Union[str, Any]=2_048**-0.5 , UpperCamelCase_ : Optional[int]=1e-1_2 , UpperCamelCase_ : Tuple=0.02 , UpperCamelCase_ : Any=0 , UpperCamelCase_ : Optional[Any]=1 , UpperCamelCase_ : Union[str, Any]=2 , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Union[str, Any]=5 , UpperCamelCase_ : Tuple=True , UpperCamelCase_ : Dict="first" , UpperCamelCase_ : Optional[Any]=True , UpperCamelCase_ : List[Any]=None , UpperCamelCase_ : Tuple=True , UpperCamelCase_ : List[Any]=0.1 , UpperCamelCase_ : Union[str, Any]=5 , UpperCamelCase_ : int=5 , UpperCamelCase_ : Optional[int]=0 , UpperCamelCase_ : int=0 , UpperCamelCase_ : int=2 , UpperCamelCase_ : Optional[int]=0 , **UpperCamelCase_ : Optional[Any] , ) -> Dict:
"""simple docstring"""
lowerCamelCase_ : Optional[Any] = vocab_size
lowerCamelCase_ : Optional[Any] = emb_dim
lowerCamelCase_ : List[Any] = n_layers
lowerCamelCase_ : Tuple = n_heads
lowerCamelCase_ : List[Any] = dropout
lowerCamelCase_ : Union[str, Any] = attention_dropout
lowerCamelCase_ : List[str] = gelu_activation
lowerCamelCase_ : Optional[int] = sinusoidal_embeddings
lowerCamelCase_ : List[Any] = causal
lowerCamelCase_ : Optional[Any] = asm
lowerCamelCase_ : Optional[int] = n_langs
lowerCamelCase_ : Tuple = use_lang_emb
lowerCamelCase_ : Any = layer_norm_eps
lowerCamelCase_ : Optional[Any] = bos_index
lowerCamelCase_ : Any = eos_index
lowerCamelCase_ : Optional[int] = pad_index
lowerCamelCase_ : Dict = unk_index
lowerCamelCase_ : List[Any] = mask_index
lowerCamelCase_ : List[str] = is_encoder
lowerCamelCase_ : Dict = max_position_embeddings
lowerCamelCase_ : Any = embed_init_std
lowerCamelCase_ : List[str] = init_std
lowerCamelCase_ : List[Any] = summary_type
lowerCamelCase_ : Union[str, Any] = summary_use_proj
lowerCamelCase_ : Optional[Any] = summary_activation
lowerCamelCase_ : Optional[Any] = summary_proj_to_labels
lowerCamelCase_ : Union[str, Any] = summary_first_dropout
lowerCamelCase_ : Optional[int] = start_n_top
lowerCamelCase_ : str = end_n_top
lowerCamelCase_ : Any = mask_token_id
lowerCamelCase_ : Tuple = lang_id
if "n_words" in kwargs:
lowerCamelCase_ : Tuple = kwargs['''n_words''']
super().__init__(pad_token_id=snake_case_ , bos_token_id=snake_case_ , **snake_case_ )
class lowerCAmelCase__ ( UpperCamelCase__ ):
@property
def __UpperCamelCase ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
lowerCamelCase_ : Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCamelCase_ : Optional[int] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 501 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] =tf.convert_to_tensor(__a )
SCREAMING_SNAKE_CASE : Union[str, Any] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] =tf.convert_to_tensor(__a )
SCREAMING_SNAKE_CASE : str =tf.cast(math.pi , x.dtype )
SCREAMING_SNAKE_CASE : Optional[Any] =tf.cast(0.044715 , x.dtype )
SCREAMING_SNAKE_CASE : Dict =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__a , 3 )) ))
return x * cdf
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple =tf.convert_to_tensor(__a )
return x * tf.tanh(tf.math.softplus(__a ) )
def lowerCAmelCase_ ( __a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any =tf.convert_to_tensor(__a )
SCREAMING_SNAKE_CASE : Optional[Any] =tf.cast(0.044715 , x.dtype )
SCREAMING_SNAKE_CASE : Union[str, Any] =tf.cast(0.7978845608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] =tf.convert_to_tensor(__a )
SCREAMING_SNAKE_CASE : List[str] =tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCAmelCase_ ( __a ) -> str:
"""simple docstring"""
return tf.clip_by_value(_gelu(__a ) , -10 , 10 )
def lowerCAmelCase_ ( __a , __a=-1 ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] =tf.split(__a , 2 , axis=__a )
return a * tf.math.sigmoid(__a )
if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
return tf.keras.activations.gelu(__a , approximate=__a )
_A = tf.keras.activations.gelu
_A = approximate_gelu_wrap
else:
_A = _gelu
_A = _gelu_new
_A = {
"""gelu""": gelu,
"""gelu_10""": gelu_aa,
"""gelu_fast""": gelu_fast,
"""gelu_new""": gelu_new,
"""glu""": glu,
"""mish""": mish,
"""quick_gelu""": quick_gelu,
"""relu""": tf.keras.activations.relu,
"""sigmoid""": tf.keras.activations.sigmoid,
"""silu""": tf.keras.activations.swish,
"""swish""": tf.keras.activations.swish,
"""tanh""": tf.keras.activations.tanh,
}
def lowerCAmelCase_ ( __a ) -> Any:
"""simple docstring"""
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 258 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __lowerCamelCase ( snake_case__ = "." ) -> int:
"""simple docstring"""
for dir_path, dir_names, filenames in os.walk(A__ ):
_SCREAMING_SNAKE_CASE = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(A__ )[1] in (".py", ".ipynb"):
yield os.path.join(A__ ,A__ ).lstrip("""./""" )
def __lowerCamelCase ( snake_case__ ) -> str:
"""simple docstring"""
return F'{i * " "}*' if i else "\n##"
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(A__ ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(A__ )} {new_part.replace("_" ," " ).title()}' )
return new_path
def __lowerCamelCase ( snake_case__ = "." ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """"""
for filepath in sorted(good_file_paths(A__ ) ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = os.path.split(A__ )
if filepath != old_path:
_SCREAMING_SNAKE_CASE = print_path(A__ ,A__ )
_SCREAMING_SNAKE_CASE = (filepath.count(os.sep ) + 1) if filepath else 0
_SCREAMING_SNAKE_CASE = F'{filepath}/{filename}'.replace(""" """ ,"""%20""" )
_SCREAMING_SNAKE_CASE = os.path.splitext(filename.replace("""_""" ,""" """ ).title() )[0]
print(F'{md_prefix(A__ )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md('''.''')
| 714 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 569 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
__SCREAMING_SNAKE_CASE = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class __UpperCamelCase ( unittest.TestCase ):
lowercase_ : List[Any] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowercase_ : Optional[int] = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
lowercase_ : List[str] = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
lowercase_ : List[Any] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def UpperCAmelCase__ ( self : Any ) -> Any:
lowerCAmelCase :Optional[int] = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='pt' )
lowerCAmelCase :int = text_classifier('This is great !' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'LABEL_0', 'score': 0.5_0_4}] )
lowerCAmelCase :Dict = text_classifier('This is great !' , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}] )
lowerCAmelCase :Dict = text_classifier(['This is great !', 'This is bad'] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
] , )
lowerCAmelCase :int = text_classifier('This is great !' , top_k=1 )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'LABEL_0', 'score': 0.5_0_4}] )
# Legacy behavior
lowerCAmelCase :List[Any] = text_classifier('This is great !' , return_all_scores=UpperCAmelCase )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'LABEL_0', 'score': 0.5_0_4}] )
lowerCAmelCase :List[str] = text_classifier('This is great !' , return_all_scores=UpperCAmelCase )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]] )
lowerCAmelCase :List[Any] = text_classifier(['This is great !', 'Something else'] , return_all_scores=UpperCAmelCase )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
] , )
lowerCAmelCase :Dict = text_classifier(['This is great !', 'Something else'] , return_all_scores=UpperCAmelCase )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [
{'label': 'LABEL_0', 'score': 0.5_0_4},
{'label': 'LABEL_0', 'score': 0.5_0_4},
] , )
@require_torch
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[int]:
import torch
lowerCAmelCase :List[str] = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='pt' , device=torch.device('cpu' ) , )
lowerCAmelCase :List[Any] = text_classifier('This is great !' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'LABEL_0', 'score': 0.5_0_4}] )
@require_tf
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Any:
lowerCAmelCase :Any = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='tf' )
lowerCAmelCase :Optional[int] = text_classifier('This is great !' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'LABEL_0', 'score': 0.5_0_4}] )
@slow
@require_torch
def UpperCAmelCase__ ( self : int ) -> Tuple:
lowerCAmelCase :Optional[Any] = pipeline('text-classification' )
lowerCAmelCase :str = text_classifier('This is great !' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'POSITIVE', 'score': 1.0}] )
lowerCAmelCase :Dict = text_classifier('This is bad !' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'NEGATIVE', 'score': 1.0}] )
lowerCAmelCase :int = text_classifier('Birds are a type of animal' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'POSITIVE', 'score': 0.9_8_8}] )
@slow
@require_tf
def UpperCAmelCase__ ( self : List[Any] ) -> Dict:
lowerCAmelCase :List[str] = pipeline('text-classification' , framework='tf' )
lowerCAmelCase :Tuple = text_classifier('This is great !' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'POSITIVE', 'score': 1.0}] )
lowerCAmelCase :Any = text_classifier('This is bad !' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'NEGATIVE', 'score': 1.0}] )
lowerCAmelCase :str = text_classifier('Birds are a type of animal' )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': 'POSITIVE', 'score': 0.9_8_8}] )
def UpperCAmelCase__ ( self : List[str] , UpperCAmelCase : int , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] ) -> Any:
lowerCAmelCase :Any = TextClassificationPipeline(model=UpperCAmelCase , tokenizer=UpperCAmelCase )
return text_classifier, ["HuggingFace is in", "This is another test"]
def UpperCAmelCase__ ( self : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Dict ) -> Optional[int]:
lowerCAmelCase :int = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
lowerCAmelCase :List[Any] = 'HuggingFace is in'
lowerCAmelCase :Optional[int] = text_classifier(UpperCAmelCase )
self.assertEqual(nested_simplify(UpperCAmelCase ) , [{'label': ANY(UpperCAmelCase ), 'score': ANY(UpperCAmelCase )}] )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
lowerCAmelCase :Any = ['HuggingFace is in ', 'Paris is in France']
lowerCAmelCase :str = text_classifier(UpperCAmelCase )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [{'label': ANY(UpperCAmelCase ), 'score': ANY(UpperCAmelCase )}, {'label': ANY(UpperCAmelCase ), 'score': ANY(UpperCAmelCase )}] , )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
self.assertTrue(outputs[1]['label'] in model.config.idalabel.values() )
# Forcing to get all results with `top_k=None`
# This is NOT the legacy format
lowerCAmelCase :Tuple = text_classifier(UpperCAmelCase , top_k=UpperCAmelCase )
lowerCAmelCase :Dict = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [[{'label': ANY(UpperCAmelCase ), 'score': ANY(UpperCAmelCase )}] * N, [{'label': ANY(UpperCAmelCase ), 'score': ANY(UpperCAmelCase )}] * N] , )
lowerCAmelCase :int = {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
lowerCAmelCase :Union[str, Any] = text_classifier(UpperCAmelCase )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {'label': ANY(UpperCAmelCase ), 'score': ANY(UpperCAmelCase )} , )
self.assertTrue(outputs['label'] in model.config.idalabel.values() )
# This might be used a text pair, but tokenizer + pipe interaction
# makes it hard to understand that it's not using the pair properly
# https://github.com/huggingface/transformers/issues/17305
# We disabled this usage instead as it was outputting wrong outputs.
lowerCAmelCase :Union[str, Any] = [['HuggingFace is in ', 'Paris is in France']]
with self.assertRaises(UpperCAmelCase ):
text_classifier(UpperCAmelCase )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
lowerCAmelCase :Optional[int] = text_classifier([[['HuggingFace is in ', 'Paris is in France']]] )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , [{'label': ANY(UpperCAmelCase ), 'score': ANY(UpperCAmelCase )}] , )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() ) | 553 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( a__ , a__ , a__ ):
'''simple docstring'''
lowerCAmelCase :Tuple = MobileBertConfig.from_json_file(a__ )
print(F"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase :Tuple = MobileBertForPreTraining(a__ )
# Load weights from tf checkpoint
lowerCAmelCase :Any = load_tf_weights_in_mobilebert(a__ , a__ , a__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , a__ )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = 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(
'--mobilebert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained MobileBERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__SCREAMING_SNAKE_CASE = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path) | 553 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__lowerCAmelCase : List[str] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 1000,
'''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''',
}
__lowerCAmelCase : List[str] = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 1000,
'''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 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''',
}
__lowerCAmelCase : List[Any] = {
'''sample_size''': 256,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 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''',
}
__lowerCAmelCase : Union[str, Any] = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__lowerCAmelCase : List[Any] = {
'''num_train_timesteps''': 201,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__lowerCAmelCase : Optional[Any] = {
'''num_train_timesteps''': 151,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
def __snake_case ( UpperCamelCase ) -> List[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 __snake_case ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=False ) -> Optional[Any]:
"""simple docstring"""
a__ = checkpoint[f"{old_prefix}.in_layers.0.weight"]
a__ = checkpoint[f"{old_prefix}.in_layers.0.bias"]
a__ = checkpoint[f"{old_prefix}.in_layers.2.weight"]
a__ = checkpoint[f"{old_prefix}.in_layers.2.bias"]
a__ = checkpoint[f"{old_prefix}.emb_layers.1.weight"]
a__ = checkpoint[f"{old_prefix}.emb_layers.1.bias"]
a__ = checkpoint[f"{old_prefix}.out_layers.0.weight"]
a__ = checkpoint[f"{old_prefix}.out_layers.0.bias"]
a__ = checkpoint[f"{old_prefix}.out_layers.3.weight"]
a__ = checkpoint[f"{old_prefix}.out_layers.3.bias"]
if has_skip:
a__ = checkpoint[f"{old_prefix}.skip_connection.weight"]
a__ = checkpoint[f"{old_prefix}.skip_connection.bias"]
return new_checkpoint
def __snake_case ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=None ) -> List[Any]:
"""simple docstring"""
a__ , a__ , a__ = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3 , dim=0 )
a__ , a__ , a__ = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3 , dim=0 )
a__ = checkpoint[f"{old_prefix}.norm.weight"]
a__ = checkpoint[f"{old_prefix}.norm.bias"]
a__ = weight_q.squeeze(-1 ).squeeze(-1 )
a__ = bias_q.squeeze(-1 ).squeeze(-1 )
a__ = weight_k.squeeze(-1 ).squeeze(-1 )
a__ = bias_k.squeeze(-1 ).squeeze(-1 )
a__ = weight_v.squeeze(-1 ).squeeze(-1 )
a__ = bias_v.squeeze(-1 ).squeeze(-1 )
a__ = (
checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1 ).squeeze(-1 )
)
a__ = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def __snake_case ( UpperCamelCase , UpperCamelCase ) -> Tuple:
"""simple docstring"""
a__ = torch.load(UpperCamelCase , map_location='''cpu''' )
a__ = {}
a__ = checkpoint['''time_embed.0.weight''']
a__ = checkpoint['''time_embed.0.bias''']
a__ = checkpoint['''time_embed.2.weight''']
a__ = checkpoint['''time_embed.2.bias''']
if unet_config["num_class_embeds"] is not None:
a__ = checkpoint['''label_emb.weight''']
a__ = checkpoint['''input_blocks.0.0.weight''']
a__ = checkpoint['''input_blocks.0.0.bias''']
a__ = unet_config['''down_block_types''']
a__ = unet_config['''layers_per_block''']
a__ = unet_config['''attention_head_dim''']
a__ = unet_config['''block_out_channels''']
a__ = 1
a__ = channels_list[0]
for i, layer_type in enumerate(UpperCamelCase ):
a__ = channels_list[i]
a__ = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(UpperCamelCase ):
a__ = f"down_blocks.{i}.resnets.{j}"
a__ = f"input_blocks.{current_layer}.0"
a__ = True if j == 0 and downsample_block_has_skip else False
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , has_skip=UpperCamelCase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(UpperCamelCase ):
a__ = f"down_blocks.{i}.resnets.{j}"
a__ = f"input_blocks.{current_layer}.0"
a__ = True if j == 0 and downsample_block_has_skip else False
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , has_skip=UpperCamelCase )
a__ = f"down_blocks.{i}.attentions.{j}"
a__ = f"input_blocks.{current_layer}.1"
a__ = convert_attention(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
current_layer += 1
if i != len(UpperCamelCase ) - 1:
a__ = f"down_blocks.{i}.downsamplers.0"
a__ = f"input_blocks.{current_layer}.0"
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
current_layer += 1
a__ = current_channels
# hardcoded the mid-block for now
a__ = '''mid_block.resnets.0'''
a__ = '''middle_block.0'''
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
a__ = '''mid_block.attentions.0'''
a__ = '''middle_block.1'''
a__ = convert_attention(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
a__ = '''mid_block.resnets.1'''
a__ = '''middle_block.2'''
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
a__ = 0
a__ = unet_config['''up_block_types''']
for i, layer_type in enumerate(UpperCamelCase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
a__ = f"up_blocks.{i}.resnets.{j}"
a__ = f"output_blocks.{current_layer}.0"
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , has_skip=UpperCamelCase )
current_layer += 1
if i != len(UpperCamelCase ) - 1:
a__ = f"up_blocks.{i}.upsamplers.0"
a__ = f"output_blocks.{current_layer-1}.1"
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
a__ = f"up_blocks.{i}.resnets.{j}"
a__ = f"output_blocks.{current_layer}.0"
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , has_skip=UpperCamelCase )
a__ = f"up_blocks.{i}.attentions.{j}"
a__ = f"output_blocks.{current_layer}.1"
a__ = convert_attention(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
current_layer += 1
if i != len(UpperCamelCase ) - 1:
a__ = f"up_blocks.{i}.upsamplers.0"
a__ = f"output_blocks.{current_layer-1}.2"
a__ = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
a__ = checkpoint['''out.0.weight''']
a__ = checkpoint['''out.0.bias''']
a__ = checkpoint['''out.2.weight''']
a__ = checkpoint['''out.2.bias''']
return new_checkpoint
if __name__ == "__main__":
__lowerCAmelCase : Optional[Any] = 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.''')
__lowerCAmelCase : str = parser.parse_args()
__lowerCAmelCase : Union[str, Any] = strabool(args.class_cond)
__lowerCAmelCase : int = os.path.basename(args.unet_path)
print(f"Checkpoint: {ckpt_name}")
# Get U-Net config
if "imagenet64" in ckpt_name:
__lowerCAmelCase : List[str] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__lowerCAmelCase : Optional[Any] = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__lowerCAmelCase : Optional[Any] = TEST_UNET_CONFIG
else:
raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")
if not args.class_cond:
__lowerCAmelCase : int = None
__lowerCAmelCase : List[str] = con_pt_to_diffuser(args.unet_path, unet_config)
__lowerCAmelCase : Union[str, Any] = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__lowerCAmelCase : Union[str, Any] = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__lowerCAmelCase : int = 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)):
__lowerCAmelCase : Any = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")
__lowerCAmelCase : Any = CMStochasticIterativeScheduler(**scheduler_config)
__lowerCAmelCase : Optional[Any] = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 158 |
"""simple docstring"""
from __future__ import annotations
__lowerCAmelCase : Optional[int] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , ) -> tuple[list[list[int]], list[list[int]]]:
"""simple docstring"""
a__ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase ) )
] # the reference grid
a__ = 1
a__ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase ) )
] # the action grid
a__ = init[0]
a__ = init[1]
a__ = 0
a__ = g + heuristic[x][y] # cost from starting cell to destination cell
a__ = [[f, g, x, y]]
a__ = False # flag that is set when search is complete
a__ = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCamelCase ) == 0:
raise ValueError('''Algorithm is unable to find solution''' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
a__ = cell.pop()
a__ = next_cell[2]
a__ = next_cell[3]
a__ = next_cell[1]
if x == goal[0] and y == goal[1]:
a__ = True
else:
for i in range(len(UpperCamelCase ) ): # to try out different valid actions
a__ = x + DIRECTIONS[i][0]
a__ = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCamelCase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
a__ = g + cost
a__ = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
a__ = 1
a__ = i
a__ = []
a__ = goal[0]
a__ = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
a__ = x - DIRECTIONS[action[x][y]][0]
a__ = y - DIRECTIONS[action[x][y]][1]
a__ = xa
a__ = ya
invpath.append([x, y] )
a__ = []
for i in range(len(UpperCamelCase ) ):
path.append(invpath[len(UpperCamelCase ) - 1 - i] )
return path, action
if __name__ == "__main__":
__lowerCAmelCase : Any = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
__lowerCAmelCase : Optional[Any] = [0, 0]
# all coordinates are given in format [y,x]
__lowerCAmelCase : Optional[Any] = [len(grid) - 1, len(grid[0]) - 1]
__lowerCAmelCase : Optional[int] = 1
# the cost map which pushes the path closer to the goal
__lowerCAmelCase : str = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
__lowerCAmelCase : Optional[int] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__lowerCAmelCase : Optional[Any] = 99
__lowerCAmelCase ,__lowerCAmelCase : Optional[int] = search(grid, init, goal, cost, heuristic)
print('''ACTION MAP''')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 158 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class a_ ( unittest.TestCase ):
@slow
def _snake_case ( self : List[str] ) ->Dict:
'''simple docstring'''
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(__UpperCamelCase ):
_UpperCAmelCase = AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = FlaxAutoModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def _snake_case ( self : Union[str, Any] ) ->Union[str, Any]:
'''simple docstring'''
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(__UpperCamelCase ):
_UpperCAmelCase = AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = FlaxAutoModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def _snake_case ( self : Optional[Any] ) ->Tuple:
'''simple docstring'''
for model_name in ["bert-base-cased", "bert-large-uncased"]:
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = FlaxBertModel.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = tokenizer("""Do you support jax jitted function?""" , return_tensors=TensorType.JAX )
@jax.jit
def eval(**__UpperCamelCase : Tuple ):
return model(**__UpperCamelCase )
eval(**__UpperCamelCase ).block_until_ready()
@slow
def _snake_case ( self : Optional[int] ) ->Union[str, Any]:
'''simple docstring'''
for model_name in ["roberta-base", "roberta-large"]:
_UpperCAmelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = FlaxRobertaModel.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = tokenizer("""Do you support jax jitted function?""" , return_tensors=TensorType.JAX )
@jax.jit
def eval(**__UpperCamelCase : Union[str, Any] ):
return model(**__UpperCamelCase )
eval(**__UpperCamelCase ).block_until_ready()
def _snake_case ( self : Optional[int] ) ->Optional[int]:
'''simple docstring'''
with self.assertRaisesRegex(
__UpperCamelCase , """bert-base is not a local folder and is not a valid model identifier""" ):
_UpperCAmelCase = FlaxAutoModel.from_pretrained("""bert-base""" )
def _snake_case ( self : Tuple ) ->Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
__UpperCamelCase , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
_UpperCAmelCase = FlaxAutoModel.from_pretrained(__UpperCamelCase , revision="""aaaaaa""" )
def _snake_case ( self : Dict ) ->str:
'''simple docstring'''
with self.assertRaisesRegex(
__UpperCamelCase , """hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack""" , ):
_UpperCAmelCase = FlaxAutoModel.from_pretrained("""hf-internal-testing/config-no-model""" )
def _snake_case ( self : Dict ) ->List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(__UpperCamelCase , """Use `from_pt=True` to load this model""" ):
_UpperCAmelCase = FlaxAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only""" ) | 555 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
a : int = '''scheduler_config.json'''
class a_ ( _UpperCAmelCase ):
a : List[Any] = 1
a : Tuple = 2
a : Dict = 3
a : str = 4
a : Optional[int] = 5
a : Any = 6
a : int = 7
a : Any = 8
a : List[Any] = 9
a : Any = 10
a : List[str] = 11
a : Optional[int] = 12
a : Any = 13
a : Dict = 14
@dataclass
class a_ ( _UpperCAmelCase ):
a : torch.FloatTensor
class a_ :
a : Dict = SCHEDULER_CONFIG_NAME
a : List[str] = []
a : Optional[int] = True
@classmethod
def _snake_case ( cls : List[str] , __UpperCamelCase : Dict[str, Any] = None , __UpperCamelCase : Optional[str] = None , __UpperCamelCase : List[str]=False , **__UpperCamelCase : int , ) ->Dict:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase = cls.load_config(
pretrained_model_name_or_path=__UpperCamelCase , subfolder=__UpperCamelCase , return_unused_kwargs=__UpperCamelCase , return_commit_hash=__UpperCamelCase , **__UpperCamelCase , )
return cls.from_config(__UpperCamelCase , return_unused_kwargs=__UpperCamelCase , **__UpperCamelCase )
def _snake_case ( self : Tuple , __UpperCamelCase : Union[str, os.PathLike] , __UpperCamelCase : bool = False , **__UpperCamelCase : List[str] ) ->Any:
'''simple docstring'''
self.save_config(save_directory=__UpperCamelCase , push_to_hub=__UpperCamelCase , **__UpperCamelCase )
@property
def _snake_case ( self : Optional[int] ) ->List[Any]:
'''simple docstring'''
return self._get_compatibles()
@classmethod
def _snake_case ( cls : List[str] ) ->Dict:
'''simple docstring'''
_UpperCAmelCase = list(set([cls.__name__] + cls._compatibles ) )
_UpperCAmelCase = importlib.import_module(__name__.split(""".""" )[0] )
_UpperCAmelCase = [
getattr(__UpperCamelCase , __UpperCamelCase ) for c in compatible_classes_str if hasattr(__UpperCamelCase , __UpperCamelCase )
]
return compatible_classes | 555 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 5_0 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :List[Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 320 | '''simple docstring'''
UpperCamelCase_ = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
UpperCamelCase_ = ['''a''', '''b''', '''c''', '''d''', '''e''']
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Any ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Any = start
# add current to visited
visited.append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :int = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
SCREAMING_SNAKE_CASE__ :Dict = topological_sort(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# if all neighbors visited add current to sort
sort.append(UpperCAmelCase__ )
# if all vertices haven't been visited select a new one to visit
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
for vertice in vertices:
if vertice not in visited:
SCREAMING_SNAKE_CASE__ :Optional[int] = topological_sort(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# return sort
return sort
if __name__ == "__main__":
UpperCamelCase_ = topological_sort('''a''', [], [])
print(sort)
| 320 | 1 |
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