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'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_SCREAMING_SNAKE_CASE : Any = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : List[Any] = [
"ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoCBertForCausalLM",
"RoCBertForMaskedLM",
"RoCBertForMultipleChoice",
"RoCBertForPreTraining",
"RoCBertForQuestionAnswering",
"RoCBertForSequenceClassification",
"RoCBertForTokenClassification",
"RoCBertLayer",
"RoCBertModel",
"RoCBertPreTrainedModel",
"load_tf_weights_in_roc_bert",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
_SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 85
|
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def lowerCamelCase (*_SCREAMING_SNAKE_CASE : int ):
with open(_SCREAMING_SNAKE_CASE , 'r' ) as fh:
fcntl.flock(_SCREAMING_SNAKE_CASE , fcntl.LOCK_EX )
try:
print(*_SCREAMING_SNAKE_CASE )
finally:
fcntl.flock(_SCREAMING_SNAKE_CASE , fcntl.LOCK_UN )
__lowercase : Dict = int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase : Tuple = torch.device('cuda', local_rank)
__lowercase : Optional[int] = socket.gethostname()
__lowercase : List[str] = f'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group('nccl')
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
__lowercase : str = dist.get_rank()
__lowercase : Union[str, Any] = dist.get_world_size()
printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(f'''{gpu} is broken''')
raise
| 27
| 0
|
"""simple docstring"""
from math import pi, sqrt
def a__ ( __lowercase ) -> float:
if num <= 0:
raise ValueError("math domain error" )
if num > 171.5:
raise OverflowError("math range error" )
elif num - int(__lowercase ) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer" )
elif num == 0.5:
return sqrt(__lowercase )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def a__ ( ) -> None:
assert gamma(0.5 ) == sqrt(__lowercase )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
a_ = 1.0
while num:
a_ = float(input("Gamma of: "))
print(f'''gamma({num}) = {gamma(num)}''')
print("\nEnter 0 to exit...")
| 163
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_xlm_roberta": [
"XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaConfig",
"XLMRobertaOnnxConfig",
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["XLMRobertaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["XLMRobertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMRobertaForCausalLM",
"XLMRobertaForMaskedLM",
"XLMRobertaForMultipleChoice",
"XLMRobertaForQuestionAnswering",
"XLMRobertaForSequenceClassification",
"XLMRobertaForTokenClassification",
"XLMRobertaModel",
"XLMRobertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMRobertaForCausalLM",
"TFXLMRobertaForMaskedLM",
"TFXLMRobertaForMultipleChoice",
"TFXLMRobertaForQuestionAnswering",
"TFXLMRobertaForSequenceClassification",
"TFXLMRobertaForTokenClassification",
"TFXLMRobertaModel",
"TFXLMRobertaPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxXLMRobertaForMaskedLM",
"FlaxXLMRobertaForCausalLM",
"FlaxXLMRobertaForMultipleChoice",
"FlaxXLMRobertaForQuestionAnswering",
"FlaxXLMRobertaForSequenceClassification",
"FlaxXLMRobertaForTokenClassification",
"FlaxXLMRobertaModel",
"FlaxXLMRobertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 163
| 1
|
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase = """src/diffusers"""
lowerCAmelCase = """."""
# This is to make sure the diffusers module imported is the one in the repo.
lowerCAmelCase = importlib.util.spec_from_file_location(
"""diffusers""",
os.path.join(DIFFUSERS_PATH, """__init__.py"""),
submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCAmelCase = spec.loader.load_module()
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : List[Any] ) ->Union[str, Any]:
return line.startswith(snake_case_ ) or len(snake_case_ ) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$' , snake_case_ ) is not None
def lowerCAmelCase_ ( snake_case_ : int ) ->Optional[int]:
lowerCamelCase__ : Dict =object_name.split('.' )
lowerCamelCase__ : Tuple =0
# First let's find the module where our object lives.
lowerCamelCase__ : Union[str, Any] =parts[i]
while i < len(snake_case_ ) and not os.path.isfile(os.path.join(snake_case_ , f"""{module}.py""" ) ):
i += 1
if i < len(snake_case_ ):
lowerCamelCase__ : Dict =os.path.join(snake_case_ , parts[i] )
if i >= len(snake_case_ ):
raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(snake_case_ , f"""{module}.py""" ) , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCamelCase__ : Optional[Any] =f.readlines()
# Now let's find the class / func in the code!
lowerCamelCase__ : List[Any] =''
lowerCamelCase__ : Any =0
for name in parts[i + 1 :]:
while (
line_index < len(snake_case_ ) and re.search(Rf"""^{indent}(class|def)\s+{name}(\(|\:)""" , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(snake_case_ ):
raise ValueError(f""" {object_name} does not match any function or class in {module}.""" )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
lowerCamelCase__ : Optional[Any] =line_index
while line_index < len(snake_case_ ) and _should_continue(lines[line_index] , snake_case_ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
lowerCamelCase__ : List[str] =lines[start_index:line_index]
return "".join(snake_case_ )
lowerCAmelCase = re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
lowerCAmelCase = re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
lowerCAmelCase = re.compile(r"""<FILL\s+[^>]*>""")
def lowerCAmelCase_ ( snake_case_ : Tuple ) ->Tuple:
lowerCamelCase__ : Tuple =code.split('\n' )
lowerCamelCase__ : Union[str, Any] =0
while idx < len(snake_case_ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(snake_case_ ):
return re.search(R'^(\s*)\S' , lines[idx] ).groups()[0]
return ""
def lowerCAmelCase_ ( snake_case_ : List[str] ) ->Tuple:
lowerCamelCase__ : Union[str, Any] =len(get_indent(snake_case_ ) ) > 0
if has_indent:
lowerCamelCase__ : int =f"""class Bla:\n{code}"""
lowerCamelCase__ : Optional[int] =black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_1_9 , preview=snake_case_ )
lowerCamelCase__ : Dict =black.format_str(snake_case_ , mode=snake_case_ )
lowerCamelCase__ , lowerCamelCase__ : Dict =style_docstrings_in_code(snake_case_ )
return result[len('class Bla:\n' ) :] if has_indent else result
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : List[Any]=False ) ->List[Any]:
with open(snake_case_ , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCamelCase__ : Optional[int] =f.readlines()
lowerCamelCase__ : Optional[Any] =[]
lowerCamelCase__ : Optional[Any] =0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(snake_case_ ):
lowerCamelCase__ : Optional[Any] =_re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] =search.groups()
lowerCamelCase__ : Union[str, Any] =find_code_in_diffusers(snake_case_ )
lowerCamelCase__ : Dict =get_indent(snake_case_ )
lowerCamelCase__ : List[Any] =line_index + 1 if indent == theoretical_indent else line_index + 2
lowerCamelCase__ : List[Any] =theoretical_indent
lowerCamelCase__ : Union[str, Any] =start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
lowerCamelCase__ : Dict =True
while line_index < len(snake_case_ ) and should_continue:
line_index += 1
if line_index >= len(snake_case_ ):
break
lowerCamelCase__ : List[Any] =lines[line_index]
lowerCamelCase__ : Optional[int] =_should_continue(snake_case_ , snake_case_ ) and re.search(f"""^{indent}# End copy""" , snake_case_ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
lowerCamelCase__ : List[Any] =lines[start_index:line_index]
lowerCamelCase__ : Tuple =''.join(snake_case_ )
# Remove any nested `Copied from` comments to avoid circular copies
lowerCamelCase__ : str =[line for line in theoretical_code.split('\n' ) if _re_copy_warning.search(snake_case_ ) is None]
lowerCamelCase__ : List[str] ='\n'.join(snake_case_ )
# Before comparing, use the `replace_pattern` on the original code.
if len(snake_case_ ) > 0:
lowerCamelCase__ : Optional[Any] =replace_pattern.replace('with' , '' ).split(',' )
lowerCamelCase__ : str =[_re_replace_pattern.search(snake_case_ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict =pattern.groups()
lowerCamelCase__ : List[Any] =re.sub(snake_case_ , snake_case_ , snake_case_ )
if option.strip() == "all-casing":
lowerCamelCase__ : Union[str, Any] =re.sub(obja.lower() , obja.lower() , snake_case_ )
lowerCamelCase__ : Dict =re.sub(obja.upper() , obja.upper() , snake_case_ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
lowerCamelCase__ : Union[str, Any] =blackify(lines[start_index - 1] + theoretical_code )
lowerCamelCase__ : Dict =theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
lowerCamelCase__ : str =lines[:start_index] + [theoretical_code] + lines[line_index:]
lowerCamelCase__ : Optional[int] =start_index + 1
if overwrite and len(snake_case_ ) > 0:
# Warn the user a file has been modified.
print(f"""Detected changes, rewriting {filename}.""" )
with open(snake_case_ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(snake_case_ )
return diffs
def lowerCAmelCase_ ( snake_case_ : bool = False ) ->Any:
lowerCamelCase__ : str =glob.glob(os.path.join(snake_case_ , '**/*.py' ) , recursive=snake_case_ )
lowerCamelCase__ : Optional[int] =[]
for filename in all_files:
lowerCamelCase__ : Any =is_copy_consistent(snake_case_ , snake_case_ )
diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(snake_case_ ) > 0:
lowerCamelCase__ : Dict ='\n'.join(snake_case_ )
raise Exception(
'Found the following copy inconsistencies:\n'
+ diff
+ '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.' )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
lowerCAmelCase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 126
|
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase = logging.get_logger(__name__)
class A_ ( A__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = ["""audio_values""", """audio_mask"""]
def __init__( self :List[str] , lowerCamelCase_ :List[str]=2_048 , lowerCamelCase_ :Dict=1 , lowerCamelCase_ :int=[16, 16] , lowerCamelCase_ :str=128 , lowerCamelCase_ :Union[str, Any]=44_100 , lowerCamelCase_ :Optional[Any]=86 , lowerCamelCase_ :Dict=2_048 , lowerCamelCase_ :Union[str, Any]=0.0 , **lowerCamelCase_ :Tuple , ):
"""simple docstring"""
super().__init__(
feature_size=lowerCamelCase_ , sampling_rate=lowerCamelCase_ , padding_value=lowerCamelCase_ , **lowerCamelCase_ , )
lowerCamelCase__ : List[str] =spectrogram_length
lowerCamelCase__ : Dict =num_channels
lowerCamelCase__ : List[Any] =patch_size
lowerCamelCase__ : Union[str, Any] =feature_size // self.patch_size[1]
lowerCamelCase__ : int =n_fft
lowerCamelCase__ : List[str] =sampling_rate // hop_length_to_sampling_rate
lowerCamelCase__ : str =sampling_rate
lowerCamelCase__ : int =padding_value
lowerCamelCase__ : Dict =mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCamelCase_ , min_frequency=0.0 , max_frequency=2_20_50.0 , sampling_rate=lowerCamelCase_ , norm='slaney' , mel_scale='slaney' , ).T
def UpperCAmelCase__ ( self :Dict , lowerCamelCase_ :np.array ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =spectrogram(
lowerCamelCase_ , window_function(self.n_fft , 'hann' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='dB' , db_range=80.0 , )
lowerCamelCase__ : Any =log_spec[:, :-1]
lowerCamelCase__ : Tuple =log_spec - 20.0
lowerCamelCase__ : List[str] =np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self :Optional[Any] , lowerCamelCase_ :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , lowerCamelCase_ :Optional[Union[str, TensorType]] = None , lowerCamelCase_ :Optional[bool] = True , lowerCamelCase_ :Optional[int] = None , lowerCamelCase_ :bool = False , lowerCamelCase_ :bool = False , **lowerCamelCase_ :Tuple , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'This feature extractor is set to support sampling rate'
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
lowerCamelCase__ : Dict =isinstance(lowerCamelCase_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
lowerCamelCase__ : Union[str, Any] =is_batched_numpy or (
isinstance(lowerCamelCase_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCamelCase__ : Optional[Any] =[np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(lowerCamelCase_ , np.ndarray ):
lowerCamelCase__ : Optional[Any] =np.asarray(lowerCamelCase_ , dtype=np.floataa )
elif isinstance(lowerCamelCase_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCamelCase__ : Union[str, Any] =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCamelCase__ : List[str] =[np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
lowerCamelCase__ : Any =[
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , lowerCamelCase_ ):
lowerCamelCase__ : Dict =[np.asarray(lowerCamelCase_ , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
lowerCamelCase__ : Optional[Any] =max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
lowerCamelCase__ : Any =[
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
lowerCamelCase__ : Union[str, Any] =np.array(lowerCamelCase_ ).astype(np.floataa )
# convert into correct format for padding
lowerCamelCase__ : Tuple =max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
lowerCamelCase__ : str =np.ones([len(lowerCamelCase_ ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
lowerCamelCase__ : Dict =padded_audio_features * self.padding_value
for i in range(len(lowerCamelCase_ ) ):
lowerCamelCase__ : Union[str, Any] =audio_features[i]
lowerCamelCase__ : Union[str, Any] =feature
# return as BatchFeature
if return_attention_mask:
lowerCamelCase__ : int ={'audio_values': padded_audio_features, 'audio_mask': audio_mask}
else:
lowerCamelCase__ : Tuple ={'audio_values': padded_audio_features}
lowerCamelCase__ : Union[str, Any] =BatchFeature(data=lowerCamelCase_ , tensor_type=lowerCamelCase_ )
return encoded_inputs
| 126
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'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = set()
# edges = list of graph's edges
_snake_case = get_edges(_SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
_snake_case, _snake_case = edges.pop()
chosen_vertices.add(_SCREAMING_SNAKE_CASE )
chosen_vertices.add(_SCREAMING_SNAKE_CASE )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_SCREAMING_SNAKE_CASE )
return chosen_vertices
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 270
|
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase (self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowercase (self ) -> Dict:
_snake_case, _snake_case = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" , from_pt=UpperCAmelCase , dtype=jnp.bfloataa )
_snake_case, _snake_case = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=UpperCAmelCase , from_pt=UpperCAmelCase , dtype=jnp.bfloataa )
_snake_case = controlnet_params
_snake_case = """bird"""
_snake_case = jax.device_count()
_snake_case = pipe.prepare_text_inputs([prompts] * num_samples )
_snake_case = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
_snake_case = pipe.prepare_image_inputs([canny_image] * num_samples )
_snake_case = jax.random.PRNGKey(0 )
_snake_case = jax.random.split(UpperCAmelCase , jax.device_count() )
_snake_case = replicate(UpperCAmelCase )
_snake_case = shard(UpperCAmelCase )
_snake_case = shard(UpperCAmelCase )
_snake_case = pipe(
prompt_ids=UpperCAmelCase , image=UpperCAmelCase , params=UpperCAmelCase , prng_seed=UpperCAmelCase , num_inference_steps=50 , jit=UpperCAmelCase , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
_snake_case = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_snake_case = images[0, 253:256, 253:256, -1]
_snake_case = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_snake_case = jnp.array(
[0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def lowercase (self ) -> Optional[int]:
_snake_case, _snake_case = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" , from_pt=UpperCAmelCase , dtype=jnp.bfloataa )
_snake_case, _snake_case = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=UpperCAmelCase , from_pt=UpperCAmelCase , dtype=jnp.bfloataa )
_snake_case = controlnet_params
_snake_case = """Chef in the kitchen"""
_snake_case = jax.device_count()
_snake_case = pipe.prepare_text_inputs([prompts] * num_samples )
_snake_case = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
_snake_case = pipe.prepare_image_inputs([pose_image] * num_samples )
_snake_case = jax.random.PRNGKey(0 )
_snake_case = jax.random.split(UpperCAmelCase , jax.device_count() )
_snake_case = replicate(UpperCAmelCase )
_snake_case = shard(UpperCAmelCase )
_snake_case = shard(UpperCAmelCase )
_snake_case = pipe(
prompt_ids=UpperCAmelCase , image=UpperCAmelCase , params=UpperCAmelCase , prng_seed=UpperCAmelCase , num_inference_steps=50 , jit=UpperCAmelCase , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
_snake_case = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_snake_case = images[0, 253:256, 253:256, -1]
_snake_case = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_snake_case = jnp.array(
[[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 270
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|
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class a ( _a ):
UpperCamelCase : Any = """blenderbot-small"""
UpperCamelCase : List[Any] = ["""past_key_values"""]
UpperCamelCase : Union[str, Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : Tuple , lowerCAmelCase : Any=5_0265 , lowerCAmelCase : Any=512 , lowerCAmelCase : Dict=8 , lowerCAmelCase : List[Any]=2048 , lowerCAmelCase : Any=16 , lowerCAmelCase : Union[str, Any]=8 , lowerCAmelCase : List[str]=2048 , lowerCAmelCase : Optional[int]=16 , lowerCAmelCase : Tuple=0.0 , lowerCAmelCase : Optional[int]=0.0 , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : int=True , lowerCAmelCase : str="gelu" , lowerCAmelCase : Union[str, Any]=512 , lowerCAmelCase : Tuple=0.1 , lowerCAmelCase : Any=0.0 , lowerCAmelCase : List[Any]=0.0 , lowerCAmelCase : Optional[int]=0.0_2 , lowerCAmelCase : List[str]=1 , lowerCAmelCase : str=False , lowerCAmelCase : List[Any]=0 , lowerCAmelCase : Tuple=1 , lowerCAmelCase : Any=2 , lowerCAmelCase : Tuple=2 , **lowerCAmelCase : str , ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Dict =vocab_size
SCREAMING_SNAKE_CASE_: Dict =max_position_embeddings
SCREAMING_SNAKE_CASE_: Optional[int] =d_model
SCREAMING_SNAKE_CASE_: str =encoder_ffn_dim
SCREAMING_SNAKE_CASE_: str =encoder_layers
SCREAMING_SNAKE_CASE_: Any =encoder_attention_heads
SCREAMING_SNAKE_CASE_: Any =decoder_ffn_dim
SCREAMING_SNAKE_CASE_: List[str] =decoder_layers
SCREAMING_SNAKE_CASE_: str =decoder_attention_heads
SCREAMING_SNAKE_CASE_: Dict =dropout
SCREAMING_SNAKE_CASE_: str =attention_dropout
SCREAMING_SNAKE_CASE_: Union[str, Any] =activation_dropout
SCREAMING_SNAKE_CASE_: Tuple =activation_function
SCREAMING_SNAKE_CASE_: Optional[int] =init_std
SCREAMING_SNAKE_CASE_: Any =encoder_layerdrop
SCREAMING_SNAKE_CASE_: Optional[int] =decoder_layerdrop
SCREAMING_SNAKE_CASE_: Optional[Any] =use_cache
SCREAMING_SNAKE_CASE_: Dict =encoder_layers
SCREAMING_SNAKE_CASE_: List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , is_encoder_decoder=lowerCAmelCase , decoder_start_token_id=lowerCAmelCase , forced_eos_token_id=lowerCAmelCase , **lowerCAmelCase , )
class a ( _a ):
@property
def lowerCamelCase__ ( self : Any ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_: List[Any] =OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_: Tuple ={0: 'batch'}
SCREAMING_SNAKE_CASE_: Optional[int] ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
SCREAMING_SNAKE_CASE_: str ={0: 'batch', 1: 'decoder_sequence'}
SCREAMING_SNAKE_CASE_: int ={0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE_: Tuple =OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_: List[str] =self.num_layers
for i in range(lowerCAmelCase ):
SCREAMING_SNAKE_CASE_: int ={0: 'batch', 2: 'past_sequence + sequence'}
SCREAMING_SNAKE_CASE_: str ={0: 'batch', 2: 'past_sequence + sequence'}
else:
SCREAMING_SNAKE_CASE_: str =OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
def lowerCamelCase__ ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_: Any =super().outputs
else:
SCREAMING_SNAKE_CASE_: Tuple =super(lowerCAmelCase , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE_: int =self.num_layers
for i in range(lowerCAmelCase ):
SCREAMING_SNAKE_CASE_: Any ={0: 'batch', 2: 'past_sequence + sequence'}
SCREAMING_SNAKE_CASE_: Tuple ={0: 'batch', 2: 'past_sequence + sequence'}
return common_outputs
def lowerCamelCase__ ( self : Dict , lowerCAmelCase : str , lowerCAmelCase : List[Any] = -1 , lowerCAmelCase : Tuple = -1 , lowerCAmelCase : str = False , lowerCAmelCase : int = None , ) -> Mapping[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Optional[int] =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# Generate decoder inputs
SCREAMING_SNAKE_CASE_: str =seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE_: List[Any] =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: Optional[int] ={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE_: Any =dict(**lowerCAmelCase , **lowerCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
SCREAMING_SNAKE_CASE_: int =common_inputs['input_ids'].shape
SCREAMING_SNAKE_CASE_: Dict =common_inputs['decoder_input_ids'].shape[1]
SCREAMING_SNAKE_CASE_: int =self.num_attention_heads
SCREAMING_SNAKE_CASE_: Tuple =(
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_: Tuple =decoder_seq_length + 3
SCREAMING_SNAKE_CASE_: str =(
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE_: int =torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase , lowerCAmelCase )] , dim=1 )
SCREAMING_SNAKE_CASE_: Optional[int] =[]
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE_: Dict =self.num_layers
SCREAMING_SNAKE_CASE_: List[str] =min(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: str =max(lowerCAmelCase , lowerCAmelCase ) - min_num_layers
SCREAMING_SNAKE_CASE_: Optional[int] ='encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
for _ in range(lowerCAmelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowerCAmelCase ),
torch.zeros(lowerCAmelCase ),
torch.zeros(lowerCAmelCase ),
torch.zeros(lowerCAmelCase ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE_: List[Any] =encoder_shape if remaining_side_name == 'encoder' else decoder_shape
for _ in range(lowerCAmelCase , lowerCAmelCase ):
common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase ), torch.zeros(lowerCAmelCase )) )
return common_inputs
def lowerCamelCase__ ( self : int , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[int] = -1 , lowerCAmelCase : Any = -1 , lowerCAmelCase : List[Any] = False , lowerCAmelCase : List[str] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Any =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
SCREAMING_SNAKE_CASE_: Optional[Any] =common_inputs['input_ids'].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE_: Any =seqlen + 2
SCREAMING_SNAKE_CASE_: Dict =self.num_layers
SCREAMING_SNAKE_CASE_: Optional[Any] =self.num_attention_heads
SCREAMING_SNAKE_CASE_: Union[str, Any] =(
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_: Tuple =common_inputs['attention_mask'].dtype
SCREAMING_SNAKE_CASE_: Optional[int] =torch.cat(
[common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase , lowerCAmelCase , dtype=lowerCAmelCase )] , dim=1 )
SCREAMING_SNAKE_CASE_: Any =[
(torch.zeros(lowerCAmelCase ), torch.zeros(lowerCAmelCase )) for _ in range(lowerCAmelCase )
]
return common_inputs
def lowerCamelCase__ ( self : List[str] , lowerCAmelCase : Dict , lowerCAmelCase : Optional[int] = -1 , lowerCAmelCase : int = -1 , lowerCAmelCase : List[Any] = False , lowerCAmelCase : Tuple = None , ) -> Mapping[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Tuple =compute_effective_axis_dimension(
lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE_: List[str] =tokenizer.num_special_tokens_to_add(lowerCAmelCase )
SCREAMING_SNAKE_CASE_: int =compute_effective_axis_dimension(
lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCAmelCase )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE_: Optional[int] =[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE_: List[str] =dict(tokenizer(lowerCAmelCase , return_tensors=lowerCAmelCase ) )
return common_inputs
def lowerCamelCase__ ( self : Tuple , lowerCAmelCase : Any , lowerCAmelCase : List[str] = -1 , lowerCAmelCase : Optional[Any] = -1 , lowerCAmelCase : int = False , lowerCAmelCase : Tuple = None , ) -> Mapping[str, Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_: Optional[Any] =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowerCAmelCase , batch_size=lowerCAmelCase , seq_length=lowerCAmelCase , is_pair=lowerCAmelCase , framework=lowerCAmelCase )
elif self.task == "causal-lm":
SCREAMING_SNAKE_CASE_: Any =self._generate_dummy_inputs_for_causal_lm(
lowerCAmelCase , batch_size=lowerCAmelCase , seq_length=lowerCAmelCase , is_pair=lowerCAmelCase , framework=lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE_: Optional[int] =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCAmelCase , batch_size=lowerCAmelCase , seq_length=lowerCAmelCase , is_pair=lowerCAmelCase , framework=lowerCAmelCase )
return common_inputs
def lowerCamelCase__ ( self : Tuple , lowerCAmelCase : Optional[int] , lowerCAmelCase : int , lowerCAmelCase : Any , lowerCAmelCase : int ) -> int:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_: List[Any] =super()._flatten_past_key_values_(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE_: Tuple =super(lowerCAmelCase , self )._flatten_past_key_values_(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
| 173
|
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater than 0' )
_lowercase : Tuple = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21
| 0
|
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_lowerCAmelCase :List[Any] = logging.get_logger(__name__)
def lowerCamelCase_ (UpperCamelCase__ : str , UpperCamelCase__ : List[Any] ):
_UpperCAmelCase : Optional[Any] = nn.functional.normalize(UpperCamelCase__ )
_UpperCAmelCase : List[Any] = nn.functional.normalize(UpperCamelCase__ )
return torch.mm(UpperCamelCase__ , normalized_text_embeds.t() )
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ =CLIPConfig
a__ =['''CLIPEncoderLayer''']
def __init__( self , A ) -> Union[str, Any]:
super().__init__(A )
_UpperCAmelCase : Any = CLIPVisionModel(config.vision_config )
_UpperCAmelCase : Optional[int] = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=A )
_UpperCAmelCase : Optional[Any] = nn.Parameter(torch.ones(1_7 , config.projection_dim ) , requires_grad=A )
_UpperCAmelCase : int = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=A )
_UpperCAmelCase : str = nn.Parameter(torch.ones(1_7 ) , requires_grad=A )
_UpperCAmelCase : Any = nn.Parameter(torch.ones(3 ) , requires_grad=A )
@torch.no_grad()
def __lowerCAmelCase ( self , A , A ) -> List[Any]:
_UpperCAmelCase : str = self.vision_model(A )[1] # pooled_output
_UpperCAmelCase : Any = self.visual_projection(A )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_UpperCAmelCase : str = cosine_distance(A , self.special_care_embeds ).cpu().float().numpy()
_UpperCAmelCase : List[str] = cosine_distance(A , self.concept_embeds ).cpu().float().numpy()
_UpperCAmelCase : Any = []
_UpperCAmelCase : Dict = image_embeds.shape[0]
for i in range(A ):
_UpperCAmelCase : Union[str, Any] = {'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
_UpperCAmelCase : Tuple = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
_UpperCAmelCase : Optional[Any] = special_cos_dist[i][concept_idx]
_UpperCAmelCase : str = self.special_care_embeds_weights[concept_idx].item()
_UpperCAmelCase : Optional[int] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]} )
_UpperCAmelCase : int = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
_UpperCAmelCase : Any = cos_dist[i][concept_idx]
_UpperCAmelCase : Optional[int] = self.concept_embeds_weights[concept_idx].item()
_UpperCAmelCase : List[Any] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(A )
result.append(A )
_UpperCAmelCase : List[str] = [len(res['''bad_concepts'''] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def __lowerCAmelCase ( self , A , A ) -> List[Any]:
_UpperCAmelCase : Dict = self.vision_model(A )[1] # pooled_output
_UpperCAmelCase : Any = self.visual_projection(A )
_UpperCAmelCase : Optional[Any] = cosine_distance(A , self.special_care_embeds )
_UpperCAmelCase : List[Any] = cosine_distance(A , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
_UpperCAmelCase : int = 0.0
_UpperCAmelCase : Any = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
_UpperCAmelCase : List[Any] = torch.any(special_scores > 0 , dim=1 )
_UpperCAmelCase : Tuple = special_care * 0.01
_UpperCAmelCase : str = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
_UpperCAmelCase : Optional[int] = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
_UpperCAmelCase : str = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 359
|
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
_UpperCAmelCase , _UpperCAmelCase : int = array[indexa], array[indexa]
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if length > 1:
_UpperCAmelCase : str = int(length / 2 )
for i in range(UpperCamelCase__ , low + middle ):
comp_and_swap(UpperCamelCase__ , UpperCamelCase__ , i + middle , UpperCamelCase__ )
bitonic_merge(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
bitonic_merge(UpperCamelCase__ , low + middle , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if length > 1:
_UpperCAmelCase : str = int(length / 2 )
bitonic_sort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , 1 )
bitonic_sort(UpperCamelCase__ , low + middle , UpperCamelCase__ , 0 )
bitonic_merge(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
_lowerCAmelCase :Any = input('Enter numbers separated by a comma:\n').strip()
_lowerCAmelCase :Tuple = [int(item.strip()) for item in user_input.split(',')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('\nSorted array in ascending order is: ', end='')
print(*unsorted, sep=', ')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('Sorted array in descending order is: ', end='')
print(*unsorted, sep=', ')
| 68
| 0
|
'''simple docstring'''
from collections import defaultdict
class A__ :
def __init__( self :Optional[int] , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :Union[str, Any] ) -> int:
'''simple docstring'''
_a : List[Any] =total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
_a : Union[str, Any] =[
[-1 for i in range(total + 1 )] for j in range(2 ** len(UpperCamelCase_ ) )
]
_a : str =defaultdict(UpperCamelCase_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
_a : Optional[int] =(1 << len(UpperCamelCase_ )) - 1
def __UpperCAmelCase ( self :Optional[int] , SCREAMING_SNAKE_CASE :List[str] , SCREAMING_SNAKE_CASE :int ) -> Optional[int]:
'''simple docstring'''
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
_a : str =self.count_ways_until(UpperCamelCase_ , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
_a : Optional[int] =total_ways_util
return self.dp[mask][task_no]
def __UpperCAmelCase ( self :str , SCREAMING_SNAKE_CASE :int ) -> List[Any]:
'''simple docstring'''
for i in range(len(UpperCamelCase_ ) ):
for j in task_performed[i]:
self.task[j].append(UpperCamelCase_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
A__: Optional[int] = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
A__: Optional[int] = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 276
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__snake_case = True
except (ImportError, ModuleNotFoundError):
__snake_case = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def a ( __a ) -> str:
'''simple docstring'''
re.sub('''<n>''' , '''''' , __a ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__a ) )
| 97
| 0
|
import argparse
from collections import defaultdict
def lowerCamelCase ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with open(_UpperCamelCase , '''r''' ) as f:
__UpperCamelCase :Union[str, Any] = f.readlines()
__UpperCamelCase :int = f"""class {class_name}("""
__UpperCamelCase :List[str] = f"""{4 * ' '}def {test_name}("""
__UpperCamelCase :str = f"""{8 * ' '}{correct_line.split()[0]}"""
__UpperCamelCase :Union[str, Any] = f"""{16 * ' '}{correct_line.split()[0]}"""
__UpperCamelCase :Dict = False
__UpperCamelCase :str = False
__UpperCamelCase :Optional[Any] = False
__UpperCamelCase :List[Any] = False
__UpperCamelCase :int = 0
__UpperCamelCase :Optional[int] = 0
__UpperCamelCase :str = []
for line in lines:
if line.startswith(_UpperCamelCase ):
__UpperCamelCase :Any = True
elif in_class and line.startswith(_UpperCamelCase ):
__UpperCamelCase :Optional[int] = True
elif in_class and in_func and (line.startswith(_UpperCamelCase ) or line.startswith(_UpperCamelCase )):
__UpperCamelCase :Optional[int] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
__UpperCamelCase :str = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
__UpperCamelCase :Union[str, Any] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(f"""{spaces * ' '}{correct_line}""" )
__UpperCamelCase :Any = False
else:
new_lines.append(_UpperCamelCase )
with open(_UpperCamelCase , '''w''' ) as f:
for line in new_lines:
f.write(_UpperCamelCase )
def lowerCamelCase ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Any=None ):
'''simple docstring'''
if fail is not None:
with open(_UpperCamelCase , '''r''' ) as f:
__UpperCamelCase :Union[str, Any] = {l.strip() for l in f.readlines()}
else:
__UpperCamelCase :Optional[Any] = None
with open(_UpperCamelCase , '''r''' ) as f:
__UpperCamelCase :Optional[Any] = f.readlines()
__UpperCamelCase :List[Any] = defaultdict(_UpperCamelCase )
for line in correct_lines:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase :str = line.split(''';''' )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
__lowercase = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 359
|
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__lowercase = '''
import os
'''
__lowercase = '''
def foo():
import os
return False
'''
__lowercase = '''
def foo():
def bar():
if True:
import os
return False
return bar()
'''
__lowercase = '''
import os
try:
import bar
except ImportError:
raise ValueError()
'''
__lowercase = '''
import os
def foo():
try:
import bar
except ImportError:
raise ValueError()
'''
__lowercase = '''
import os
try:
import bar
except (ImportError, AttributeError):
raise ValueError()
'''
__lowercase = '''
import os
try:
import bar
except ImportError as e:
raise ValueError()
'''
__lowercase = '''
import os
try:
import bar
except:
raise ValueError()
'''
__lowercase = '''
import os
try:
import bar
import baz
except ImportError:
raise ValueError()
'''
__lowercase = '''
import os
try:
import bar
import baz
except ImportError:
x = 1
raise ValueError()
'''
__lowercase = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case''' , SCREAMING_SNAKE_CASE )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :List[Any] = os.path.join(SCREAMING_SNAKE_CASE , '''test_file.py''' )
with open(SCREAMING_SNAKE_CASE , '''w''' ) as _tmp_file:
_tmp_file.write(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Dict = get_imports(SCREAMING_SNAKE_CASE )
assert parsed_imports == ["os"]
| 105
| 0
|
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_a = 16
_a = 32
def __a ( __lowerCamelCase, __lowerCamelCase = 16, __lowerCamelCase = "bert-base-cased" ):
UpperCAmelCase_ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
UpperCAmelCase_ : List[Any] = load_dataset("glue", "mrpc" )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_ : str = tokenizer(examples["sentence1"], examples["sentence2"], truncation=__lowerCAmelCase, max_length=__lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase_ : Union[str, Any] = datasets.map(
__lowerCAmelCase, batched=__lowerCAmelCase, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=__lowerCAmelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase_ : List[Any] = tokenized_datasets.rename_column("label", "labels" )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__lowerCAmelCase, padding="max_length", max_length=128, return_tensors="pt" )
return tokenizer.pad(__lowerCAmelCase, padding="longest", return_tensors="pt" )
# Instantiate dataloaders.
UpperCAmelCase_ : str = DataLoader(
tokenized_datasets["train"], shuffle=__lowerCAmelCase, collate_fn=__lowerCAmelCase, batch_size=__lowerCAmelCase )
UpperCAmelCase_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"], shuffle=__lowerCAmelCase, collate_fn=__lowerCAmelCase, batch_size=__lowerCAmelCase )
return train_dataloader, eval_dataloader
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
model.eval()
UpperCAmelCase_ : Tuple = 0
for step, batch in enumerate(__lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase_ : Dict = model(**__lowerCAmelCase )
UpperCAmelCase_ : int = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase_ : Optional[Any] = accelerator.gather(
(predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__lowerCAmelCase ) - 1:
UpperCAmelCase_ : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase_ : Union[str, Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__lowerCAmelCase, references=__lowerCAmelCase, )
UpperCAmelCase_ : Dict = metric.compute()
return eval_metric["accuracy"]
def __a ( __lowerCamelCase, __lowerCamelCase ):
# Initialize accelerator
UpperCAmelCase_ : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase_ : List[str] = config["""lr"""]
UpperCAmelCase_ : Any = int(config["num_epochs"] )
UpperCAmelCase_ : Optional[Any] = int(config["seed"] )
UpperCAmelCase_ : Union[str, Any] = int(config["batch_size"] )
UpperCAmelCase_ : List[str] = args.model_name_or_path
set_seed(__lowerCAmelCase )
UpperCAmelCase_ : str = get_dataloaders(__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase_ : Tuple = AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase, return_dict=__lowerCAmelCase )
# Instantiate optimizer
UpperCAmelCase_ : Optional[int] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase_ : Optional[Any] = optimizer_cls(params=model.parameters(), lr=__lowerCAmelCase )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase_ : Any = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
UpperCAmelCase_ : List[Any] = 1
UpperCAmelCase_ : str = (len(__lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCAmelCase_ : Dict = get_linear_schedule_with_warmup(
optimizer=__lowerCAmelCase, num_warmup_steps=0, num_training_steps=__lowerCAmelCase, )
else:
UpperCAmelCase_ : List[Any] = DummyScheduler(__lowerCAmelCase, total_num_steps=__lowerCAmelCase, warmup_num_steps=0 )
# 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_ : List[Any] = accelerator.prepare(
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase_ : str = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase_ : Tuple = 0
UpperCAmelCase_ : Union[str, Any] = evaluate.load("glue", "mrpc" )
UpperCAmelCase_ : Union[str, Any] = num_epochs
if args.partial_train_epoch is not None:
UpperCAmelCase_ : int = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
UpperCAmelCase_ : int = args.resume_from_checkpoint.split("epoch_" )[1]
UpperCAmelCase_ : Tuple = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
UpperCAmelCase_ : Optional[Any] = int(__lowerCAmelCase ) + 1
UpperCAmelCase_ : Tuple = evaluation_loop(__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase )
accelerator.print("resumed checkpoint performance:", __lowerCAmelCase )
accelerator.print("resumed checkpoint's scheduler's lr:", lr_scheduler.get_lr()[0] )
accelerator.print("resumed optimizers's lr:", optimizer.param_groups[0]["lr"] )
with open(os.path.join(args.output_dir, f"""state_{starting_epoch-1}.json""" ), "r" ) as f:
UpperCAmelCase_ : Dict = json.load(__lowerCAmelCase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
UpperCAmelCase_ : List[str] = {}
for epoch in range(__lowerCAmelCase, __lowerCAmelCase ):
model.train()
for step, batch in enumerate(__lowerCAmelCase ):
UpperCAmelCase_ : Dict = model(**__lowerCAmelCase )
UpperCAmelCase_ : Dict = outputs.loss
UpperCAmelCase_ : Tuple = loss / gradient_accumulation_steps
accelerator.backward(__lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
UpperCAmelCase_ : Union[str, Any] = f"""epoch_{epoch}"""
UpperCAmelCase_ : Optional[int] = os.path.join(args.output_dir, __lowerCAmelCase )
accelerator.save_state(__lowerCAmelCase )
UpperCAmelCase_ : int = evaluation_loop(__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase )
UpperCAmelCase_ : Dict = accuracy
UpperCAmelCase_ : Tuple = lr_scheduler.get_lr()[0]
UpperCAmelCase_ : int = optimizer.param_groups[0]["""lr"""]
UpperCAmelCase_ : Any = epoch
UpperCAmelCase_ : List[str] = overall_step
accelerator.print(f"""epoch {epoch}:""", __lowerCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir, f"""state_{epoch}.json""" ), "w" ) as f:
json.dump(__lowerCAmelCase, __lowerCAmelCase )
def __a ( ):
UpperCAmelCase_ : Dict = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
parser.add_argument(
"--model_name_or_path", type=__lowerCAmelCase, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=__lowerCAmelCase, )
parser.add_argument(
"--output_dir", type=__lowerCAmelCase, 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=__lowerCAmelCase, default=__lowerCAmelCase, help="If the training should continue from a checkpoint folder.", )
parser.add_argument(
"--partial_train_epoch", type=__lowerCAmelCase, default=__lowerCAmelCase, help="If passed, the training will stop after this number of epochs.", )
parser.add_argument(
"--num_epochs", type=__lowerCAmelCase, default=2, help="Number of train epochs.", )
UpperCAmelCase_ : Any = parser.parse_args()
UpperCAmelCase_ : Union[str, Any] = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(__lowerCAmelCase, __lowerCAmelCase )
if __name__ == "__main__":
main()
| 61
|
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BridgeTowerImageProcessor
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a = True , _a = None , _a = 32 , _a = True , _a = 1 / 255 , _a = True , _a = True , _a = [0.48_145_466, 0.4_578_275, 0.40_821_073] , _a = [0.26_862_954, 0.26_130_258, 0.27_577_711] , _a = True , _a=7 , _a=30 , _a=400 , _a=3 , ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = parent
SCREAMING_SNAKE_CASE__ : Tuple = do_resize
SCREAMING_SNAKE_CASE__ : Union[str, Any] = size if size is not None else {"""shortest_edge""": 288}
SCREAMING_SNAKE_CASE__ : List[str] = size_divisor
SCREAMING_SNAKE_CASE__ : Tuple = do_rescale
SCREAMING_SNAKE_CASE__ : List[str] = rescale_factor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = do_normalize
SCREAMING_SNAKE_CASE__ : List[str] = do_center_crop
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_mean
SCREAMING_SNAKE_CASE__ : List[str] = image_std
SCREAMING_SNAKE_CASE__ : List[str] = do_pad
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : int = num_channels
SCREAMING_SNAKE_CASE__ : Dict = min_resolution
SCREAMING_SNAKE_CASE__ : str = max_resolution
def _a ( self ) -> List[str]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
}
def _a ( self , _a , _a=False ) -> int:
"""simple docstring"""
if not batched:
SCREAMING_SNAKE_CASE__ : List[Any] = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_inputs[0]
if isinstance(_a , Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = image.size
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = image.shape[1], image.shape[2]
SCREAMING_SNAKE_CASE__ : Tuple = size / min(_a , _a )
if h < w:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = size, scale * w
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = scale * h, size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = int((1_333 / 800) * size )
if max(_a , _a ) > max_size:
SCREAMING_SNAKE_CASE__ : List[str] = max_size / max(_a , _a )
SCREAMING_SNAKE_CASE__ : Any = newh * scale
SCREAMING_SNAKE_CASE__ : Any = neww * scale
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = int(newh + 0.5 ), int(neww + 0.5 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
SCREAMING_SNAKE_CASE__ : Dict = []
for image in image_inputs:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE__ : Any = max(_a , key=lambda _a : item[0] )[0]
SCREAMING_SNAKE_CASE__ : Any = max(_a , key=lambda _a : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = BridgeTowerImageProcessor if is_vision_available() else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = BridgeTowerImageProcessingTester(self )
@property
def _a ( self ) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , """image_mean""" ) )
self.assertTrue(hasattr(_a , """image_std""" ) )
self.assertTrue(hasattr(_a , """do_normalize""" ) )
self.assertTrue(hasattr(_a , """do_resize""" ) )
self.assertTrue(hasattr(_a , """size""" ) )
self.assertTrue(hasattr(_a , """size_divisor""" ) )
def _a ( self ) -> List[str]:
"""simple docstring"""
pass
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : 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
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[str] = image_processing(_a , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : 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
SCREAMING_SNAKE_CASE__ : Any = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processing(_a , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : int = 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
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Dict = image_processing(_a , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
| 132
| 0
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_A = logging.get_logger(__name__)
class lowercase_ ( __SCREAMING_SNAKE_CASE ):
A__ : Union[str, Any] = ["""pixel_values"""]
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_5_5 , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase )
UpperCamelCase_ = size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
UpperCamelCase_ = crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
UpperCamelCase_ = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase , param_name="""crop_size""" )
UpperCamelCase_ = do_resize
UpperCamelCase_ = do_rescale
UpperCamelCase_ = do_normalize
UpperCamelCase_ = do_center_crop
UpperCamelCase_ = crop_size
UpperCamelCase_ = size
UpperCamelCase_ = resample
UpperCamelCase_ = rescale_factor
UpperCamelCase_ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCamelCase_ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if "shortest_edge" in size:
UpperCamelCase_ = get_resize_output_image_size(__UpperCamelCase , size=size["""shortest_edge"""] , default_to_square=__UpperCamelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
UpperCamelCase_ = (size["""height"""], size["""width"""])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(__UpperCamelCase , size=(size["""height"""], size["""width"""]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase ):
"""simple docstring"""
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
return normalize(__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCamelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCamelCase_ = crop_size if crop_size is not None else self.crop_size
UpperCamelCase_ = get_size_dict(__UpperCamelCase , param_name="""crop_size""" , default_to_square=__UpperCamelCase )
UpperCamelCase_ = resample if resample is not None else self.resample
UpperCamelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCamelCase_ = image_std if image_std is not None else self.image_std
UpperCamelCase_ = size if size is not None else self.size
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if not is_batched(__UpperCamelCase ):
UpperCamelCase_ = [images]
if not valid_images(__UpperCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase_ = [to_numpy_array(__UpperCamelCase ) for image in images]
if do_resize:
UpperCamelCase_ = [self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase ) for image in images]
if do_center_crop:
UpperCamelCase_ = [self.center_crop(image=__UpperCamelCase , size=__UpperCamelCase ) for image in images]
if do_rescale:
UpperCamelCase_ = [self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase ) for image in images]
if do_normalize:
UpperCamelCase_ = [self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase ) for image in images]
UpperCamelCase_ = [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase ) for image in images]
UpperCamelCase_ = {"""pixel_values""": images}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 261
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_A = logging.get_logger(__name__)
class lowercase_ ( __SCREAMING_SNAKE_CASE ):
A__ : Union[str, Any] = ["""pixel_values"""]
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_5_5 , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase )
UpperCamelCase_ = size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
UpperCamelCase_ = crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
UpperCamelCase_ = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase , param_name="""crop_size""" )
UpperCamelCase_ = do_resize
UpperCamelCase_ = do_rescale
UpperCamelCase_ = do_normalize
UpperCamelCase_ = do_center_crop
UpperCamelCase_ = crop_size
UpperCamelCase_ = size
UpperCamelCase_ = resample
UpperCamelCase_ = rescale_factor
UpperCamelCase_ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCamelCase_ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if "shortest_edge" in size:
UpperCamelCase_ = get_resize_output_image_size(__UpperCamelCase , size=size["""shortest_edge"""] , default_to_square=__UpperCamelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
UpperCamelCase_ = (size["""height"""], size["""width"""])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(__UpperCamelCase , size=(size["""height"""], size["""width"""]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase ):
"""simple docstring"""
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
return normalize(__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCamelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCamelCase_ = crop_size if crop_size is not None else self.crop_size
UpperCamelCase_ = get_size_dict(__UpperCamelCase , param_name="""crop_size""" , default_to_square=__UpperCamelCase )
UpperCamelCase_ = resample if resample is not None else self.resample
UpperCamelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCamelCase_ = image_std if image_std is not None else self.image_std
UpperCamelCase_ = size if size is not None else self.size
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if not is_batched(__UpperCamelCase ):
UpperCamelCase_ = [images]
if not valid_images(__UpperCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase_ = [to_numpy_array(__UpperCamelCase ) for image in images]
if do_resize:
UpperCamelCase_ = [self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase ) for image in images]
if do_center_crop:
UpperCamelCase_ = [self.center_crop(image=__UpperCamelCase , size=__UpperCamelCase ) for image in images]
if do_rescale:
UpperCamelCase_ = [self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase ) for image in images]
if do_normalize:
UpperCamelCase_ = [self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase ) for image in images]
UpperCamelCase_ = [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase ) for image in images]
UpperCamelCase_ = {"""pixel_values""": images}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 261
| 1
|
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
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_ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase_ = {
'tokenizer_file': {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
},
}
UpperCamelCase_ = {
'gpt-neox-20b': 2048,
}
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : Optional[int] = VOCAB_FILES_NAMES
a_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
a_ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ : str = ["""input_ids""", """attention_mask"""]
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase="<|endoftext|>" , __UpperCAmelCase=False , **__UpperCAmelCase , ) ->Union[str, Any]:
super().__init__(
__UpperCAmelCase , __UpperCAmelCase , tokenizer_file=__UpperCAmelCase , unk_token=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , **__UpperCAmelCase , )
a_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space" , __UpperCAmelCase) != add_prefix_space:
a_ = getattr(__UpperCAmelCase , pre_tok_state.pop("type"))
a_ = add_prefix_space
a_ = pre_tok_class(**__UpperCAmelCase)
a_ = add_prefix_space
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase = None) ->Tuple[str]:
a_ = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase)
return tuple(__UpperCAmelCase)
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->List[int]:
a_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase) + [self.eos_token_id])
if len(__UpperCAmelCase) > self.model_max_length:
a_ = input_ids[-self.model_max_length :]
return input_ids
| 243
|
"""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 , __UpperCAmelCase = "cpu" , __UpperCAmelCase = "openai/clip-vit-large-patch14") ->None:
a_ = device
a_ = CLIPTokenizerFast.from_pretrained(__UpperCAmelCase)
a_ = [0.48_145_466, 0.4_578_275, 0.40_821_073]
a_ = [0.26_862_954, 0.26_130_258, 0.27_577_711]
a_ = torchvision.transforms.Normalize(self.image_mean , self.image_std)
a_ = torchvision.transforms.Resize(2_24)
a_ = torchvision.transforms.CenterCrop(2_24)
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->List[Any]:
a_ = self.resize(__UpperCAmelCase)
a_ = self.center_crop(__UpperCAmelCase)
a_ = self.normalize(__UpperCAmelCase)
return images
def __call__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase) ->Union[str, Any]:
a_ = self.tokenizer(text=__UpperCAmelCase , **__UpperCAmelCase)
a_ = self.preprocess_img(__UpperCAmelCase)
a_ = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class snake_case ( nn.Module ):
def __init__( self , __UpperCAmelCase=10 , __UpperCAmelCase=0.01 , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase="image" , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=False , ) ->None:
super().__init__()
a_ = None
a_ = device if device else get_device()
if vqgan:
a_ = vqgan
else:
a_ = load_vqgan(self.device , conf_path=__UpperCAmelCase , ckpt_path=__UpperCAmelCase)
self.vqgan.eval()
if clip:
a_ = clip
else:
a_ = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.clip.to(self.device)
a_ = ProcessorGradientFlow(device=self.device)
a_ = iterations
a_ = lr
a_ = log
a_ = make_grid
a_ = return_val
a_ = quantize
a_ = self.vqgan.decoder.z_shape
def UpperCAmelCase__ ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=5 , __UpperCAmelCase=True) ->Any:
a_ = []
if output_path is None:
a_ = "./animation.gif"
if input_path is None:
a_ = self.save_path
a_ = sorted(glob(input_path + "/*"))
if not len(__UpperCAmelCase):
raise ValueError(
"No images found in save path, aborting (did you pass save_intermediate=True to the generate"
" function?)")
if len(__UpperCAmelCase) == 1:
print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
a_ = total_duration / len(__UpperCAmelCase)
a_ = [frame_duration] * len(__UpperCAmelCase)
if extend_frames:
a_ = 1.5
a_ = 3
for file_name in paths:
if file_name.endswith(".png"):
images.append(imageio.imread(__UpperCAmelCase))
imageio.mimsave(__UpperCAmelCase , __UpperCAmelCase , duration=__UpperCAmelCase)
print(F'''gif saved to {output_path}''')
def UpperCAmelCase__ ( self , __UpperCAmelCase=None , __UpperCAmelCase=None) ->List[Any]:
if not (path or img):
raise ValueError("Input either path or tensor")
if img is not None:
raise NotImplementedError
a_ = preprocess(Image.open(__UpperCAmelCase) , target_image_size=2_56).to(self.device)
a_ = preprocess_vqgan(__UpperCAmelCase)
a_ , *a_ = self.vqgan.encode(__UpperCAmelCase)
return z
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->Tuple:
a_ = self.latent.detach().requires_grad_()
a_ = base_latent + transform_vector
if self.quantize:
a_ , *a_ = self.vqgan.quantize(__UpperCAmelCase)
else:
a_ = trans_latent
return self.vqgan.decode(__UpperCAmelCase)
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None) ->str:
a_ = self.clip_preprocessor(text=__UpperCAmelCase , images=__UpperCAmelCase , return_tensors="pt" , padding=__UpperCAmelCase)
a_ = self.clip(**__UpperCAmelCase)
a_ = clip_outputs.logits_per_image
if weights is not None:
a_ = similarity_logits * weights
return similarity_logits.sum()
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Optional[int]:
a_ = self._get_clip_similarity(pos_prompts["prompts"] , __UpperCAmelCase , weights=(1 / pos_prompts["weights"]))
if neg_prompts:
a_ = self._get_clip_similarity(neg_prompts["prompts"] , __UpperCAmelCase , weights=neg_prompts["weights"])
else:
a_ = torch.tensor([1] , device=self.device)
a_ = -torch.log(__UpperCAmelCase) + torch.log(__UpperCAmelCase)
return loss
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->int:
a_ = torch.randn_like(self.latent , requires_grad=__UpperCAmelCase , device=self.device)
a_ = torch.optim.Adam([vector] , lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
a_ = self._add_vector(__UpperCAmelCase)
a_ = loop_post_process(__UpperCAmelCase)
a_ = self._get_CLIP_loss(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
print("CLIP loss" , __UpperCAmelCase)
if self.log:
wandb.log({"CLIP Loss": clip_loss})
clip_loss.backward(retain_graph=__UpperCAmelCase)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Tuple:
wandb.init(reinit=__UpperCAmelCase , 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:
a_ = Image.open(__UpperCAmelCase)
a_ = image.resize((2_56, 2_56))
wandb.log("Original Image" , wandb.Image(__UpperCAmelCase))
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->List[str]:
if not prompts:
return []
a_ = []
a_ = []
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
a_ = [prompt.strip() for prompt in prompts.split("|")]
for prompt in prompts:
if isinstance(__UpperCAmelCase , (tuple, list)):
a_ = prompt[0]
a_ = float(prompt[1])
elif ":" in prompt:
a_ , a_ = prompt.split(":")
a_ = float(__UpperCAmelCase)
else:
a_ = prompt
a_ = 1.0
processed_prompts.append(__UpperCAmelCase)
weights.append(__UpperCAmelCase)
return {
"prompts": processed_prompts,
"weights": torch.tensor(__UpperCAmelCase , device=self.device),
}
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=None , ) ->List[Any]:
if image_path:
a_ = self._get_latent(__UpperCAmelCase)
else:
a_ = torch.randn(self.latent_dim , device=self.device)
if self.log:
self._init_logging(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
assert pos_prompts, "You must provide at least one positive prompt."
a_ = self.process_prompts(__UpperCAmelCase)
a_ = self.process_prompts(__UpperCAmelCase)
if save_final and save_path is None:
a_ = os.path.join("./outputs/" , "_".join(pos_prompts["prompts"]))
if not os.path.exists(__UpperCAmelCase):
os.makedirs(__UpperCAmelCase)
else:
a_ = save_path + "_" + get_timestamp()
os.makedirs(__UpperCAmelCase)
a_ = save_path
a_ = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print("Original Image")
show_pil(custom_to_pil(__UpperCAmelCase))
a_ = loop_post_process(__UpperCAmelCase)
for iter, transformed_img in enumerate(self._optimize_CLIP(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)):
if show_intermediate:
show_pil(__UpperCAmelCase)
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(__UpperCAmelCase)})
if show_final:
show_pil(__UpperCAmelCase)
if save_final:
transformed_img.save(os.path.join(self.save_path , F'''iter_{iter:03d}_final.png'''))
| 243
| 1
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
_lowercase: Dict = get_tests_dir("fixtures")
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
a = mock.Mock()
a = 500
a = {}
a = HTTPError
a = {}
# Download this model to make sure it's in the cache.
a = WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=lowerCamelCase_ ) as mock_head:
a = WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase_ (self ):
"""simple docstring"""
a = WavaVecaFeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json" )
@is_staging_test
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def UpperCamelCase_ (cls ):
"""simple docstring"""
a = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def UpperCamelCase_ (cls ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-feature-extractor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-feature-extractor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-feature-extractor" )
except HTTPError:
pass
def UpperCamelCase_ (self ):
"""simple docstring"""
a = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_ )
feature_extractor.push_to_hub("test-feature-extractor" , use_auth_token=self._token )
a = WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-feature-extractor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
lowerCamelCase_ , repo_id="test-feature-extractor" , push_to_hub=lowerCamelCase_ , use_auth_token=self._token )
a = WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
def UpperCamelCase_ (self ):
"""simple docstring"""
a = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_ )
feature_extractor.push_to_hub("valid_org/test-feature-extractor" , use_auth_token=self._token )
a = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-feature-extractor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
lowerCamelCase_ , repo_id="valid_org/test-feature-extractor-org" , push_to_hub=lowerCamelCase_ , use_auth_token=self._token )
a = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
def UpperCamelCase_ (self ):
"""simple docstring"""
CustomFeatureExtractor.register_for_auto_class()
a = CustomFeatureExtractor.from_pretrained(lowerCamelCase_ )
feature_extractor.push_to_hub("test-dynamic-feature-extractor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"} , )
a = AutoFeatureExtractor.from_pretrained(
F'''{USER}/test-dynamic-feature-extractor''' , trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , "CustomFeatureExtractor" )
| 352
|
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__(self , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ ):
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , lowerCamelCase_ , )
a = kwargs.pop("feature_extractor" )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(lowerCamelCase_ , lowerCamelCase_ )
def __call__(self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ ):
"""simple docstring"""
if text is None and visual_prompt is None and images is None:
raise ValueError("You have to specify either text, visual prompt or images." )
if text is not None and visual_prompt is not None:
raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt." )
if text is not None:
a = self.tokenizer(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if visual_prompt is not None:
a = self.image_processor(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if images is not None:
a = self.image_processor(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if visual_prompt is not None and images is not None:
a = {
"pixel_values": image_features.pixel_values,
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
a = {
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowerCamelCase_ ) , tensor_type=lowerCamelCase_ )
def UpperCamelCase_ (self , *lowerCamelCase_ , **lowerCamelCase_ ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCamelCase_ , **lowerCamelCase_ )
def UpperCamelCase_ (self , *lowerCamelCase_ , **lowerCamelCase_ ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCamelCase_ , **lowerCamelCase_ )
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , lowerCamelCase_ , )
return self.image_processor_class
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , lowerCamelCase_ , )
return self.image_processor
| 71
| 0
|
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
__UpperCamelCase =FileLock(str(tmpdir / 'foo.lock' ) )
__UpperCamelCase =FileLock(str(tmpdir / 'foo.lock' ) )
__UpperCamelCase =0.01
with locka.acquire():
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
__UpperCamelCase =time.time()
locka.acquire(SCREAMING_SNAKE_CASE__ )
assert time.time() - _start > timeout
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
__UpperCamelCase ='a' * 10_00 + '.lock'
__UpperCamelCase =FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith('.lock' )
assert not locka._lock_file.endswith(SCREAMING_SNAKE_CASE__ )
assert len(os.path.basename(locka._lock_file ) ) <= 2_55
__UpperCamelCase =FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
locka.acquire(0 )
| 62
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : str = logging.get_logger(__name__)
A : int = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class _lowercase ( lowercase__):
"""simple docstring"""
A__ = "mgp-str"
def __init__( self : List[str] , __lowerCamelCase : List[Any]=[32, 128] , __lowerCamelCase : Optional[int]=4 , __lowerCamelCase : str=3 , __lowerCamelCase : Optional[Any]=27 , __lowerCamelCase : List[str]=38 , __lowerCamelCase : Dict=50257 , __lowerCamelCase : List[Any]=30522 , __lowerCamelCase : Optional[Any]=768 , __lowerCamelCase : str=12 , __lowerCamelCase : Optional[int]=12 , __lowerCamelCase : List[str]=4.0 , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Any=False , __lowerCamelCase : Dict=1E-5 , __lowerCamelCase : int=0.0 , __lowerCamelCase : Optional[int]=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : Union[str, Any]=False , __lowerCamelCase : str=0.0_2 , **__lowerCamelCase : Dict , ):
'''simple docstring'''
super().__init__(**__lowerCamelCase )
lowerCamelCase__ : int = image_size
lowerCamelCase__ : Union[str, Any] = patch_size
lowerCamelCase__ : Dict = num_channels
lowerCamelCase__ : Union[str, Any] = max_token_length
lowerCamelCase__ : Optional[int] = num_character_labels
lowerCamelCase__ : Union[str, Any] = num_bpe_labels
lowerCamelCase__ : Optional[int] = num_wordpiece_labels
lowerCamelCase__ : List[str] = hidden_size
lowerCamelCase__ : Dict = num_hidden_layers
lowerCamelCase__ : Any = num_attention_heads
lowerCamelCase__ : Any = mlp_ratio
lowerCamelCase__ : List[Any] = distilled
lowerCamelCase__ : Optional[Any] = layer_norm_eps
lowerCamelCase__ : Union[str, Any] = drop_rate
lowerCamelCase__ : List[Any] = qkv_bias
lowerCamelCase__ : int = attn_drop_rate
lowerCamelCase__ : List[Any] = drop_path_rate
lowerCamelCase__ : List[str] = output_aa_attentions
lowerCamelCase__ : Dict = initializer_range
| 184
| 0
|
"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCamelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = 4_2 # [batch_size x 3]
SCREAMING_SNAKE_CASE = 4_2 # [batch_size x 3]
SCREAMING_SNAKE_CASE = 4_2 # [batch_size x 3]
SCREAMING_SNAKE_CASE = 4_2 # [batch_size x 3]
SCREAMING_SNAKE_CASE = 4_2
SCREAMING_SNAKE_CASE = 4_2
SCREAMING_SNAKE_CASE = 4_2
SCREAMING_SNAKE_CASE = 4_2
SCREAMING_SNAKE_CASE = 4_2
def _a (self ):
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def _a (self ):
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def _a (self ):
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = torch.arange(self.height * self.width )
UpperCAmelCase__ : Union[str, Any] = torch.stack(
[
pixel_indices % self.width,
torch.div(_lowerCamelCase , self.width , rounding_mode="""trunc""" ),
] , axis=1 , )
return coords
@property
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Any = self.shape
UpperCAmelCase__ : Any = int(np.prod(_lowerCamelCase ) )
UpperCAmelCase__ : Union[str, Any] = self.get_image_coords()
UpperCAmelCase__ : Optional[int] = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
UpperCAmelCase__ : int = self.get_camera_rays(_lowerCamelCase )
UpperCAmelCase__ : Any = rays.view(_lowerCamelCase , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def _a (self , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : str = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
UpperCAmelCase__ : str = coords.view(_lowerCamelCase , -1 , 2 )
UpperCAmelCase__ : Optional[Any] = self.resolution()
UpperCAmelCase__ : List[Any] = self.fov()
UpperCAmelCase__ : Union[str, Any] = (flat.float() / (res - 1)) * 2 - 1
UpperCAmelCase__ : List[Any] = fracs * torch.tan(fov / 2 )
UpperCAmelCase__ : Tuple = fracs.view(_lowerCamelCase , -1 , 2 )
UpperCAmelCase__ : List[Any] = (
self.z.view(_lowerCamelCase , 1 , 3 )
+ self.x.view(_lowerCamelCase , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(_lowerCamelCase , 1 , 3 ) * fracs[:, :, 1:]
)
UpperCAmelCase__ : Any = directions / directions.norm(dim=-1 , keepdim=_lowerCamelCase )
UpperCAmelCase__ : List[Any] = torch.stack(
[
torch.broadcast_to(self.origin.view(_lowerCamelCase , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(_lowerCamelCase , *_lowerCamelCase , 2 , 3 )
def _a (self , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=_lowerCamelCase , height=_lowerCamelCase , x_fov=self.x_fov , y_fov=self.y_fov , )
def a__ ( lowerCAmelCase ) -> DifferentiableProjectiveCamera:
UpperCAmelCase__ : List[Any] = []
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : List[str] = []
UpperCAmelCase__ : Any = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
UpperCAmelCase__ : int = np.array([np.sin(lowerCAmelCase ), np.cos(lowerCAmelCase ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
UpperCAmelCase__ : List[Any] = -z * 4
UpperCAmelCase__ : Optional[int] = np.array([np.cos(lowerCAmelCase ), -np.sin(lowerCAmelCase ), 0.0] )
UpperCAmelCase__ : str = np.cross(lowerCAmelCase , lowerCAmelCase )
origins.append(lowerCAmelCase )
xs.append(lowerCAmelCase )
ys.append(lowerCAmelCase )
zs.append(lowerCAmelCase )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowerCAmelCase , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowerCAmelCase , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowerCAmelCase , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowerCAmelCase , axis=0 ) ).float() , width=lowerCAmelCase , height=lowerCAmelCase , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowerCAmelCase )) , )
| 355
|
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase :
'''simple docstring'''
def __init__(self , _lowerCamelCase , _lowerCamelCase=13 , _lowerCamelCase=32 , _lowerCamelCase=3 , _lowerCamelCase=4 , _lowerCamelCase=[10, 20, 30, 40] , _lowerCamelCase=[2, 2, 3, 2] , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=37 , _lowerCamelCase="gelu" , _lowerCamelCase=10 , _lowerCamelCase=0.02 , _lowerCamelCase=["stage2", "stage3", "stage4"] , _lowerCamelCase=3 , _lowerCamelCase=None , ):
"""simple docstring"""
UpperCAmelCase__ : int = parent
UpperCAmelCase__ : str = batch_size
UpperCAmelCase__ : Union[str, Any] = image_size
UpperCAmelCase__ : Optional[Any] = num_channels
UpperCAmelCase__ : Optional[int] = num_stages
UpperCAmelCase__ : Optional[Any] = hidden_sizes
UpperCAmelCase__ : Any = depths
UpperCAmelCase__ : str = is_training
UpperCAmelCase__ : Tuple = use_labels
UpperCAmelCase__ : Optional[Any] = intermediate_size
UpperCAmelCase__ : Optional[Any] = hidden_act
UpperCAmelCase__ : Tuple = type_sequence_label_size
UpperCAmelCase__ : Dict = initializer_range
UpperCAmelCase__ : Tuple = out_features
UpperCAmelCase__ : Dict = num_labels
UpperCAmelCase__ : Tuple = scope
UpperCAmelCase__ : Optional[int] = num_stages
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : List[Any] = None
if self.use_labels:
UpperCAmelCase__ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Dict = self.get_config()
return config, pixel_values, labels
def _a (self ):
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def _a (self ):
"""simple docstring"""
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=512 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=_lowerCamelCase , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=256 , auxiliary_num_convs=1 , auxiliary_concat_input=_lowerCamelCase , loss_ignore_index=255 , num_labels=self.num_labels , )
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Any = UperNetForSemanticSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase__ : Tuple = model(_lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Any = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) : Union[str, Any] = config_and_inputs
UpperCAmelCase__ : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = {'image-segmentation': UperNetForSemanticSegmentation} 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
SCREAMING_SNAKE_CASE = False
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = UperNetModelTester(self )
UpperCAmelCase__ : Dict = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase , hidden_size=37 )
def _a (self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a (self ):
"""simple docstring"""
return
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(_lowerCamelCase )
UpperCAmelCase__ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : List[Any] = [*signature.parameters.keys()]
UpperCAmelCase__ : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase )
@unittest.skip(reason="""UperNet does not use inputs_embeds""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip(reason="""UperNet does not support input and output embeddings""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip(reason="""UperNet does not have a base model""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip(reason="""UperNet does not have a base model""" )
def _a (self ):
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a (self ):
"""simple docstring"""
pass
def _a (self ):
"""simple docstring"""
def check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCAmelCase__ : List[Any] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
UpperCAmelCase__ : int = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
UpperCAmelCase__ : Union[str, Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase__ : Tuple = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[Any] = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : Any = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : List[Any] = _config_zero_init(_lowerCamelCase )
UpperCAmelCase__ : List[str] = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(config=_lowerCamelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip(reason="""UperNet does not have tied weights""" )
def _a (self ):
"""simple docstring"""
pass
@slow
def _a (self ):
"""simple docstring"""
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : str = UperNetForSemanticSegmentation.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def a__ ( ) -> List[Any]:
UpperCAmelCase__ : List[str] = hf_hub_download(
repo_id="""hf-internal-testing/fixtures_ade20k""" , repo_type="""dataset""" , filename="""ADE_val_00000001.jpg""" )
UpperCAmelCase__ : str = Image.open(lowerCAmelCase ).convert("""RGB""" )
return image
@require_torch
@require_vision
@slow
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny""" )
UpperCAmelCase__ : Any = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny""" ).to(_lowerCamelCase )
UpperCAmelCase__ : Optional[Any] = prepare_img()
UpperCAmelCase__ : int = processor(images=_lowerCamelCase , return_tensors="""pt""" ).to(_lowerCamelCase )
with torch.no_grad():
UpperCAmelCase__ : Any = model(**_lowerCamelCase )
UpperCAmelCase__ : List[Any] = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
UpperCAmelCase__ : Optional[Any] = torch.tensor(
[[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , _lowerCamelCase , atol=1e-4 ) )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny""" )
UpperCAmelCase__ : List[str] = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny""" ).to(_lowerCamelCase )
UpperCAmelCase__ : Tuple = prepare_img()
UpperCAmelCase__ : int = processor(images=_lowerCamelCase , return_tensors="""pt""" ).to(_lowerCamelCase )
with torch.no_grad():
UpperCAmelCase__ : int = model(**_lowerCamelCase )
UpperCAmelCase__ : str = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
UpperCAmelCase__ : int = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , _lowerCamelCase , atol=1e-4 ) )
| 166
| 0
|
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_UpperCamelCase : Any = re.compile(r"\b(a|an|the)\b", re.UNICODE)
_UpperCamelCase : Union[str, Any] = None
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.' )
parser.add_argument('data_file' , metavar='data.json' , help='Input data JSON file.' )
parser.add_argument('pred_file' , metavar='pred.json' , help='Model predictions.' )
parser.add_argument(
'--out-file' , '-o' , metavar='eval.json' , help='Write accuracy metrics to file (default is stdout).' )
parser.add_argument(
'--na-prob-file' , '-n' , metavar='na_prob.json' , help='Model estimates of probability of no answer.' )
parser.add_argument(
'--na-prob-thresh' , '-t' , type=_lowerCAmelCase , default=1.0 , help='Predict "" if no-answer probability exceeds this (default = 1.0).' , )
parser.add_argument(
'--out-image-dir' , '-p' , metavar='out_images' , default=_lowerCAmelCase , help='Save precision-recall curves to directory.' )
parser.add_argument('--verbose' , '-v' , action='store_true' )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Tuple = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowercase__ : Optional[int] = bool(qa['answers']['text'] )
return qid_to_has_ans
def a_ ( _lowerCAmelCase : Any ):
'''simple docstring'''
def remove_articles(_lowerCAmelCase : int ):
return ARTICLES_REGEX.sub(' ' , _lowerCAmelCase )
def white_space_fix(_lowerCAmelCase : str ):
return " ".join(text.split() )
def remove_punc(_lowerCAmelCase : List[Any] ):
lowercase__ : int = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_lowerCAmelCase : List[str] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase ) ) ) )
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
if not s:
return []
return normalize_answer(_lowerCAmelCase ).split()
def a_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ):
'''simple docstring'''
return int(normalize_answer(_lowerCAmelCase ) == normalize_answer(_lowerCAmelCase ) )
def a_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : Dict = get_tokens(_lowerCAmelCase )
lowercase__ : List[str] = get_tokens(_lowerCAmelCase )
lowercase__ : List[Any] = collections.Counter(_lowerCAmelCase ) & collections.Counter(_lowerCAmelCase )
lowercase__ : int = sum(common.values() )
if len(_lowerCAmelCase ) == 0 or len(_lowerCAmelCase ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
lowercase__ : Any = 1.0 * num_same / len(_lowerCAmelCase )
lowercase__ : Dict = 1.0 * num_same / len(_lowerCAmelCase )
lowercase__ : Any = (2 * precision * recall) / (precision + recall)
return fa
def a_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Optional[int] = {}
lowercase__ : Union[str, Any] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowercase__ : Any = qa['id']
lowercase__ : Union[str, Any] = [t for t in qa['answers']['text'] if normalize_answer(_lowerCAmelCase )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
lowercase__ : Dict = ['']
if qid not in preds:
print(f"""Missing prediction for {qid}""" )
continue
lowercase__ : Optional[int] = preds[qid]
# Take max over all gold answers
lowercase__ : int = max(compute_exact(_lowerCAmelCase , _lowerCAmelCase ) for a in gold_answers )
lowercase__ : Optional[Any] = max(compute_fa(_lowerCAmelCase , _lowerCAmelCase ) for a in gold_answers )
return exact_scores, fa_scores
def a_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : str = {}
for qid, s in scores.items():
lowercase__ : int = na_probs[qid] > na_prob_thresh
if pred_na:
lowercase__ : Optional[Any] = float(not qid_to_has_ans[qid] )
else:
lowercase__ : Optional[Any] = s
return new_scores
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : str=None ):
'''simple docstring'''
if not qid_list:
lowercase__ : Optional[Any] = len(_lowerCAmelCase )
return collections.OrderedDict(
[
('exact', 1_0_0.0 * sum(exact_scores.values() ) / total),
('f1', 1_0_0.0 * sum(fa_scores.values() ) / total),
('total', total),
] )
else:
lowercase__ : Optional[Any] = len(_lowerCAmelCase )
return collections.OrderedDict(
[
('exact', 1_0_0.0 * sum(exact_scores[k] for k in qid_list ) / total),
('f1', 1_0_0.0 * sum(fa_scores[k] for k in qid_list ) / total),
('total', total),
] )
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
for k in new_eval:
lowercase__ : int = new_eval[k]
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Tuple ):
'''simple docstring'''
plt.step(_lowerCAmelCase , _lowerCAmelCase , color='b' , alpha=0.2 , where='post' )
plt.fill_between(_lowerCAmelCase , _lowerCAmelCase , step='post' , alpha=0.2 , color='b' )
plt.xlabel('Recall' )
plt.ylabel('Precision' )
plt.xlim([0.0, 1.0_5] )
plt.ylim([0.0, 1.0_5] )
plt.title(_lowerCAmelCase )
plt.savefig(_lowerCAmelCase )
plt.clf()
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : Any=None , _lowerCAmelCase : List[str]=None ):
'''simple docstring'''
lowercase__ : Optional[int] = sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : na_probs[k] )
lowercase__ : Tuple = 0.0
lowercase__ : List[str] = 1.0
lowercase__ : List[str] = 0.0
lowercase__ : Union[str, Any] = [1.0]
lowercase__ : List[Any] = [0.0]
lowercase__ : Optional[int] = 0.0
for i, qid in enumerate(_lowerCAmelCase ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
lowercase__ : Tuple = true_pos / float(i + 1 )
lowercase__ : Union[str, Any] = true_pos / float(_lowerCAmelCase )
if i == len(_lowerCAmelCase ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(_lowerCAmelCase )
recalls.append(_lowerCAmelCase )
if out_image:
plot_pr_curve(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return {"ap": 1_0_0.0 * avg_prec}
def a_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
if out_image_dir and not os.path.exists(_lowerCAmelCase ):
os.makedirs(_lowerCAmelCase )
lowercase__ : List[str] = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
lowercase__ : Dict = make_precision_recall_eval(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , out_image=os.path.join(_lowerCAmelCase , 'pr_exact.png' ) , title='Precision-Recall curve for Exact Match score' , )
lowercase__ : Tuple = make_precision_recall_eval(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , out_image=os.path.join(_lowerCAmelCase , 'pr_f1.png' ) , title='Precision-Recall curve for F1 score' , )
lowercase__ : List[Any] = {k: float(_lowerCAmelCase ) for k, v in qid_to_has_ans.items()}
lowercase__ : Any = make_precision_recall_eval(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , out_image=os.path.join(_lowerCAmelCase , 'pr_oracle.png' ) , title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)' , )
merge_eval(_lowerCAmelCase , _lowerCAmelCase , 'pr_exact' )
merge_eval(_lowerCAmelCase , _lowerCAmelCase , 'pr_f1' )
merge_eval(_lowerCAmelCase , _lowerCAmelCase , 'pr_oracle' )
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if not qid_list:
return
lowercase__ : List[str] = [na_probs[k] for k in qid_list]
lowercase__ : Tuple = np.ones_like(_lowerCAmelCase ) / float(len(_lowerCAmelCase ) )
plt.hist(_lowerCAmelCase , weights=_lowerCAmelCase , bins=20 , range=(0.0, 1.0) )
plt.xlabel('Model probability of no-answer' )
plt.ylabel('Proportion of dataset' )
plt.title(f"""Histogram of no-answer probability: {name}""" )
plt.savefig(os.path.join(_lowerCAmelCase , f"""na_prob_hist_{name}.png""" ) )
plt.clf()
def a_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
lowercase__ : Tuple = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
lowercase__ : int = num_no_ans
lowercase__ : Optional[int] = cur_score
lowercase__ : Tuple = 0.0
lowercase__ : Dict = sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : na_probs[k] )
for i, qid in enumerate(_lowerCAmelCase ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
lowercase__ : Optional[int] = scores[qid]
else:
if preds[qid]:
lowercase__ : List[Any] = -1
else:
lowercase__ : Optional[int] = 0
cur_score += diff
if cur_score > best_score:
lowercase__ : Dict = cur_score
lowercase__ : Optional[int] = na_probs[qid]
return 1_0_0.0 * best_score / len(_lowerCAmelCase ), best_thresh
def a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ , lowercase__ : List[Any] = find_best_thresh(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase__ , lowercase__ : Dict = find_best_thresh(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowercase__ : Any = best_exact
lowercase__ : Tuple = exact_thresh
lowercase__ : Optional[Any] = best_fa
lowercase__ : Any = fa_thresh
def a_ ( ):
'''simple docstring'''
with open(OPTS.data_file ) as f:
lowercase__ : List[Any] = json.load(_lowerCAmelCase )
lowercase__ : Union[str, Any] = dataset_json['data']
with open(OPTS.pred_file ) as f:
lowercase__ : str = json.load(_lowerCAmelCase )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
lowercase__ : Union[str, Any] = json.load(_lowerCAmelCase )
else:
lowercase__ : str = {k: 0.0 for k in preds}
lowercase__ : int = make_qid_to_has_ans(_lowerCAmelCase ) # maps qid to True/False
lowercase__ : List[str] = [k for k, v in qid_to_has_ans.items() if v]
lowercase__ : Any = [k for k, v in qid_to_has_ans.items() if not v]
lowercase__ , lowercase__ : Any = get_raw_scores(_lowerCAmelCase , _lowerCAmelCase )
lowercase__ : Optional[Any] = apply_no_ans_threshold(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , OPTS.na_prob_thresh )
lowercase__ : Union[str, Any] = apply_no_ans_threshold(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , OPTS.na_prob_thresh )
lowercase__ : Tuple = make_eval_dict(_lowerCAmelCase , _lowerCAmelCase )
if has_ans_qids:
lowercase__ : int = make_eval_dict(_lowerCAmelCase , _lowerCAmelCase , qid_list=_lowerCAmelCase )
merge_eval(_lowerCAmelCase , _lowerCAmelCase , 'HasAns' )
if no_ans_qids:
lowercase__ : Optional[Any] = make_eval_dict(_lowerCAmelCase , _lowerCAmelCase , qid_list=_lowerCAmelCase )
merge_eval(_lowerCAmelCase , _lowerCAmelCase , 'NoAns' )
if OPTS.na_prob_file:
find_all_best_thresh(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , OPTS.out_image_dir )
histogram_na_prob(_lowerCAmelCase , _lowerCAmelCase , OPTS.out_image_dir , 'hasAns' )
histogram_na_prob(_lowerCAmelCase , _lowerCAmelCase , OPTS.out_image_dir , 'noAns' )
if OPTS.out_file:
with open(OPTS.out_file , 'w' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
else:
print(json.dumps(_lowerCAmelCase , indent=2 ) )
if __name__ == "__main__":
_UpperCamelCase : Optional[int] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 77
|
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def lowerCAmelCase_ ( __A, __A=False ) -> Any:
'''simple docstring'''
try:
UpperCAmelCase__ = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
UpperCAmelCase__ = default
else:
# KEY is set, convert it to True or False.
try:
UpperCAmelCase__ = strtobool(__A )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"""If set, {key} must be yes or no.""" )
return _value
UpperCamelCase__ = parse_flag_from_env('RUN_SLOW', default=False)
UpperCamelCase__ = parse_flag_from_env('RUN_REMOTE', default=False)
UpperCamelCase__ = parse_flag_from_env('RUN_LOCAL', default=True)
UpperCamelCase__ = parse_flag_from_env('RUN_PACKAGED', default=True)
# Compression
UpperCamelCase__ = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4')
UpperCamelCase__ = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr')
UpperCamelCase__ = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard')
# Audio
UpperCamelCase__ = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'),
reason='test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ',
)
# Beam
UpperCamelCase__ = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'),
reason='test requires apache-beam and a compatible dill version',
)
# Dill-cloudpickle compatibility
UpperCamelCase__ = pytest.mark.skipif(
config.DILL_VERSION <= version.parse('0.3.2'),
reason='test requires dill>0.3.2 for cloudpickle compatibility',
)
# Windows
UpperCamelCase__ = pytest.mark.skipif(
sys.platform == 'win32',
reason='test should not be run on Windows',
)
def lowerCAmelCase_ ( __A ) -> Any:
'''simple docstring'''
try:
import faiss # noqa
except ImportError:
UpperCAmelCase__ = unittest.skip("test requires faiss" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> Optional[Any]:
'''simple docstring'''
try:
import regex # noqa
except ImportError:
UpperCAmelCase__ = unittest.skip("test requires regex" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> List[str]:
'''simple docstring'''
try:
import elasticsearch # noqa
except ImportError:
UpperCAmelCase__ = unittest.skip("test requires elasticsearch" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> List[Any]:
'''simple docstring'''
try:
import sqlalchemy # noqa
except ImportError:
UpperCAmelCase__ = unittest.skip("test requires sqlalchemy" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> List[str]:
'''simple docstring'''
if not config.TORCH_AVAILABLE:
UpperCAmelCase__ = unittest.skip("test requires PyTorch" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> Union[str, Any]:
'''simple docstring'''
if not config.TF_AVAILABLE:
UpperCAmelCase__ = unittest.skip("test requires TensorFlow" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> Any:
'''simple docstring'''
if not config.JAX_AVAILABLE:
UpperCAmelCase__ = unittest.skip("test requires JAX" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> int:
'''simple docstring'''
if not config.PIL_AVAILABLE:
UpperCAmelCase__ = unittest.skip("test requires Pillow" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> Tuple:
'''simple docstring'''
try:
import transformers # noqa F401
except ImportError:
return unittest.skip("test requires transformers" )(__A )
else:
return test_case
def lowerCAmelCase_ ( __A ) -> Dict:
'''simple docstring'''
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip("test requires tiktoken" )(__A )
else:
return test_case
def lowerCAmelCase_ ( __A ) -> Optional[Any]:
'''simple docstring'''
try:
import spacy # noqa F401
except ImportError:
return unittest.skip("test requires spacy" )(__A )
else:
return test_case
def lowerCAmelCase_ ( __A ) -> Optional[int]:
'''simple docstring'''
def _require_spacy_model(__A ):
try:
import spacy # noqa F401
spacy.load(__A )
except ImportError:
return unittest.skip("test requires spacy" )(__A )
except OSError:
return unittest.skip("test requires spacy model '{}'".format(__A ) )(__A )
else:
return test_case
return _require_spacy_model
def lowerCAmelCase_ ( __A ) -> Optional[Any]:
'''simple docstring'''
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip("test requires pyspark" )(__A )
else:
return test_case
def lowerCAmelCase_ ( __A ) -> Tuple:
'''simple docstring'''
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip("test requires joblibspark" )(__A )
else:
return test_case
def lowerCAmelCase_ ( __A ) -> Optional[int]:
'''simple docstring'''
if not _run_slow_tests or _run_slow_tests == 0:
UpperCAmelCase__ = unittest.skip("test is slow" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> List[Any]:
'''simple docstring'''
if not _run_local_tests or _run_local_tests == 0:
UpperCAmelCase__ = unittest.skip("test is local" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> Optional[Any]:
'''simple docstring'''
if not _run_packaged_tests or _run_packaged_tests == 0:
UpperCAmelCase__ = unittest.skip("test is packaged" )(__A )
return test_case
def lowerCAmelCase_ ( __A ) -> Any:
'''simple docstring'''
if not _run_remote_tests or _run_remote_tests == 0:
UpperCAmelCase__ = unittest.skip("test requires remote" )(__A )
return test_case
def lowerCAmelCase_ ( *__A ) -> Optional[int]:
'''simple docstring'''
def decorate(cls ):
for name, fn in cls.__dict__.items():
if callable(__A ) and name.startswith("test" ):
for decorator in decorators:
UpperCAmelCase__ = decorator(__A )
setattr(cls, __A, __A )
return cls
return decorate
class A ( UpperCAmelCase_ ):
pass
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : Union[str, Any] = 0
__UpperCAmelCase : str = 1
__UpperCAmelCase : int = 2
@contextmanager
def lowerCAmelCase_ ( __A=OfflineSimulationMode.CONNECTION_FAILS, __A=1e-16 ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ = requests.Session().request
def timeout_request(__A, __A, __A, **__A ):
# Change the url to an invalid url so that the connection hangs
UpperCAmelCase__ = "https://10.255.255.1"
if kwargs.get("timeout" ) is None:
raise RequestWouldHangIndefinitelyError(
f"""Tried a call to {url} in offline mode with no timeout set. Please set a timeout.""" )
UpperCAmelCase__ = timeout
try:
return online_request(__A, __A, **__A )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
UpperCAmelCase__ = url
UpperCAmelCase__ = e.args[0]
UpperCAmelCase__ = (max_retry_error.args[0].replace("10.255.255.1", f"""OfflineMock[{url}]""" ),)
UpperCAmelCase__ = (max_retry_error,)
raise
def raise_connection_error(__A, __A, **__A ):
raise requests.ConnectionError("Offline mode is enabled.", request=__A )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch("requests.Session.send", __A ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch("requests.Session.request", __A ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch("datasets.config.HF_DATASETS_OFFLINE", __A ):
yield
else:
raise ValueError("Please use a value from the OfflineSimulationMode enum." )
@contextmanager
def lowerCAmelCase_ ( *__A, **__A ) -> str:
'''simple docstring'''
UpperCAmelCase__ = str(Path().resolve() )
with tempfile.TemporaryDirectory(*__A, **__A ) as tmp_dir:
try:
os.chdir(__A )
yield
finally:
os.chdir(__A )
@contextmanager
def lowerCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
import gc
gc.collect()
UpperCAmelCase__ = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def lowerCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
import gc
gc.collect()
UpperCAmelCase__ = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def lowerCAmelCase_ ( __A, __A ) -> List[str]:
'''simple docstring'''
return deepcopy(__A ).integers(0, 100, 10 ).tolist() == deepcopy(__A ).integers(0, 100, 10 ).tolist()
def lowerCAmelCase_ ( __A ) -> Optional[int]:
'''simple docstring'''
import decorator
from requests.exceptions import HTTPError
def _wrapper(__A, *__A, **__A ):
try:
return func(*__A, **__A )
except HTTPError as err:
if str(__A ).startswith("500" ) or str(__A ).startswith("502" ):
pytest.xfail(str(__A ) )
raise err
return decorator.decorator(_wrapper, __A )
class A :
def __init__(self : Optional[Any] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : List[str] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = returncode
UpperCAmelCase__ = stdout
UpperCAmelCase__ = stderr
async def lowerCAmelCase_ ( __A, __A ) -> Optional[int]:
'''simple docstring'''
while True:
UpperCAmelCase__ = await stream.readline()
if line:
callback(__A )
else:
break
async def lowerCAmelCase_ ( __A, __A=None, __A=None, __A=None, __A=False, __A=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print("\nRunning: ", " ".join(__A ) )
UpperCAmelCase__ = await asyncio.create_subprocess_exec(
cmd[0], *cmd[1:], stdin=__A, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, env=__A, )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
UpperCAmelCase__ = []
UpperCAmelCase__ = []
def tee(__A, __A, __A, __A="" ):
UpperCAmelCase__ = line.decode("utf-8" ).rstrip()
sink.append(__A )
if not quiet:
print(__A, __A, file=__A )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout, lambda __A : tee(__A, __A, sys.stdout, label="stdout:" ) ),
_read_stream(p.stderr, lambda __A : tee(__A, __A, sys.stderr, label="stderr:" ) ),
], timeout=__A, )
return _RunOutput(await p.wait(), __A, __A )
def lowerCAmelCase_ ( __A, __A=None, __A=None, __A=180, __A=False, __A=True ) -> _RunOutput:
'''simple docstring'''
UpperCAmelCase__ = asyncio.get_event_loop()
UpperCAmelCase__ = loop.run_until_complete(
_stream_subprocess(__A, env=__A, stdin=__A, timeout=__A, quiet=__A, echo=__A ) )
UpperCAmelCase__ = " ".join(__A )
if result.returncode > 0:
UpperCAmelCase__ = "\n".join(result.stderr )
raise RuntimeError(
f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
f"""The combined stderr from workers follows:\n{stderr}""" )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(f"""'{cmd_str}' produced no output.""" )
return result
def lowerCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ = os.environ.get("PYTEST_XDIST_WORKER", "gw0" )
UpperCAmelCase__ = re.sub(r"^gw", "", __A, 0, re.M )
return int(__A )
def lowerCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ = 29_500
UpperCAmelCase__ = pytest_xdist_worker_id()
return port + uniq_delta
| 65
| 0
|
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCAmelCase__ : Union[str, Any] =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=5_12,
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 _lowercase ( _UpperCAmelCase ) -> List[str]:
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__ : Any =parser.parse_args()
UpperCAmelCase__ : Optional[Any] =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)
| 368
|
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
UpperCAmelCase__ : Dict =logging.get_logger(__name__) # pylint: disable=invalid-name
class __A ( a ):
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_ ):
super().__init__()
self.register_modules(unet=UpperCAmelCase_ , scheduler=UpperCAmelCase_ )
@torch.no_grad()
def __call__( self , UpperCAmelCase_ = 1 , UpperCAmelCase_ = 100 , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = True , ):
if audio_length_in_s is None:
lowerCamelCase =self.unet.config.sample_size / self.unet.config.sample_rate
lowerCamelCase =audio_length_in_s * self.unet.config.sample_rate
lowerCamelCase =2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )
lowerCamelCase =int(UpperCAmelCase_ )
if sample_size % down_scale_factor != 0:
lowerCamelCase =(
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
""" process.""" )
lowerCamelCase =int(UpperCAmelCase_ )
lowerCamelCase =next(iter(self.unet.parameters() ) ).dtype
lowerCamelCase =(batch_size, self.unet.config.in_channels, sample_size)
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and len(UpperCAmelCase_ ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(UpperCAmelCase_ )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
lowerCamelCase =randn_tensor(UpperCAmelCase_ , generator=UpperCAmelCase_ , device=self.device , dtype=UpperCAmelCase_ )
# set step values
self.scheduler.set_timesteps(UpperCAmelCase_ , device=audio.device )
lowerCamelCase =self.scheduler.timesteps.to(UpperCAmelCase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowerCamelCase =self.unet(UpperCAmelCase_ , UpperCAmelCase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowerCamelCase =self.scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ).prev_sample
lowerCamelCase =audio.clamp(-1 , 1 ).float().cpu().numpy()
lowerCamelCase =audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=UpperCAmelCase_ )
| 262
| 0
|
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False ) -> list[float]:
if radian_mode:
return [magnitude * cos(lowerCamelCase__ ), magnitude * sin(lowerCamelCase__ )]
return [magnitude * cos(radians(lowerCamelCase__ ) ), magnitude * sin(radians(lowerCamelCase__ ) )]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = 1_0**-1 ) -> bool:
__lowerCamelCase : NDArray[floataa] = cross(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : float = sum(lowerCamelCase__ )
return abs(lowerCamelCase__ ) < eps
if __name__ == "__main__":
# Test to check if it works
a =array(
[
polar_force(7_18.4, 180 - 30),
polar_force(8_79.54, 45),
polar_force(100, -90),
]
)
a =array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
a =array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
a =array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
a =array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])
a =array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 73
|
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class __magic_name__ :
"""simple docstring"""
def __init__( self :Tuple , snake_case :Optional[Any] , snake_case :Tuple=13 , snake_case :Dict=7 , snake_case :List[Any]=True , snake_case :List[Any]=True , snake_case :Dict=True , snake_case :Any=True , snake_case :Optional[int]=99 , snake_case :Any=32 , snake_case :Dict=2 , snake_case :int=4 , snake_case :Optional[int]=37 , snake_case :List[str]="gelu" , snake_case :List[Any]=0.1 , snake_case :Optional[Any]=0.1 , snake_case :Tuple=512 , snake_case :Tuple=16 , snake_case :Tuple=2 , snake_case :Optional[int]=0.02 , snake_case :str=3 , snake_case :Optional[int]=4 , snake_case :List[str]=None , snake_case :Tuple=1_000 , ):
'''simple docstring'''
A_ : str = parent
A_ : str = batch_size
A_ : str = seq_length
A_ : Any = is_training
A_ : Any = use_input_mask
A_ : str = use_token_type_ids
A_ : Tuple = use_labels
A_ : Optional[Any] = vocab_size
A_ : Dict = hidden_size
A_ : str = num_hidden_layers
A_ : Dict = num_attention_heads
A_ : str = intermediate_size
A_ : int = hidden_act
A_ : List[Any] = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : Optional[Any] = max_position_embeddings
A_ : List[Any] = type_vocab_size
A_ : Any = type_sequence_label_size
A_ : Dict = initializer_range
A_ : Any = num_labels
A_ : Optional[int] = num_choices
A_ : Optional[Any] = scope
A_ : Any = range_bbox
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
A_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
A_ : Tuple = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
A_ : str = bbox[i, j, 3]
A_ : Union[str, Any] = bbox[i, j, 1]
A_ : List[Any] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
A_ : Any = bbox[i, j, 2]
A_ : Tuple = bbox[i, j, 0]
A_ : int = t
A_ : int = tf.convert_to_tensor(snake_case )
A_ : Any = None
if self.use_input_mask:
A_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
A_ : str = None
if self.use_token_type_ids:
A_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ : Dict = None
A_ : List[Any] = None
A_ : List[str] = None
if self.use_labels:
A_ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ : str = ids_tensor([self.batch_size] , self.num_choices )
A_ : int = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE ( self :str , snake_case :Dict , snake_case :Union[str, Any] , snake_case :int , snake_case :int , snake_case :Union[str, Any] , snake_case :Tuple , snake_case :Optional[int] , snake_case :List[Any] ):
'''simple docstring'''
A_ : Any = TFLayoutLMModel(config=snake_case )
A_ : Tuple = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case )
A_ : str = model(snake_case , snake_case , token_type_ids=snake_case )
A_ : List[Any] = model(snake_case , snake_case )
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 SCREAMING_SNAKE_CASE ( self :Optional[int] , snake_case :Any , snake_case :List[Any] , snake_case :List[str] , snake_case :Optional[Any] , snake_case :Dict , snake_case :Any , snake_case :Union[str, Any] , snake_case :List[Any] ):
'''simple docstring'''
A_ : Optional[int] = TFLayoutLMForMaskedLM(config=snake_case )
A_ : Tuple = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self :List[str] , snake_case :Dict , snake_case :Tuple , snake_case :Tuple , snake_case :List[str] , snake_case :Tuple , snake_case :str , snake_case :Optional[int] , snake_case :Any ):
'''simple docstring'''
A_ : Union[str, Any] = self.num_labels
A_ : int = TFLayoutLMForSequenceClassification(config=snake_case )
A_ : Optional[int] = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self :Optional[Any] , snake_case :Dict , snake_case :str , snake_case :Optional[Any] , snake_case :int , snake_case :Any , snake_case :Tuple , snake_case :List[str] , snake_case :Union[str, Any] ):
'''simple docstring'''
A_ : List[Any] = self.num_labels
A_ : str = TFLayoutLMForTokenClassification(config=snake_case )
A_ : Union[str, Any] = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self :int , snake_case :List[str] , snake_case :Optional[int] , snake_case :Union[str, Any] , snake_case :List[Any] , snake_case :int , snake_case :Any , snake_case :Union[str, Any] , snake_case :Any ):
'''simple docstring'''
A_ : Optional[Any] = TFLayoutLMForQuestionAnswering(config=snake_case )
A_ : List[Any] = model(snake_case , snake_case , attention_mask=snake_case , token_type_ids=snake_case )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE ( self :Dict ):
'''simple docstring'''
A_ : int = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : Union[str, Any] = config_and_inputs
A_ : Optional[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_tf
class __magic_name__ ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
__UpperCamelCase = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = 10
def SCREAMING_SNAKE_CASE ( self :Dict ):
'''simple docstring'''
A_ : Tuple = TFLayoutLMModelTester(self )
A_ : List[Any] = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE ( self :Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE ( self :Optional[int] ):
'''simple docstring'''
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case )
def SCREAMING_SNAKE_CASE ( self :Tuple ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case )
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case )
@slow
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : List[str] = TFLayoutLMModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@unittest.skip("Onnx compliancy broke with TF 2.10" )
def SCREAMING_SNAKE_CASE ( self :Dict ):
'''simple docstring'''
pass
def __snake_case ( ) -> Optional[Any]:
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
A_ : int = tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]] ) # noqa: E231
A_ : int = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 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: E231
A_ : Union[str, Any] = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
A_ : List[Any] = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
A_ : Tuple = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE ( self :Tuple ):
'''simple docstring'''
A_ : str = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased" )
A_ , A_ , A_ , A_ , A_ : Tuple = prepare_layoutlm_batch_inputs()
# forward pass
A_ : Tuple = model(input_ids=snake_case , bbox=snake_case , attention_mask=snake_case , token_type_ids=snake_case )
# test the sequence output on [0, :3, :3]
A_ : List[Any] = tf.convert_to_tensor(
[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , snake_case , atol=1e-3 ) )
# test the pooled output on [1, :3]
A_ : Optional[Any] = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , snake_case , atol=1e-3 ) )
@slow
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
A_ : Union[str, Any] = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased" , num_labels=2 )
A_ , A_ , A_ , A_ , A_ : Any = prepare_layoutlm_batch_inputs()
# forward pass
A_ : Dict = model(
input_ids=snake_case , bbox=snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
A_ : List[str] = outputs.loss
A_ : Union[str, Any] = (2,)
self.assertEqual(loss.shape , snake_case )
# test the shape of the logits
A_ : Tuple = outputs.logits
A_ : Tuple = (2, 2)
self.assertEqual(logits.shape , snake_case )
@slow
def SCREAMING_SNAKE_CASE ( self :Optional[int] ):
'''simple docstring'''
A_ : int = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased" , num_labels=13 )
A_ , A_ , A_ , A_ , A_ : Optional[int] = prepare_layoutlm_batch_inputs()
# forward pass
A_ : Union[str, Any] = model(
input_ids=snake_case , bbox=snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case )
# test the shape of the logits
A_ : Dict = outputs.logits
A_ : List[Any] = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , snake_case )
@slow
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
A_ : Optional[Any] = TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased" )
A_ , A_ , A_ , A_ , A_ : str = prepare_layoutlm_batch_inputs()
# forward pass
A_ : Union[str, Any] = model(input_ids=snake_case , bbox=snake_case , attention_mask=snake_case , token_type_ids=snake_case )
# test the shape of the logits
A_ : Union[str, Any] = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , snake_case )
self.assertEqual(outputs.end_logits.shape , snake_case )
| 300
| 0
|
'''simple docstring'''
a__ : Optional[int] = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
a__ : List[str] = [{'type': 'code', 'content': INSTALL_CONTENT}]
a__ : str = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 243
|
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
a__ : Tuple = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 243
| 1
|
'''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,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict =StableDiffusionInstructPixaPixPipeline
SCREAMING_SNAKE_CASE_ : Tuple =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
SCREAMING_SNAKE_CASE_ : List[Any] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
SCREAMING_SNAKE_CASE_ : Tuple =IMAGE_TO_IMAGE_IMAGE_PARAMS
SCREAMING_SNAKE_CASE_ : List[str] =IMAGE_TO_IMAGE_IMAGE_PARAMS
def _lowerCamelCase ( self : List[Any] ):
torch.manual_seed(0 )
__UpperCamelCase = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=8 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=3_2 , )
__UpperCamelCase = PNDMScheduler(skip_prk_steps=__A )
torch.manual_seed(0 )
__UpperCamelCase = 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 , )
torch.manual_seed(0 )
__UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
__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 _lowerCamelCase ( self : str , __A : Dict , __A : Any=0 ):
__UpperCamelCase = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(__A ) ).to(__A )
__UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__UpperCamelCase = Image.fromarray(np.uinta(__A ) ).convert('RGB' )
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': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'image_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def _lowerCamelCase ( self : Dict ):
__UpperCamelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase = self.get_dummy_components()
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline(**__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, 3_2, 3_2, 3)
__UpperCamelCase = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self : Optional[int] ):
__UpperCamelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase = self.get_dummy_components()
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline(**__A )
__UpperCamelCase = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
__UpperCamelCase = self.get_dummy_inputs(__A )
__UpperCamelCase = 'french fries'
__UpperCamelCase = sd_pipe(**__A , negative_prompt=__A )
__UpperCamelCase = output.images
__UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__UpperCamelCase = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self : List[str] ):
__UpperCamelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase = self.get_dummy_components()
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline(**__A )
__UpperCamelCase = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
__UpperCamelCase = self.get_dummy_inputs(__A )
__UpperCamelCase = [inputs['prompt']] * 2
__UpperCamelCase = np.array(inputs['image'] ).astype(np.floataa ) / 255.0
__UpperCamelCase = torch.from_numpy(__A ).unsqueeze(0 ).to(__A )
__UpperCamelCase = image / 2 + 0.5
__UpperCamelCase = image.permute(0 , 3 , 1 , 2 )
__UpperCamelCase = image.repeat(2 , 1 , 1 , 1 )
__UpperCamelCase = sd_pipe(**__A ).images
__UpperCamelCase = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
__UpperCamelCase = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self : List[Any] ):
__UpperCamelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase = self.get_dummy_components()
__UpperCamelCase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' )
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline(**__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]
__UpperCamelCase = [round(__A , 4 ) for x in image_slice.flatten().tolist()]
print(','.join([str(__A ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
__UpperCamelCase = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self : Any ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def _lowerCamelCase ( self : str ):
__UpperCamelCase = self.get_dummy_components()
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline(**__A )
__UpperCamelCase = VaeImageProcessor(do_resize=__A , do_normalize=__A )
__UpperCamelCase = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
__UpperCamelCase = pipe(**self.get_dummy_inputs_by_type(__A , input_image_type='pt' ) )[0]
__UpperCamelCase = components['vae']
__UpperCamelCase = self.get_dummy_inputs_by_type(__A , input_image_type='pt' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
__UpperCamelCase = vae.encode(inputs[image_param] ).latent_dist.mode()
__UpperCamelCase = pipe(**__A )[0]
__UpperCamelCase = np.abs(out - out_latents_inputs ).max()
self.assertLess(__A , 1e-4 , 'passing latents as image input generate different result from passing image' )
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def _lowerCamelCase ( self : Any ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self : int , __A : int=0 ):
__UpperCamelCase = torch.manual_seed(__A )
__UpperCamelCase = load_image(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
__UpperCamelCase = {
'prompt': 'turn him into a cyborg',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'image_guidance_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def _lowerCamelCase ( self : Optional[Any] ):
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
__UpperCamelCase = self.get_inputs()
__UpperCamelCase = pipe(**__A ).images
__UpperCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__UpperCamelCase = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowerCamelCase ( self : Dict ):
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=__A )
__UpperCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
__UpperCamelCase = self.get_inputs()
__UpperCamelCase = pipe(**__A ).images
__UpperCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__UpperCamelCase = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowerCamelCase ( self : int ):
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=__A )
__UpperCamelCase = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
__UpperCamelCase = self.get_inputs()
__UpperCamelCase = pipe(**__A ).images
__UpperCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__UpperCamelCase = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowerCamelCase ( self : List[str] ):
__UpperCamelCase = 0
def callback_fn(__A : int , __A : int , __A : torch.FloatTensor ) -> None:
__UpperCamelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__UpperCamelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
__UpperCamelCase = latents[0, -3:, -3:, -1]
__UpperCamelCase = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
__UpperCamelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
__UpperCamelCase = latents[0, -3:, -3:, -1]
__UpperCamelCase = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
__UpperCamelCase = False
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=__A , torch_dtype=torch.floataa )
__UpperCamelCase = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
__UpperCamelCase = self.get_inputs()
pipe(**__A , callback=__A , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _lowerCamelCase ( self : List[str] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=__A , torch_dtype=torch.floataa )
__UpperCamelCase = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__UpperCamelCase = self.get_inputs()
__UpperCamelCase = pipe(**__A )
__UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def _lowerCamelCase ( self : Optional[int] ):
__UpperCamelCase = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
__UpperCamelCase = inputs['image'].resize((5_0_4, 5_0_4) )
__UpperCamelCase = 'timbrooks/instruct-pix2pix'
__UpperCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
__A , safety_checker=__A , )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
__UpperCamelCase = pipe(**__A )
__UpperCamelCase = output.images[0]
__UpperCamelCase = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
__UpperCamelCase = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 53
|
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
a__ : Tuple ='''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 53
| 1
|
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
lowercase__ :Any = 0
lowercase__ :List[str] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
lowercase__ :Optional[Any] = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
lowercase__ :List[Any] = tuple[int, int]
class lowercase :
def __init__( self ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,):
lowercase = pos_x
lowercase = pos_y
lowercase = (pos_y, pos_x)
lowercase = goal_x
lowercase = goal_y
lowercase = g_cost
lowercase = parent
lowercase = self.calculate_heuristic()
lowercase = self.g_cost + self.h_cost
def A__ ( self):
lowercase = self.pos_x - self.goal_x
lowercase = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(a_) + abs(a_)
else:
return sqrt(dy**2 + dx**2)
def __lt__( self ,A__):
return self.f_cost < other.f_cost
class lowercase :
def __init__( self ,A__ ,A__):
lowercase = Node(start[1] ,start[0] ,goal[1] ,goal[0] ,0 ,a_)
lowercase = Node(goal[1] ,goal[0] ,goal[1] ,goal[0] ,9_9_9_9_9 ,a_)
lowercase = [self.start]
lowercase = []
lowercase = False
def A__ ( self):
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
lowercase = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
return self.retrace_path(a_)
self.closed_nodes.append(a_)
lowercase = self.get_successors(a_)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(a_)
else:
# retrieve the best current path
lowercase = self.open_nodes.pop(self.open_nodes.index(a_))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(a_)
else:
self.open_nodes.append(a_)
return [self.start.pos]
def A__ ( self ,A__):
lowercase = []
for action in delta:
lowercase = parent.pos_x + action[1]
lowercase = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(a_) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
a_ ,a_ ,self.target.pos_y ,self.target.pos_x ,parent.g_cost + 1 ,a_ ,))
return successors
def A__ ( self ,A__):
lowercase = node
lowercase = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
lowercase = current_node.parent
path.reverse()
return path
class lowercase :
def __init__( self ,A__ ,A__):
lowercase = AStar(a_ ,a_)
lowercase = AStar(a_ ,a_)
lowercase = False
def A__ ( self):
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
lowercase = self.fwd_astar.open_nodes.pop(0)
lowercase = self.bwd_astar.open_nodes.pop(0)
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
a_ ,a_)
self.fwd_astar.closed_nodes.append(a_)
self.bwd_astar.closed_nodes.append(a_)
lowercase = current_bwd_node
lowercase = current_fwd_node
lowercase = {
self.fwd_astar: self.fwd_astar.get_successors(a_),
self.bwd_astar: self.bwd_astar.get_successors(a_),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(a_)
else:
# retrieve the best current path
lowercase = astar.open_nodes.pop(
astar.open_nodes.index(a_))
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(a_)
else:
astar.open_nodes.append(a_)
return [self.fwd_astar.start.pos]
def A__ ( self ,A__ ,A__):
lowercase = self.fwd_astar.retrace_path(a_)
lowercase = self.bwd_astar.retrace_path(a_)
bwd_path.pop()
bwd_path.reverse()
lowercase = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
lowercase__ :Tuple = (0, 0)
lowercase__ :List[str] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
lowercase__ :List[str] = time.time()
lowercase__ :str = AStar(init, goal)
lowercase__ :Tuple = a_star.search()
lowercase__ :int = time.time() - start_time
print(F'AStar execution time = {end_time:f} seconds')
lowercase__ :Dict = time.time()
lowercase__ :List[str] = BidirectionalAStar(init, goal)
lowercase__ :Tuple = time.time() - bd_start_time
print(F'BidirectionalAStar execution time = {bd_end_time:f} seconds')
| 352
|
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : int =ComputeEnvironment.AMAZON_SAGEMAKER
lowercase_ : Optional[int] =True
lowercase_ : Any ='''ml.p3.2xlarge'''
lowercase_ : Any ='''accelerate_sagemaker_execution_role'''
lowercase_ : Union[str, Any] ='''hf-sm'''
lowercase_ : Any ='''us-east-1'''
lowercase_ : List[str] =1
lowercase_ : Any ='''accelerate-sagemaker-1'''
lowercase_ : Union[str, Any] ='''1.6'''
lowercase_ : Any ='''4.4'''
lowercase_ : Any ='''train.py'''
lowercase_ : int =[
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''False''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
lowercase_ : List[Any] =[
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''--do_test''',
'''False''',
'''--do_predict''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
class lowercase ( unittest.TestCase ):
def A__ ( self):
# If no defaults are changed, `to_kwargs` returns an empty dict.
lowercase = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
assert isinstance(converted_args['''model_name_or_path'''] ,A__)
assert isinstance(converted_args['''do_train'''] ,A__)
assert isinstance(converted_args['''epochs'''] ,A__)
assert isinstance(converted_args['''learning_rate'''] ,A__)
assert isinstance(converted_args['''max_steps'''] ,A__)
with pytest.raises(A__):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args)
| 97
| 0
|
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class a_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(a__ ,'hidden_sizes' ) )
self.parent.assertTrue(hasattr(a__ ,'num_attention_heads' ) )
class a_ :
"""simple docstring"""
def __init__( self : Any ,snake_case : Tuple ,snake_case : List[Any]=13 ,snake_case : Any=64 ,snake_case : List[Any]=3 ,snake_case : int=3 ,snake_case : Union[str, Any]=2 ,snake_case : Dict=1 ,snake_case : Union[str, Any]=16 ,snake_case : Dict=[128, 256, 384] ,snake_case : int=[4, 6, 8] ,snake_case : Dict=[2, 3, 4] ,snake_case : int=[16, 16, 16] ,snake_case : Union[str, Any]=0 ,snake_case : Tuple=[2, 2, 2] ,snake_case : Any=[2, 2, 2] ,snake_case : Union[str, Any]=0.02 ,snake_case : Optional[Any]=True ,snake_case : List[Any]=True ,snake_case : Dict=2 ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =kernel_size
SCREAMING_SNAKE_CASE =stride
SCREAMING_SNAKE_CASE =padding
SCREAMING_SNAKE_CASE =hidden_sizes
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =depths
SCREAMING_SNAKE_CASE =key_dim
SCREAMING_SNAKE_CASE =drop_path_rate
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =attention_ratio
SCREAMING_SNAKE_CASE =mlp_ratio
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =[
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =initializer_range
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_labels )
SCREAMING_SNAKE_CASE =self.get_config()
return config, pixel_values, labels
def _lowerCAmelCase ( self : Dict ):
return LevitConfig(
image_size=self.image_size ,num_channels=self.num_channels ,kernel_size=self.kernel_size ,stride=self.stride ,padding=self.padding ,patch_size=self.patch_size ,hidden_sizes=self.hidden_sizes ,num_attention_heads=self.num_attention_heads ,depths=self.depths ,key_dim=self.key_dim ,drop_path_rate=self.drop_path_rate ,mlp_ratio=self.mlp_ratio ,attention_ratio=self.attention_ratio ,initializer_range=self.initializer_range ,down_ops=self.down_ops ,)
def _lowerCAmelCase ( self : int ,snake_case : Union[str, Any] ,snake_case : Optional[int] ,snake_case : Optional[int] ):
SCREAMING_SNAKE_CASE =LevitModel(config=a__ )
model.to(a__ )
model.eval()
SCREAMING_SNAKE_CASE =model(a__ )
SCREAMING_SNAKE_CASE =(self.image_size, self.image_size)
SCREAMING_SNAKE_CASE =image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE =floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
SCREAMING_SNAKE_CASE =floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) ,)
def _lowerCAmelCase ( self : Any ,snake_case : List[str] ,snake_case : Optional[Any] ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =LevitForImageClassification(a__ )
model.to(a__ )
model.eval()
SCREAMING_SNAKE_CASE =model(a__ ,labels=a__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE =config_and_inputs
SCREAMING_SNAKE_CASE ={"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
__UpperCAmelCase = (
{
"feature-extraction": LevitModel,
"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =LevitModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=a__ ,has_text_modality=a__ ,hidden_size=37 )
def _lowerCAmelCase ( self : List[Any] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _lowerCAmelCase ( self : Optional[int] ):
return
@unittest.skip(reason='Levit does not use inputs_embeds' )
def _lowerCAmelCase ( self : Dict ):
pass
@unittest.skip(reason='Levit does not support input and output embeddings' )
def _lowerCAmelCase ( self : Optional[Any] ):
pass
@unittest.skip(reason='Levit does not output attentions' )
def _lowerCAmelCase ( self : Dict ):
pass
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(a__ )
SCREAMING_SNAKE_CASE =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE =["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,a__ )
def _lowerCAmelCase ( self : Union[str, Any] ):
def check_hidden_states_output(snake_case : Any ,snake_case : Optional[Any] ,snake_case : List[str] ):
SCREAMING_SNAKE_CASE =model_class(a__ )
model.to(a__ )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**self._prepare_for_class(a__ ,a__ ) )
SCREAMING_SNAKE_CASE =outputs.hidden_states
SCREAMING_SNAKE_CASE =len(self.model_tester.depths ) + 1
self.assertEqual(len(a__ ) ,a__ )
SCREAMING_SNAKE_CASE =(self.model_tester.image_size, self.model_tester.image_size)
SCREAMING_SNAKE_CASE =image_size[0], image_size[1]
for _ in range(4 ):
SCREAMING_SNAKE_CASE =floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
SCREAMING_SNAKE_CASE =floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[
height * width,
self.model_tester.hidden_sizes[0],
] ,)
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =True
check_hidden_states_output(a__ ,a__ ,a__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE =True
check_hidden_states_output(a__ ,a__ ,a__ )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _lowerCAmelCase ( self : Optional[Any] ):
pass
def _lowerCAmelCase ( self : Any ,snake_case : Tuple ,snake_case : Union[str, Any] ,snake_case : Any=False ):
SCREAMING_SNAKE_CASE =super()._prepare_for_class(a__ ,a__ ,return_labels=a__ )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a__ )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a__ )
def _lowerCAmelCase ( self : List[Any] ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(a__ )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE =model_class(a__ )
model.to(a__ )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(a__ ,a__ ,return_labels=a__ )
SCREAMING_SNAKE_CASE =model(**a__ ).loss
loss.backward()
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
if model_class in get_values(a__ ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE =model_class(a__ )
model.gradient_checkpointing_enable()
model.to(a__ )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(a__ ,a__ ,return_labels=a__ )
SCREAMING_SNAKE_CASE =model(**a__ ).loss
loss.backward()
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =[
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(a__ ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}' ):
SCREAMING_SNAKE_CASE =problem_type["""title"""]
SCREAMING_SNAKE_CASE =problem_type["""num_labels"""]
SCREAMING_SNAKE_CASE =model_class(a__ )
model.to(a__ )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(a__ ,a__ ,return_labels=a__ )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE =inputs["""labels"""].unsqueeze(1 ).repeat(1 ,problem_type['num_labels'] )
SCREAMING_SNAKE_CASE =inputs["""labels"""].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=a__ ) as warning_list:
SCREAMING_SNAKE_CASE =model(**a__ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f'Something is going wrong in the regression problem: intercepted {w.message}' )
loss.backward()
@slow
def _lowerCAmelCase ( self : int ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =LevitModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowerCAmelCase ( self : int ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
a__ )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=a__ ,return_tensors='pt' ).to(a__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**a__ )
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape ,a__ )
SCREAMING_SNAKE_CASE =torch.tensor([1.0_448, -0.3_745, -1.8_317] ).to(a__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,a__ ,atol=1e-4 ) )
| 334
|
"""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 ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Optional[Any] = "naver-clova-ix/donut-base-finetuned-docvqa"
_UpperCamelCase : Dict = (
"This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
"should be the document containing the information, as well as a `question` that is the question about the "
"document. It returns a text that contains the answer to the question."
)
_UpperCamelCase : Optional[int] = "document_qa"
_UpperCamelCase : Any = AutoProcessor
_UpperCamelCase : Union[str, Any] = VisionEncoderDecoderModel
_UpperCamelCase : Union[str, Any] = ["image", "text"]
_UpperCamelCase : List[str] = ["text"]
def __init__( self , *a__ , **a__ ):
if not is_vision_available():
raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool.""" )
super().__init__(*a__ , **a__ )
def __A ( self , a__ , a__ ):
_lowerCAmelCase : Optional[int] = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
_lowerCAmelCase : Dict = task_prompt.replace("""{user_input}""" , a__ )
_lowerCAmelCase : str = self.pre_processor.tokenizer(
a__ , add_special_tokens=a__ , return_tensors="""pt""" ).input_ids
_lowerCAmelCase : Dict = self.pre_processor(a__ , return_tensors="""pt""" ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def __A ( self , a__ ):
return self.model.generate(
inputs["""pixel_values"""].to(self.device ) , decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=a__ , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=a__ , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=a__ , ).sequences
def __A ( self , a__ ):
_lowerCAmelCase : Tuple = self.pre_processor.batch_decode(a__ )[0]
_lowerCAmelCase : int = sequence.replace(self.pre_processor.tokenizer.eos_token , """""" )
_lowerCAmelCase : List[str] = sequence.replace(self.pre_processor.tokenizer.pad_token , """""" )
_lowerCAmelCase : List[str] = re.sub(r"""<.*?>""" , """""" , a__ , count=1 ).strip() # remove first task start token
_lowerCAmelCase : List[str] = self.pre_processor.tokenajson(a__ )
return sequence["answer"]
| 44
| 0
|
def lowerCamelCase__ ( a ) -> bool:
return str(a ) == str(a )[::-1]
def lowerCamelCase__ ( a ) -> int:
return int(a ) + int(str(a )[::-1] )
def lowerCamelCase__ ( a = 1_00_00 ) -> int:
_A: Tuple = []
for num in range(1 , a ):
_A: int = 0
_A: Any = num
while iterations < 50:
_A: List[Any] = sum_reverse(a )
iterations += 1
if is_palindrome(a ):
break
else:
lychrel_nums.append(a )
return len(a )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 301
|
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCAmelCase :
'''simple docstring'''
__UpperCamelCase : Any = MBartConfig
__UpperCamelCase : Tuple = {}
__UpperCamelCase : Dict = '''gelu'''
def __init__( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any]=1_3 , lowerCAmelCase_ : Dict=7 , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : Union[str, Any]=9_9 , lowerCAmelCase_ : Dict=3_2 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : Union[str, Any]=3_7 , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : List[str]=2_0 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : Optional[int]=1 , lowerCAmelCase_ : List[Any]=0 , ):
"""simple docstring"""
_A: Union[str, Any] = parent
_A: List[Any] = batch_size
_A: Dict = seq_length
_A: Dict = is_training
_A: str = use_labels
_A: int = vocab_size
_A: str = hidden_size
_A: Tuple = num_hidden_layers
_A: Optional[Any] = num_attention_heads
_A: Tuple = intermediate_size
_A: int = hidden_dropout_prob
_A: Tuple = attention_probs_dropout_prob
_A: Tuple = max_position_embeddings
_A: Dict = eos_token_id
_A: int = pad_token_id
_A: Any = bos_token_id
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A: Optional[int] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_A: Dict = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_A: List[Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
_A: Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A: int = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_A: Any = prepare_mbart_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return config, inputs_dict
def __magic_name__ ( self : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[str] ):
"""simple docstring"""
_A: Tuple = TFMBartModel(config=lowerCAmelCase_ ).get_decoder()
_A: List[str] = inputs_dict['''input_ids''']
_A: Tuple = input_ids[:1, :]
_A: List[Any] = inputs_dict['''attention_mask'''][:1, :]
_A: str = inputs_dict['''head_mask''']
_A: Optional[Any] = 1
# first forward pass
_A: Any = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , use_cache=lowerCAmelCase_ )
_A , _A: List[str] = outputs.to_tuple()
_A: Dict = past_key_values[1]
def lowerCamelCase__ ( a , a , a , a=None , a=None , a=None , a=None , a=None , ) -> Tuple:
if attention_mask is None:
_A: Union[str, Any] = tf.cast(tf.math.not_equal(a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_A: Optional[int] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_A: Tuple = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_A: Union[str, Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_A: Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
__UpperCamelCase : int = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
__UpperCamelCase : Tuple = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
__UpperCamelCase : List[Any] = True
__UpperCamelCase : int = False
__UpperCamelCase : Optional[Any] = False
def __magic_name__ ( self : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int ):
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def __magic_name__ ( self : Any ):
"""simple docstring"""
_A: Dict = TFMBartModelTester(self )
_A: Tuple = ConfigTester(self , config_class=lowerCAmelCase_ )
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
_A: str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_ )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
__UpperCamelCase : List[str] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
__UpperCamelCase : Union[str, Any] = '''facebook/mbart-large-en-ro'''
@cached_property
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __magic_name__ ( self : str ):
"""simple docstring"""
_A: Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __magic_name__ ( self : Union[str, Any] , **lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Optional[Any] = self.translate_src_text(**lowerCAmelCase_ )
self.assertListEqual(self.expected_text , lowerCAmelCase_ )
def __magic_name__ ( self : Dict , **lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Any = self.tokenizer(self.src_text , **lowerCAmelCase_ , return_tensors='''tf''' )
_A: Any = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
_A: Optional[Any] = self.tokenizer.batch_decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_ )
return generated_words
@slow
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 301
| 1
|
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
lowercase : List[str] = {
"""iou_prediction_head.layers.0""": """iou_prediction_head.proj_in""",
"""iou_prediction_head.layers.1""": """iou_prediction_head.layers.0""",
"""iou_prediction_head.layers.2""": """iou_prediction_head.proj_out""",
"""mask_decoder.output_upscaling.0""": """mask_decoder.upscale_conv1""",
"""mask_decoder.output_upscaling.1""": """mask_decoder.upscale_layer_norm""",
"""mask_decoder.output_upscaling.3""": """mask_decoder.upscale_conv2""",
"""mask_downscaling.0""": """mask_embed.conv1""",
"""mask_downscaling.1""": """mask_embed.layer_norm1""",
"""mask_downscaling.3""": """mask_embed.conv2""",
"""mask_downscaling.4""": """mask_embed.layer_norm2""",
"""mask_downscaling.6""": """mask_embed.conv3""",
"""point_embeddings""": """point_embed""",
"""pe_layer.positional_encoding_gaussian_matrix""": """shared_embedding.positional_embedding""",
"""image_encoder""": """vision_encoder""",
"""neck.0""": """neck.conv1""",
"""neck.1""": """neck.layer_norm1""",
"""neck.2""": """neck.conv2""",
"""neck.3""": """neck.layer_norm2""",
"""patch_embed.proj""": """patch_embed.projection""",
""".norm""": """.layer_norm""",
"""blocks""": """layers""",
}
def A_ ( A__ ) -> int:
a__ : str = {}
state_dict.pop('pixel_mean' , A__ )
state_dict.pop('pixel_std' , A__ )
a__ : Any = R'.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
a__ : str = key.replace(A__ , A__ )
if re.match(A__ , A__ ):
a__ : Any = int(re.match(A__ , A__ ).group(2 ) )
if layer_nb == 0:
a__ : List[str] = key.replace('layers.0' , 'proj_in' )
elif layer_nb == 1:
a__ : Optional[int] = key.replace('layers.1' , 'layers.0' )
elif layer_nb == 2:
a__ : List[str] = key.replace('layers.2' , 'proj_out' )
a__ : Optional[Any] = value
a__ : List[Any] = model_state_dict[
'prompt_encoder.shared_embedding.positional_embedding'
]
return model_state_dict
def A_ ( A__ , A__ , A__ , A__="ybelkada/segment-anything" ) -> str:
a__ : int = hf_hub_download(A__ , F'checkpoints/{model_name}.pth' )
if "sam_vit_b" in model_name:
a__ : Tuple = SamConfig()
elif "sam_vit_l" in model_name:
a__ : List[str] = SamVisionConfig(
hidden_size=1024 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
a__ : List[str] = SamConfig(
vision_config=A__ , )
elif "sam_vit_h" in model_name:
a__ : Tuple = SamVisionConfig(
hidden_size=1280 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
a__ : Optional[int] = SamConfig(
vision_config=A__ , )
a__ : Tuple = torch.load(A__ , map_location='cpu' )
a__ : Optional[int] = replace_keys(A__ )
a__ : Union[str, Any] = SamImageProcessor()
a__ : Optional[Any] = SamProcessor(image_processor=A__ )
a__ : List[Any] = SamModel(A__ )
hf_model.load_state_dict(A__ )
a__ : Optional[Any] = hf_model.to('cuda' )
a__ : List[Any] = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
a__ : str = Image.open(requests.get(A__ , stream=A__ ).raw ).convert('RGB' )
a__ : int = [[[400, 650]]]
a__ : List[str] = [[1]]
a__ : str = processor(images=np.array(A__ ) , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
a__ : Union[str, Any] = hf_model(**A__ )
a__ : Any = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.5_79_89_02_51_15_96_68
a__ : Any = processor(
images=np.array(A__ ) , input_points=A__ , input_labels=A__ , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
a__ : Union[str, Any] = hf_model(**A__ )
a__ : Dict = output.iou_scores.squeeze()
assert scores[-1].item() == 0.97_12_60_30_92_19_36_04
a__ : str = ((75, 275, 1725, 850),)
a__ : Union[str, Any] = processor(images=np.array(A__ ) , input_boxes=A__ , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
a__ : List[Any] = hf_model(**A__ )
a__ : Any = output.iou_scores.squeeze()
assert scores[-1].item() == 0.86_86_01_56_05_92_65_14
# Test with 2 points and 1 image.
a__ : Dict = [[[400, 650], [800, 650]]]
a__ : List[str] = [[1, 1]]
a__ : Any = processor(
images=np.array(A__ ) , input_points=A__ , input_labels=A__ , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
a__ : Optional[Any] = hf_model(**A__ )
a__ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.99_36_04_77_92_43_46_92
if __name__ == "__main__":
lowercase : Dict = argparse.ArgumentParser()
lowercase : List[str] = ["""sam_vit_b_01ec64""", """sam_vit_h_4b8939""", """sam_vit_l_0b3195"""]
parser.add_argument(
"""--model_name""",
default="""sam_vit_h_4b8939""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
parser.add_argument(
"""--model_hub_id""",
default="""ybelkada/segment-anything""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
lowercase : List[Any] = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 99
|
'''simple docstring'''
import operator as op
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> int:
lowercase_ : Optional[Any] = []
lowercase_ : str = lambda UpperCAmelCase__ , UpperCAmelCase__ : int(x / y ) # noqa: E731 integer division operation
lowercase_ : Optional[Any] = {
"""^""": op.pow,
"""*""": op.mul,
"""/""": div,
"""+""": op.add,
"""-""": op.sub,
} # operators & their respective operation
# print table header
print("""Symbol""".center(8 ) , """Action""".center(12 ) , """Stack""" , sep=""" | """ )
print("""-""" * (30 + len(UpperCAmelCase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(UpperCAmelCase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ("""push(""" + x + """)""").ljust(12 ) , """,""".join(UpperCAmelCase__ ) , sep=""" | """ )
else:
lowercase_ : str = stack.pop() # pop stack
# output in tabular format
print("""""".rjust(8 ) , ("""pop(""" + b + """)""").ljust(12 ) , """,""".join(UpperCAmelCase__ ) , sep=""" | """ )
lowercase_ : Optional[int] = stack.pop() # pop stack
# output in tabular format
print("""""".rjust(8 ) , ("""pop(""" + a + """)""").ljust(12 ) , """,""".join(UpperCAmelCase__ ) , sep=""" | """ )
stack.append(
str(opr[x](int(UpperCAmelCase__ ) , int(UpperCAmelCase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ("""push(""" + a + x + b + """)""").ljust(12 ) , """,""".join(UpperCAmelCase__ ) , sep=""" | """ , )
return int(stack[0] )
if __name__ == "__main__":
_lowercase : Tuple = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| 239
| 0
|
from __future__ import annotations
def __lowerCamelCase ( __magic_name__ : list , __magic_name__ : int | None = None , __magic_name__ : int | None = None ):
if start is None:
a__: int =0
if end is None:
a__: Any =len(__magic_name__ ) - 1
if start >= end:
return
a__: Dict =(start + end) // 2
slowsort(__magic_name__ , __magic_name__ , __magic_name__ )
slowsort(__magic_name__ , mid + 1 , __magic_name__ )
if sequence[end] < sequence[mid]:
a__ , a__: str =sequence[mid], sequence[end]
slowsort(__magic_name__ , __magic_name__ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 42
|
from __future__ import annotations
def __lowerCamelCase ( __magic_name__ : str , __magic_name__ : Any , __magic_name__ : List[Any] , __magic_name__ : str ): # noqa: E741
while r - l > 1:
a__: Any =(l + r) // 2
if v[m] >= key:
a__: Any =m
else:
a__: Optional[int] =m # noqa: E741
return r
def __lowerCamelCase ( __magic_name__ : list[int] ):
if len(__magic_name__ ) == 0:
return 0
a__: Tuple =[0] * len(__magic_name__ )
a__: Optional[int] =1
a__: Optional[Any] =v[0]
for i in range(1 , len(__magic_name__ ) ):
if v[i] < tail[0]:
a__: Union[str, Any] =v[i]
elif v[i] > tail[length - 1]:
a__: Optional[int] =v[i]
length += 1
else:
a__: Dict =v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42
| 1
|
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase__ = logging.getLogger(__name__)
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = np.argmax(lowercase ,axis=1 )
return np.sum(outputs == labels )
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
with open(lowercase ,encoding="""utf_8""" ) as f:
_UpperCAmelCase = csv.reader(lowercase )
_UpperCAmelCase = []
next(lowercase ) # skip the first line
for line in tqdm(lowercase ):
output.append((""" """.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = []
for dataset in encoded_datasets:
_UpperCAmelCase = len(lowercase )
_UpperCAmelCase = np.zeros((n_batch, 2, input_len) ,dtype=np.intaa )
_UpperCAmelCase = np.zeros((n_batch, 2) ,dtype=np.intaa )
_UpperCAmelCase = np.full((n_batch, 2, input_len) ,fill_value=-1_00 ,dtype=np.intaa )
_UpperCAmelCase = np.zeros((n_batch,) ,dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(lowercase ):
_UpperCAmelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
_UpperCAmelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
_UpperCAmelCase = with_conta
_UpperCAmelCase = with_conta
_UpperCAmelCase = len(lowercase ) - 1
_UpperCAmelCase = len(lowercase ) - 1
_UpperCAmelCase = with_conta
_UpperCAmelCase = with_conta
_UpperCAmelCase = mc_label
_UpperCAmelCase = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(lowercase ) for t in all_inputs ) )
return tensor_datasets
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--model_name""" ,type=lowercase ,default="""openai-gpt""" ,help="""pretrained model name""" )
parser.add_argument("""--do_train""" ,action="""store_true""" ,help="""Whether to run training.""" )
parser.add_argument("""--do_eval""" ,action="""store_true""" ,help="""Whether to run eval on the dev set.""" )
parser.add_argument(
"""--output_dir""" ,default=lowercase ,type=lowercase ,required=lowercase ,help="""The output directory where the model predictions and checkpoints will be written.""" ,)
parser.add_argument("""--train_dataset""" ,type=lowercase ,default="""""" )
parser.add_argument("""--eval_dataset""" ,type=lowercase ,default="""""" )
parser.add_argument("""--seed""" ,type=lowercase ,default=42 )
parser.add_argument("""--num_train_epochs""" ,type=lowercase ,default=3 )
parser.add_argument("""--train_batch_size""" ,type=lowercase ,default=8 )
parser.add_argument("""--eval_batch_size""" ,type=lowercase ,default=16 )
parser.add_argument("""--adam_epsilon""" ,default=1E-8 ,type=lowercase ,help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""" ,type=lowercase ,default=1 )
parser.add_argument(
"""--max_steps""" ,default=-1 ,type=lowercase ,help=(
"""If > 0: set total number of training steps to perform. Override num_train_epochs."""
) ,)
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=lowercase ,default=1 ,help="""Number of updates steps to accumulate before performing a backward/update pass.""" ,)
parser.add_argument("""--learning_rate""" ,type=lowercase ,default=6.25E-5 )
parser.add_argument("""--warmup_steps""" ,default=0 ,type=lowercase ,help="""Linear warmup over warmup_steps.""" )
parser.add_argument("""--lr_schedule""" ,type=lowercase ,default="""warmup_linear""" )
parser.add_argument("""--weight_decay""" ,type=lowercase ,default=0.01 )
parser.add_argument("""--lm_coef""" ,type=lowercase ,default=0.9 )
parser.add_argument("""--n_valid""" ,type=lowercase ,default=3_74 )
parser.add_argument("""--server_ip""" ,type=lowercase ,default="""""" ,help="""Can be used for distant debugging.""" )
parser.add_argument("""--server_port""" ,type=lowercase ,default="""""" ,help="""Can be used for distant debugging.""" )
_UpperCAmelCase = parser.parse_args()
print(lowercase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("""Waiting for debugger attach""" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,redirect_output=lowercase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
_UpperCAmelCase = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
_UpperCAmelCase = torch.cuda.device_count()
logger.info("""device: {}, n_gpu {}""".format(lowercase ,lowercase ) )
if not args.do_train and not args.do_eval:
raise ValueError("""At least one of `do_train` or `do_eval` must be True.""" )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
_UpperCAmelCase = ["""_start_""", """_delimiter_""", """_classify_"""]
_UpperCAmelCase = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(lowercase )
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(lowercase )
_UpperCAmelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(lowercase ) )
model.to(lowercase )
# Load and encode the datasets
def tokenize_and_encode(lowercase ):
if isinstance(lowercase ,lowercase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(lowercase ) )
elif isinstance(lowercase ,lowercase ):
return obj
return [tokenize_and_encode(lowercase ) for o in obj]
logger.info("""Encoding dataset...""" )
_UpperCAmelCase = load_rocstories_dataset(args.train_dataset )
_UpperCAmelCase = load_rocstories_dataset(args.eval_dataset )
_UpperCAmelCase = (train_dataset, eval_dataset)
_UpperCAmelCase = tokenize_and_encode(lowercase )
# Compute the max input length for the Transformer
_UpperCAmelCase = model.config.n_positions // 2 - 2
_UpperCAmelCase = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) ,len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
_UpperCAmelCase = min(lowercase ,model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
_UpperCAmelCase = pre_process_datasets(lowercase ,lowercase ,lowercase ,*lowercase )
_UpperCAmelCase , _UpperCAmelCase = tensor_datasets[0], tensor_datasets[1]
_UpperCAmelCase = TensorDataset(*lowercase )
_UpperCAmelCase = RandomSampler(lowercase )
_UpperCAmelCase = DataLoader(lowercase ,sampler=lowercase ,batch_size=args.train_batch_size )
_UpperCAmelCase = TensorDataset(*lowercase )
_UpperCAmelCase = SequentialSampler(lowercase )
_UpperCAmelCase = DataLoader(lowercase ,sampler=lowercase ,batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
_UpperCAmelCase = args.max_steps
_UpperCAmelCase = args.max_steps // (len(lowercase ) // args.gradient_accumulation_steps) + 1
else:
_UpperCAmelCase = len(lowercase ) // args.gradient_accumulation_steps * args.num_train_epochs
_UpperCAmelCase = list(model.named_parameters() )
_UpperCAmelCase = ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
_UpperCAmelCase = [
{
"""params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"""weight_decay""": args.weight_decay,
},
{"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], """weight_decay""": 0.0},
]
_UpperCAmelCase = AdamW(lowercase ,lr=args.learning_rate ,eps=args.adam_epsilon )
_UpperCAmelCase = get_linear_schedule_with_warmup(
lowercase ,num_warmup_steps=args.warmup_steps ,num_training_steps=lowercase )
if args.do_train:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) ,desc="""Epoch""" ):
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = tqdm(lowercase ,desc="""Training""" )
for step, batch in enumerate(lowercase ):
_UpperCAmelCase = tuple(t.to(lowercase ) for t in batch )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = batch
_UpperCAmelCase = model(lowercase ,mc_token_ids=lowercase ,lm_labels=lowercase ,mc_labels=lowercase )
_UpperCAmelCase = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
_UpperCAmelCase = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
_UpperCAmelCase = """Training loss: {:.2e} lr: {:.2e}""".format(lowercase ,scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
_UpperCAmelCase = model.module if hasattr(lowercase ,"""module""" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
_UpperCAmelCase = os.path.join(args.output_dir ,lowercase )
_UpperCAmelCase = os.path.join(args.output_dir ,lowercase )
torch.save(model_to_save.state_dict() ,lowercase )
model_to_save.config.to_json_file(lowercase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
_UpperCAmelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
_UpperCAmelCase = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(lowercase )
if args.do_eval:
model.eval()
_UpperCAmelCase , _UpperCAmelCase = 0, 0
_UpperCAmelCase , _UpperCAmelCase = 0, 0
for batch in tqdm(lowercase ,desc="""Evaluating""" ):
_UpperCAmelCase = tuple(t.to(lowercase ) for t in batch )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = batch
with torch.no_grad():
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = model(
lowercase ,mc_token_ids=lowercase ,lm_labels=lowercase ,mc_labels=lowercase )
_UpperCAmelCase = mc_logits.detach().cpu().numpy()
_UpperCAmelCase = mc_labels.to("""cpu""" ).numpy()
_UpperCAmelCase = accuracy(lowercase ,lowercase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
_UpperCAmelCase = eval_loss / nb_eval_steps
_UpperCAmelCase = eval_accuracy / nb_eval_examples
_UpperCAmelCase = tr_loss / nb_tr_steps if args.do_train else None
_UpperCAmelCase = {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}
_UpperCAmelCase = os.path.join(args.output_dir ,"""eval_results.txt""" )
with open(lowercase ,"""w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" ,lowercase ,str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 289
|
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class a :
def __init__( self : Union[str, Any] ):
_UpperCAmelCase = {}
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase : str ):
_UpperCAmelCase = {}
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : float ):
if nodea not in self.connections:
self.add_node(__lowerCAmelCase )
if nodea not in self.connections:
self.add_node(__lowerCAmelCase )
_UpperCAmelCase = probability
def lowerCAmelCase_ ( self : Optional[Any] ):
return list(self.connections )
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : str ):
_UpperCAmelCase = 0
_UpperCAmelCase = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(lowercase ,lowercase ,lowercase )
_UpperCAmelCase = Counter(graph.get_nodes() )
_UpperCAmelCase = start
for _ in range(lowercase ):
_UpperCAmelCase = graph.transition(lowercase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 289
| 1
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = state_dict.pop(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[int] = val
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ : Dict = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCAmelCase_ : Optional[int] = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
UpperCAmelCase_ : Union[str, Any] = value
else:
UpperCAmelCase_ : int = value
return new_state_dict
def a__ ( _SCREAMING_SNAKE_CASE : List[str] ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : Dict = ""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
UpperCAmelCase_ : str = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase_ : Optional[int] = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ : List[Any] = in_proj_weight[:2_56, :]
UpperCAmelCase_ : Optional[int] = in_proj_bias[:2_56]
UpperCAmelCase_ : Dict = in_proj_weight[2_56:5_12, :]
UpperCAmelCase_ : Dict = in_proj_bias[2_56:5_12]
UpperCAmelCase_ : int = in_proj_weight[-2_56:, :]
UpperCAmelCase_ : Dict = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
UpperCAmelCase_ : Optional[int] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase_ : str = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ : Union[str, Any] = in_proj_weight[:2_56, :]
UpperCAmelCase_ : Optional[int] = in_proj_bias[:2_56]
UpperCAmelCase_ : Optional[Any] = in_proj_weight[2_56:5_12, :]
UpperCAmelCase_ : List[str] = in_proj_bias[2_56:5_12]
UpperCAmelCase_ : Optional[int] = in_proj_weight[-2_56:, :]
UpperCAmelCase_ : List[Any] = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
UpperCAmelCase_ : int = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCAmelCase_ : Union[str, Any] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
UpperCAmelCase_ : List[str] = in_proj_weight_cross_attn[:2_56, :]
UpperCAmelCase_ : Dict = in_proj_bias_cross_attn[:2_56]
UpperCAmelCase_ : List[Any] = in_proj_weight_cross_attn[2_56:5_12, :]
UpperCAmelCase_ : int = in_proj_bias_cross_attn[2_56:5_12]
UpperCAmelCase_ : int = in_proj_weight_cross_attn[-2_56:, :]
UpperCAmelCase_ : str = in_proj_bias_cross_attn[-2_56:]
def a__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ) -> Any:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = image.size
UpperCAmelCase_ : int = max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = 8_00 if "detection" in checkpoint_url else 10_00
UpperCAmelCase_ : str = target_max_size / current_max_size
UpperCAmelCase_ : Tuple = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a__ ( _SCREAMING_SNAKE_CASE : Any ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : Any = F.to_tensor(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = F.normalize(_SCREAMING_SNAKE_CASE , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : str ) -> Any:
"""simple docstring"""
logger.info("Converting model..." )
# load original state dict
UpperCAmelCase_ : Union[str, Any] = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[str] = rename_backbone_keys(_SCREAMING_SNAKE_CASE )
# query, key and value matrices need special treatment
read_in_q_k_v(_SCREAMING_SNAKE_CASE )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase_ : Any = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
UpperCAmelCase_ : Optional[Any] = state_dict.pop(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[int] = val
# create HuggingFace model and load state dict
UpperCAmelCase_ : str = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCAmelCase_ : str = 15
UpperCAmelCase_ : str = 2
UpperCAmelCase_ : Union[str, Any] = {0: "table", 1: "table rotated"}
UpperCAmelCase_ : Tuple = idalabel
UpperCAmelCase_ : List[str] = {v: k for k, v in idalabel.items()}
else:
UpperCAmelCase_ : Tuple = 1_25
UpperCAmelCase_ : Tuple = 6
UpperCAmelCase_ : Union[str, Any] = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
UpperCAmelCase_ : str = idalabel
UpperCAmelCase_ : Any = {v: k for k, v in idalabel.items()}
UpperCAmelCase_ : List[Any] = DetrImageProcessor(
format="coco_detection" , max_size=8_00 if "detection" in checkpoint_url else 10_00 )
UpperCAmelCase_ : Optional[int] = TableTransformerForObjectDetection(_SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
model.eval()
# verify our conversion
UpperCAmelCase_ : Optional[Any] = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
UpperCAmelCase_ : Dict = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = Image.open(_SCREAMING_SNAKE_CASE ).convert("RGB" )
UpperCAmelCase_ : int = normalize(resize(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ).unsqueeze(0 )
UpperCAmelCase_ : Optional[int] = model(_SCREAMING_SNAKE_CASE )
if "detection" in checkpoint_url:
UpperCAmelCase_ : Any = (1, 15, 3)
UpperCAmelCase_ : Optional[int] = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]] )
UpperCAmelCase_ : Dict = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]] )
else:
UpperCAmelCase_ : Union[str, Any] = (1, 1_25, 7)
UpperCAmelCase_ : List[str] = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]] )
UpperCAmelCase_ : Any = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
UpperCAmelCase_ : List[str] = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(_SCREAMING_SNAKE_CASE )
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_lowerCamelCase = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 67
|
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class _snake_case (__SCREAMING_SNAKE_CASE):
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : str = tempfile.mkdtemp()
UpperCAmelCase_ : Optional[int] = 8
# DPR tok
UpperCAmelCase_ : Optional[Any] = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
UpperCAmelCase_ : Any = os.path.join(self.tmpdirname ,"dpr_tokenizer" )
os.makedirs(_snake_case ,exist_ok=_snake_case )
UpperCAmelCase_ : List[str] = os.path.join(_snake_case ,DPR_VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
# BART tok
UpperCAmelCase_ : Optional[int] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
UpperCAmelCase_ : str = dict(zip(_snake_case ,range(len(_snake_case ) ) ) )
UpperCAmelCase_ : Optional[int] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
UpperCAmelCase_ : Optional[int] = {"unk_token": "<unk>"}
UpperCAmelCase_ : Dict = os.path.join(self.tmpdirname ,"bart_tokenizer" )
os.makedirs(_snake_case ,exist_ok=_snake_case )
UpperCAmelCase_ : Any = os.path.join(_snake_case ,BART_VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase_ : Union[str, Any] = os.path.join(_snake_case ,BART_VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(_snake_case ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(_snake_case ) )
def UpperCamelCase__ ( self ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"dpr_tokenizer" ) )
def UpperCamelCase__ ( self ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"dpr_tokenizer" ) )
def UpperCamelCase__ ( self ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"bart_tokenizer" ) )
def UpperCamelCase__ ( self ):
shutil.rmtree(self.tmpdirname )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Any = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("embeddings" ,string_factory="Flat" ,metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : int = self.get_dummy_dataset()
UpperCAmelCase_ : Optional[Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size ,question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() ,)
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
UpperCAmelCase_ : List[Any] = dataset
UpperCAmelCase_ : Any = RagRetriever(
_snake_case ,question_encoder_tokenizer=self.get_dpr_tokenizer() ,generator_tokenizer=self.get_bart_tokenizer() ,)
return retriever
def UpperCamelCase__ ( self ,_snake_case ):
UpperCAmelCase_ : Union[str, Any] = self.get_dummy_dataset()
UpperCAmelCase_ : Union[str, Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size ,question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() ,index_name="custom" ,)
if from_disk:
UpperCAmelCase_ : str = os.path.join(self.tmpdirname ,"dataset" )
UpperCAmelCase_ : str = os.path.join(self.tmpdirname ,"index.faiss" )
dataset.get_index("embeddings" ).save(os.path.join(self.tmpdirname ,"index.faiss" ) )
dataset.drop_index("embeddings" )
dataset.save_to_disk(os.path.join(self.tmpdirname ,"dataset" ) )
del dataset
UpperCAmelCase_ : List[Any] = RagRetriever(
_snake_case ,question_encoder_tokenizer=self.get_dpr_tokenizer() ,generator_tokenizer=self.get_bart_tokenizer() ,)
else:
UpperCAmelCase_ : int = RagRetriever(
_snake_case ,question_encoder_tokenizer=self.get_dpr_tokenizer() ,generator_tokenizer=self.get_bart_tokenizer() ,index=CustomHFIndex(config.retrieval_vector_size ,_snake_case ) ,)
return retriever
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Optional[int] = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("embeddings" ,string_factory="Flat" ,metric_type=faiss.METRIC_INNER_PRODUCT )
UpperCAmelCase_ : Optional[Any] = os.path.join(self.tmpdirname ,"hf_bert_base.hnswSQ8_correct_phi_128.c_index" )
dataset.save_faiss_index("embeddings" ,index_file_name + ".index.dpr" )
pickle.dump(dataset["id"] ,open(index_file_name + ".index_meta.dpr" ,"wb" ) )
UpperCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,"psgs_w100.tsv.pkl" )
UpperCAmelCase_ : Optional[Any] = {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
pickle.dump(_snake_case ,open(_snake_case ,"wb" ) )
UpperCAmelCase_ : List[Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size ,question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() ,index_name="legacy" ,index_path=self.tmpdirname ,)
UpperCAmelCase_ : Optional[Any] = RagRetriever(
_snake_case ,question_encoder_tokenizer=self.get_dpr_tokenizer() ,generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : List[str] = 1
UpperCAmelCase_ : Dict = self.get_dummy_canonical_hf_index_retriever()
UpperCAmelCase_ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : str = retriever.retrieve(_snake_case ,n_docs=_snake_case )
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(_snake_case ) ,2 )
self.assertEqual(sorted(doc_dicts[0] ) ,["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) ,_snake_case )
self.assertEqual(doc_dicts[0]["id"][0] ,"1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] ,"0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() ,[[1], [0]] )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Tuple = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
UpperCAmelCase_ : Union[str, Any] = self.get_dummy_dataset()
retriever.save_pretrained(_snake_case )
UpperCAmelCase_ : Optional[Any] = RagRetriever.from_pretrained(_snake_case )
self.assertIsInstance(_snake_case ,_snake_case )
UpperCAmelCase_ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ : Dict = retriever.retrieve(_snake_case ,n_docs=1 )
self.assertTrue(out is not None )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Tuple = 1
UpperCAmelCase_ : Any = self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case )
UpperCAmelCase_ : Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[str] = retriever.retrieve(_snake_case ,n_docs=_snake_case )
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(_snake_case ) ,2 )
self.assertEqual(sorted(doc_dicts[0] ) ,["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) ,_snake_case )
self.assertEqual(doc_dicts[0]["id"][0] ,"1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] ,"0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() ,[[1], [0]] )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : str = self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(_snake_case )
UpperCAmelCase_ : int = RagRetriever.from_pretrained(_snake_case )
self.assertIsInstance(_snake_case ,_snake_case )
UpperCAmelCase_ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ : List[Any] = retriever.retrieve(_snake_case ,n_docs=1 )
self.assertTrue(out is not None )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Optional[int] = 1
UpperCAmelCase_ : Union[str, Any] = self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case )
UpperCAmelCase_ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Dict = retriever.retrieve(_snake_case ,n_docs=_snake_case )
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(_snake_case ) ,2 )
self.assertEqual(sorted(doc_dicts[0] ) ,["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) ,_snake_case )
self.assertEqual(doc_dicts[0]["id"][0] ,"1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] ,"0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() ,[[1], [0]] )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : int = self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(_snake_case )
UpperCAmelCase_ : str = RagRetriever.from_pretrained(_snake_case )
self.assertIsInstance(_snake_case ,_snake_case )
UpperCAmelCase_ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ : Optional[int] = retriever.retrieve(_snake_case ,n_docs=1 )
self.assertTrue(out is not None )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Optional[Any] = 1
UpperCAmelCase_ : List[str] = self.get_dummy_legacy_index_retriever()
UpperCAmelCase_ : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = retriever.retrieve(_snake_case ,n_docs=_snake_case )
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(_snake_case ) ,2 )
self.assertEqual(sorted(doc_dicts[0] ) ,["text", "title"] )
self.assertEqual(len(doc_dicts[0]["text"] ) ,_snake_case )
self.assertEqual(doc_dicts[0]["text"][0] ,"bar" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["text"][0] ,"foo" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() ,[[1], [0]] )
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Union[str, Any] = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(_snake_case )
UpperCAmelCase_ : Tuple = RagRetriever.from_pretrained(_snake_case )
self.assertIsInstance(_snake_case ,_snake_case )
UpperCAmelCase_ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ : Dict = retriever.retrieve(_snake_case ,n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def UpperCamelCase__ ( self ):
import torch
UpperCAmelCase_ : List[str] = 1
UpperCAmelCase_ : List[Any] = self.get_dummy_canonical_hf_index_retriever()
UpperCAmelCase_ : Tuple = [[5, 7], [10, 11]]
UpperCAmelCase_ : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ : Optional[int] = retriever(_snake_case ,_snake_case ,prefix=retriever.config.generator.prefix ,n_docs=_snake_case )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = (
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
)
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(_snake_case ,_snake_case )
self.assertIsInstance(_snake_case ,_snake_case )
self.assertIsInstance(_snake_case ,np.ndarray )
UpperCAmelCase_ : Optional[Any] = retriever(
_snake_case ,_snake_case ,prefix=retriever.config.generator.prefix ,n_docs=_snake_case ,return_tensors="pt" ,)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = ( # noqa: F841
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
out["doc_ids"],
)
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(_snake_case ,torch.Tensor )
self.assertIsInstance(_snake_case ,torch.Tensor )
self.assertIsInstance(_snake_case ,torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : int = self.get_dpr_ctx_encoder_tokenizer()
UpperCAmelCase_ : int = 1
UpperCAmelCase_ : str = self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case )
retriever.set_ctx_encoder_tokenizer(_snake_case )
UpperCAmelCase_ : Optional[int] = [[5, 7], [10, 11]]
UpperCAmelCase_ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
UpperCAmelCase_ : Optional[int] = retriever(_snake_case ,_snake_case ,prefix=retriever.config.generator.prefix ,n_docs=_snake_case )
self.assertEqual(
len(_snake_case ) ,6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask") ) ,_snake_case ) # check for doc token related keys in dictionary.
| 67
| 1
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
snake_case_ = [
"""openmmlab/upernet-convnext-tiny""",
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
snake_case_ = """UperNetConfig"""
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Tuple , lowercase_ :int , lowercase_ :int , lowercase_ :Union[int, Tuple[int, int]] , lowercase_ :Union[int, Tuple[int, int], str] = 0 , lowercase_ :bool = False , lowercase_ :Union[int, Tuple[int, int]] = 1 , ) -> None:
super().__init__()
UpperCAmelCase = nn.Convad(
in_channels=lowercase_ , out_channels=lowercase_ , kernel_size=lowercase_ , padding=lowercase_ , bias=lowercase_ , dilation=lowercase_ , )
UpperCAmelCase = nn.BatchNormad(lowercase_ )
UpperCAmelCase = nn.ReLU()
def UpperCAmelCase__ ( self :Optional[Any] , lowercase_ :torch.Tensor ) -> torch.Tensor:
UpperCAmelCase = self.conv(lowercase_ )
UpperCAmelCase = self.batch_norm(lowercase_ )
UpperCAmelCase = self.activation(lowercase_ )
return output
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Optional[Any] , lowercase_ :int , lowercase_ :int , lowercase_ :int ) -> None:
super().__init__()
UpperCAmelCase = [
nn.AdaptiveAvgPoolad(lowercase_ ),
UperNetConvModule(lowercase_ , lowercase_ , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(lowercase_ ) , lowercase_ )
def UpperCAmelCase__ ( self :List[str] , lowercase_ :torch.Tensor ) -> torch.Tensor:
UpperCAmelCase = input
for layer in self.layers:
UpperCAmelCase = layer(lowercase_ )
return hidden_state
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Optional[Any] , lowercase_ :Tuple[int, ...] , lowercase_ :int , lowercase_ :int , lowercase_ :bool ) -> None:
super().__init__()
UpperCAmelCase = pool_scales
UpperCAmelCase = align_corners
UpperCAmelCase = in_channels
UpperCAmelCase = channels
UpperCAmelCase = []
for i, pool_scale in enumerate(lowercase_ ):
UpperCAmelCase = UperNetPyramidPoolingBlock(pool_scale=lowercase_ , in_channels=lowercase_ , channels=lowercase_ )
self.blocks.append(lowercase_ )
self.add_module(str(lowercase_ ) , lowercase_ )
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :torch.Tensor ) -> List[torch.Tensor]:
UpperCAmelCase = []
for ppm in self.blocks:
UpperCAmelCase = ppm(lowercase_ )
UpperCAmelCase = nn.functional.interpolate(
lowercase_ , size=x.size()[2:] , mode='bilinear' , align_corners=self.align_corners )
ppm_outs.append(lowercase_ )
return ppm_outs
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Dict , lowercase_ :Optional[Any] , lowercase_ :Optional[int] ) -> Any:
super().__init__()
UpperCAmelCase = config
UpperCAmelCase = config.pool_scales # e.g. (1, 2, 3, 6)
UpperCAmelCase = in_channels
UpperCAmelCase = config.hidden_size
UpperCAmelCase = False
UpperCAmelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
UpperCAmelCase = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
UpperCAmelCase = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
UpperCAmelCase = nn.ModuleList()
UpperCAmelCase = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
UpperCAmelCase = UperNetConvModule(lowercase_ , self.channels , kernel_size=1 )
UpperCAmelCase = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(lowercase_ )
self.fpn_convs.append(lowercase_ )
UpperCAmelCase = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def UpperCAmelCase__ ( self :Optional[Any] ) -> List[Any]:
self.apply(self._init_weights )
def UpperCAmelCase__ ( self :str , lowercase_ :Union[str, Any] ) -> str:
if isinstance(lowercase_ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__ ( self :Dict , lowercase_ :int ) -> int:
UpperCAmelCase = inputs[-1]
UpperCAmelCase = [x]
psp_outs.extend(self.psp_modules(lowercase_ ) )
UpperCAmelCase = torch.cat(lowercase_ , dim=1 )
UpperCAmelCase = self.bottleneck(lowercase_ )
return output
def UpperCAmelCase__ ( self :str , lowercase_ :torch.Tensor ) -> torch.Tensor:
# build laterals
UpperCAmelCase = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(lowercase_ ) )
# build top-down path
UpperCAmelCase = len(lowercase_ )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCAmelCase = laterals[i - 1].shape[2:]
UpperCAmelCase = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=lowercase_ , mode='bilinear' , align_corners=self.align_corners )
# build outputs
UpperCAmelCase = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
UpperCAmelCase = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='bilinear' , align_corners=self.align_corners )
UpperCAmelCase = torch.cat(lowercase_ , dim=1 )
UpperCAmelCase = self.fpn_bottleneck(lowercase_ )
UpperCAmelCase = self.classifier(lowercase_ )
return output
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self :Optional[Any] , lowercase_ :Optional[Any] , lowercase_ :int = 2 , lowercase_ :int = 3 , lowercase_ :Union[int, Tuple[int, int]] = 1 ) -> None:
super().__init__()
UpperCAmelCase = config
UpperCAmelCase = config.auxiliary_in_channels
UpperCAmelCase = config.auxiliary_channels
UpperCAmelCase = config.auxiliary_num_convs
UpperCAmelCase = config.auxiliary_concat_input
UpperCAmelCase = in_index
UpperCAmelCase = (kernel_size // 2) * dilation
UpperCAmelCase = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=lowercase_ , padding=lowercase_ , dilation=lowercase_ ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=lowercase_ , padding=lowercase_ , dilation=lowercase_ ) )
if self.num_convs == 0:
UpperCAmelCase = nn.Identity()
else:
UpperCAmelCase = nn.Sequential(*lowercase_ )
if self.concat_input:
UpperCAmelCase = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=lowercase_ , padding=kernel_size // 2 )
UpperCAmelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def UpperCAmelCase__ ( self :List[str] ) -> Dict:
self.apply(self._init_weights )
def UpperCAmelCase__ ( self :int , lowercase_ :Any ) -> List[Any]:
if isinstance(lowercase_ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__ ( self :Dict , lowercase_ :torch.Tensor ) -> torch.Tensor:
# just take the relevant feature maps
UpperCAmelCase = encoder_hidden_states[self.in_index]
UpperCAmelCase = self.convs(lowercase_ )
if self.concat_input:
UpperCAmelCase = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
UpperCAmelCase = self.classifier(lowercase_ )
return output
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__UpperCamelCase = UperNetConfig
__UpperCamelCase = """pixel_values"""
__UpperCamelCase = True
def UpperCAmelCase__ ( self :Tuple , lowercase_ :List[str] ) -> Union[str, Any]:
if isinstance(lowercase_ , lowercase_ ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase__ ( self :int ) -> Tuple:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase__ ( self :Any , lowercase_ :Dict , lowercase_ :Union[str, Any]=False ) -> Optional[int]:
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = value
snake_case_ = R"""
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
snake_case_ = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"""UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.""" , SCREAMING_SNAKE_CASE_ , )
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :int , lowercase_ :Tuple ) -> int:
super().__init__(lowercase_ )
UpperCAmelCase = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
UpperCAmelCase = UperNetHead(lowercase_ , in_channels=self.backbone.channels )
UpperCAmelCase = UperNetFCNHead(lowercase_ ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length' ) )
@replace_return_docstrings(output_type=lowercase_ , config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[torch.Tensor] = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[torch.Tensor] = None , lowercase_ :Optional[bool] = None , ) -> Union[tuple, SemanticSegmenterOutput]:
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase = output_attentions if output_attentions is not None else self.config.output_attentions
UpperCAmelCase = self.backbone.forward_with_filtered_kwargs(
lowercase_ , output_hidden_states=lowercase_ , output_attentions=lowercase_ )
UpperCAmelCase = outputs.feature_maps
UpperCAmelCase = self.decode_head(lowercase_ )
UpperCAmelCase = nn.functional.interpolate(lowercase_ , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=lowercase_ )
UpperCAmelCase = None
if self.auxiliary_head is not None:
UpperCAmelCase = self.auxiliary_head(lowercase_ )
UpperCAmelCase = nn.functional.interpolate(
lowercase_ , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=lowercase_ )
UpperCAmelCase = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('The number of labels should be greater than one' )
else:
# compute weighted loss
UpperCAmelCase = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
UpperCAmelCase = loss_fct(lowercase_ , lowercase_ )
UpperCAmelCase = loss_fct(lowercase_ , lowercase_ )
UpperCAmelCase = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
UpperCAmelCase = (logits,) + outputs[1:]
else:
UpperCAmelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=lowercase_ , logits=lowercase_ , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 78
|
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
SCREAMING_SNAKE_CASE__ = 2_048
SCREAMING_SNAKE_CASE__ = 4_096
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = os.environ.pop("PROCESS_TRAIN", "false")
SCREAMING_SNAKE_CASE__ = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def lowerCAmelCase__ ( _UpperCamelCase : str ) -> Any:
"""simple docstring"""
def choose_first(_UpperCamelCase : Tuple , _UpperCamelCase : List[Any]=False ):
assert isinstance(_UpperCamelCase , _UpperCamelCase )
if len(_UpperCamelCase ) == 1:
snake_case = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
snake_case = {k: [a[k]] for k in a}
if len(a['start_token'] ) > 0:
break
return a
snake_case = {'id': example['id']}
snake_case = example['annotations']
snake_case = annotation['yes_no_answer']
if 0 in yes_no_answer or 1 in yes_no_answer:
snake_case = ['yes'] if 1 in yes_no_answer else ['no']
snake_case = snake_case = []
snake_case = snake_case = []
snake_case = ['<cls>']
else:
snake_case = ['short']
snake_case = choose_first(annotation['short_answers'] )
if len(out['start_token'] ) == 0:
# answer will be long if short is not available
snake_case = ['long']
snake_case = choose_first(annotation['long_answer'] , is_long_answer=_UpperCamelCase )
snake_case = []
answer.update(_UpperCamelCase )
# disregard some samples
if len(answer['start_token'] ) > 1 or answer["start_token"] == answer["end_token"]:
snake_case = True
else:
snake_case = False
snake_case = ['start_token', 'end_token', 'start_byte', 'end_byte', 'text']
if not all(isinstance(answer[k] , _UpperCamelCase ) for k in cols ):
raise ValueError('Issue in ID' , example['id'] )
return answer
def lowerCAmelCase__ ( _UpperCamelCase : Dict , _UpperCamelCase : List[str]=False ) -> Union[str, Any]:
"""simple docstring"""
snake_case = _get_single_answer(_UpperCamelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
snake_case = example['document']['tokens']
snake_case = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
return {
"context": " ".join(_UpperCamelCase ),
"answer": {
"start_token": -1_0_0, # ignore index in cross-entropy
"end_token": -1_0_0, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
snake_case = ['start_token', 'end_token']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
snake_case = example['document']['tokens']
snake_case = answer['start_token']
snake_case = answer['end_token']
snake_case = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
snake_case = ' '.join(context[start_token:end_token] )
# checking above code
if assertion:
snake_case = doc['is_html'][answer['start_token'] : answer['end_token']]
snake_case = doc['token'][answer['start_token'] : answer['end_token']]
snake_case = ' '.join([old[i] for i in range(len(_UpperCamelCase ) ) if not is_html[i]] )
if new != old:
print('ID:' , example['id'] )
print('New:' , _UpperCamelCase , end='\n' )
print('Old:' , _UpperCamelCase , end='\n\n' )
return {
"context": " ".join(_UpperCamelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase__ ( _UpperCamelCase : Dict , _UpperCamelCase : Any , _UpperCamelCase : Optional[int]=2_0_4_8 , _UpperCamelCase : Union[str, Any]=4_0_9_6 , _UpperCamelCase : Dict=True ) -> Optional[Any]:
"""simple docstring"""
snake_case = get_context_and_ans(_UpperCamelCase , assertion=_UpperCamelCase )
snake_case = out['answer']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
snake_case = tokenizer(example['question']['text'] , out['context'] ).input_ids
snake_case = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
snake_case = []
snake_case = []
snake_case = input_ids[:q_len]
snake_case = range(_UpperCamelCase , len(_UpperCamelCase ) , max_length - doc_stride )
for i in doc_start_indices:
snake_case = i + max_length - q_len
snake_case = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer['category'][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-1_0_0] * len(_UpperCamelCase ),
"end_token": [-1_0_0] * len(_UpperCamelCase ),
"category": category,
},
}
snake_case = out['context'].split()
snake_case = splitted_context[answer['end_token']]
snake_case = len(
tokenizer(
' '.join(splitted_context[: answer['start_token']] ) , add_special_tokens=_UpperCamelCase , ).input_ids )
snake_case = len(
tokenizer(' '.join(splitted_context[: answer['end_token']] ) , add_special_tokens=_UpperCamelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
snake_case = len(tokenizer(_UpperCamelCase , add_special_tokens=_UpperCamelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
snake_case = input_ids[answer['start_token'] : answer['end_token'] + 1] # right & left are inclusive
snake_case = answer['start_token']
snake_case = answer['end_token']
if assertion:
snake_case = tokenizer.decode(_UpperCamelCase )
if answer["span"] != new:
print('ISSUE IN TOKENIZATION' )
print('OLD:' , answer['span'] )
print('NEW:' , _UpperCamelCase , end='\n\n' )
if len(_UpperCamelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
snake_case = input_ids[:q_len]
snake_case = range(_UpperCamelCase , len(_UpperCamelCase ) , max_length - doc_stride )
snake_case = []
snake_case = []
snake_case = []
snake_case = [] # null, yes, no, long, short
for i in doc_start_indices:
snake_case = i + max_length - q_len
snake_case = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
snake_case = start_token - i + q_len
snake_case = end_token - i + q_len
answers_category.append(answer['category'][0] ) # ["short"] -> "short"
else:
snake_case = -1_0_0
snake_case = -1_0_0
answers_category.append('null' )
snake_case = inputs[-1][start_token : end_token + 1]
answers_start_token.append(_UpperCamelCase )
answers_end_token.append(_UpperCamelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('ISSUE in strided for ID:' , example['id'] )
print('New:' , tokenizer.decode(_UpperCamelCase ) )
print('Old:' , tokenizer.decode(_UpperCamelCase ) , end='\n\n' )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase__ ( _UpperCamelCase : Optional[Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[int]=2_0_4_8 , _UpperCamelCase : Union[str, Any]=4_0_9_6 , _UpperCamelCase : List[str]=False ) -> Union[str, Any]:
"""simple docstring"""
snake_case = get_strided_contexts_and_ans(
_UpperCamelCase , _UpperCamelCase , doc_stride=_UpperCamelCase , max_length=_UpperCamelCase , assertion=_UpperCamelCase , )
return example
def lowerCAmelCase__ ( _UpperCamelCase : Tuple , _UpperCamelCase : List[str] ) -> Any:
"""simple docstring"""
with jsonlines.open(_UpperCamelCase , 'a' ) as writer:
for example in tqdm(_UpperCamelCase , total=len(_UpperCamelCase ) , desc='Saving samples ... ' ):
snake_case = example['labels']
for ids, start, end, cat in zip(
example['input_ids'] , labels['start_token'] , labels['end_token'] , labels['category'] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'input_ids': ids,
'start_token': start,
'end_token': end,
'category': CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
SCREAMING_SNAKE_CASE__ = load_dataset("natural_questions")
SCREAMING_SNAKE_CASE__ = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
SCREAMING_SNAKE_CASE__ = data["train" if PROCESS_TRAIN == "true" else "validation"]
SCREAMING_SNAKE_CASE__ = {
"tokenizer": tokenizer,
"doc_stride": DOC_STRIDE,
"max_length": MAX_LENGTH,
"assertion": False,
}
SCREAMING_SNAKE_CASE__ = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
SCREAMING_SNAKE_CASE__ = data.remove_columns(["annotations", "document", "id", "question"])
print(data)
np.random.seed(SEED)
SCREAMING_SNAKE_CASE__ = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
save_to_disk(data, file_name=cache_file_name)
| 150
| 0
|
import qiskit
def lowerCamelCase__ ( A__ : int , A__ : int ):
'''simple docstring'''
__lowerCamelCase = qiskit.Aer.get_backend("""aer_simulator""" )
__lowerCamelCase = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
__lowerCamelCase = qiskit.execute(A__ , A__ , shots=1000 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(A__ )
if __name__ == "__main__":
UpperCAmelCase_ = half_adder(1, 1)
print(f"""Half Adder Output Qubit Counts: {counts}""")
| 29
|
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
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Union[str, Any] = 'yolos'
def __init__( self: Dict , UpperCamelCase_: List[Any]=7_68 , UpperCamelCase_: Tuple=12 , UpperCamelCase_: int=12 , UpperCamelCase_: int=30_72 , UpperCamelCase_: List[str]="gelu" , UpperCamelCase_: Union[str, Any]=0.0 , UpperCamelCase_: int=0.0 , UpperCamelCase_: Optional[int]=0.02 , UpperCamelCase_: Dict=1E-12 , UpperCamelCase_: List[Any]=[5_12, 8_64] , UpperCamelCase_: Optional[int]=16 , UpperCamelCase_: Any=3 , UpperCamelCase_: Union[str, Any]=True , UpperCamelCase_: List[str]=1_00 , UpperCamelCase_: List[str]=True , UpperCamelCase_: Any=False , UpperCamelCase_: Optional[Any]=1 , UpperCamelCase_: Any=5 , UpperCamelCase_: Any=2 , UpperCamelCase_: Tuple=5 , UpperCamelCase_: str=2 , UpperCamelCase_: Any=0.1 , **UpperCamelCase_: Any , ):
super().__init__(**UpperCamelCase_ )
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = qkv_bias
__lowerCamelCase = num_detection_tokens
__lowerCamelCase = use_mid_position_embeddings
__lowerCamelCase = auxiliary_loss
# Hungarian matcher
__lowerCamelCase = class_cost
__lowerCamelCase = bbox_cost
__lowerCamelCase = giou_cost
# Loss coefficients
__lowerCamelCase = bbox_loss_coefficient
__lowerCamelCase = giou_loss_coefficient
__lowerCamelCase = eos_coefficient
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Tuple = version.parse('1.11')
@property
def lowerCAmelCase__ ( self: Any ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCAmelCase__ ( self: Dict ):
return 1E-4
@property
def lowerCAmelCase__ ( self: Dict ):
return 12
| 29
| 1
|
from __future__ import annotations
def UpperCAmelCase ( lowercase ): # This function is recursive
"""simple docstring"""
__lowercase = len(__SCREAMING_SNAKE_CASE )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
__lowercase = array[0]
__lowercase = False
__lowercase = 1
__lowercase = []
while not is_found and i < array_length:
if array[i] < pivot:
__lowercase = True
__lowercase = [element for element in array[i:] if element >= array[i]]
__lowercase = longest_subsequence(__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > len(__SCREAMING_SNAKE_CASE ):
__lowercase = temp_array
else:
i += 1
__lowercase = [element for element in array[1:] if element >= pivot]
__lowercase = [pivot, *longest_subsequence(__SCREAMING_SNAKE_CASE )]
if len(__SCREAMING_SNAKE_CASE ) > len(__SCREAMING_SNAKE_CASE ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 210
|
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
def lowercase__( __SCREAMING_SNAKE_CASE : Union[str, Any] ):
print('Loading config file...' )
def flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any="" , __SCREAMING_SNAKE_CASE : List[Any]="." ):
lowercase_ : List[str] = []
for k, v in d.items():
lowercase_ : Dict = parent_key + sep + k if parent_key else k
if isinstance(__SCREAMING_SNAKE_CASE , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , sep=__SCREAMING_SNAKE_CASE ).items() )
else:
items.append((new_key, v) )
return dict(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = argparse.Namespace()
with open(__SCREAMING_SNAKE_CASE , 'r' ) as yaml_file:
try:
lowercase_ : str = yaml.load(__SCREAMING_SNAKE_CASE , Loader=yaml.FullLoader )
lowercase_ : List[Any] = flatten_yaml_as_dict(__SCREAMING_SNAKE_CASE )
for k, v in flat_cfg.items():
setattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
except yaml.YAMLError as exc:
logger.error('Error while loading config file: {}. Error message: {}'.format(__SCREAMING_SNAKE_CASE , str(__SCREAMING_SNAKE_CASE ) ) )
return config
def lowercase__( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[str] ):
lowercase_ : int = MobileViTVaConfig()
lowercase_ : List[str] = False
# dataset
if task_name.startswith('imagenet1k_' ):
lowercase_ : List[Any] = 10_00
if int(task_name.strip().split('_' )[-1] ) == 3_84:
lowercase_ : str = 3_84
else:
lowercase_ : Dict = 2_56
lowercase_ : int = 'imagenet-1k-id2label.json'
elif task_name.startswith('imagenet21k_to_1k_' ):
lowercase_ : int = 2_10_00
if int(task_name.strip().split('_' )[-1] ) == 3_84:
lowercase_ : Optional[Any] = 3_84
else:
lowercase_ : Tuple = 2_56
lowercase_ : List[str] = 'imagenet-22k-id2label.json'
elif task_name.startswith('ade20k_' ):
lowercase_ : int = 1_51
lowercase_ : Optional[Any] = 5_12
lowercase_ : str = 'ade20k-id2label.json'
lowercase_ : List[Any] = True
elif task_name.startswith('voc_' ):
lowercase_ : Union[str, Any] = 21
lowercase_ : Tuple = 5_12
lowercase_ : List[str] = 'pascal-voc-id2label.json'
lowercase_ : str = True
# orig_config
lowercase_ : Optional[int] = load_orig_config_file(__SCREAMING_SNAKE_CASE )
assert getattr(__SCREAMING_SNAKE_CASE , 'model.classification.name' , -1 ) == "mobilevit_v2", "Invalid model"
lowercase_ : Optional[Any] = getattr(__SCREAMING_SNAKE_CASE , 'model.classification.mitv2.width_multiplier' , 1.0 )
assert (
getattr(__SCREAMING_SNAKE_CASE , 'model.classification.mitv2.attn_norm_layer' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
lowercase_ : Any = getattr(__SCREAMING_SNAKE_CASE , 'model.classification.activation.name' , 'swish' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
lowercase_ : Any = getattr(__SCREAMING_SNAKE_CASE , 'model.segmentation.output_stride' , 16 )
if "_deeplabv3" in task_name:
lowercase_ : Any = getattr(__SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_rates' , [12, 24, 36] )
lowercase_ : Union[str, Any] = getattr(__SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_out_channels' , 5_12 )
lowercase_ : Any = getattr(__SCREAMING_SNAKE_CASE , 'model.segmentation.deeplabv3.aspp_dropout' , 0.1 )
# id2label
lowercase_ : Optional[Any] = 'huggingface/label-files'
lowercase_ : List[Any] = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
lowercase_ : List[str] = {int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase_ : int = idalabel
lowercase_ : List[Any] = {v: k for k, v in idalabel.items()}
return config
def lowercase__( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str ):
lowercase_ : List[Any] = dct.pop(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = val
def lowercase__( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any]=False ):
if base_model:
lowercase_ : int = ''
else:
lowercase_ : str = 'mobilevitv2.'
lowercase_ : Dict = []
for k in state_dict.keys():
if k[:8] == "encoder.":
lowercase_ : Dict = k[8:]
else:
lowercase_ : Union[str, Any] = k
if ".block." in k:
lowercase_ : List[str] = k_new.replace('.block.' , '.' )
if ".conv." in k:
lowercase_ : List[Any] = k_new.replace('.conv.' , '.convolution.' )
if ".norm." in k:
lowercase_ : str = k_new.replace('.norm.' , '.normalization.' )
if "conv_1." in k:
lowercase_ : Dict = k_new.replace('conv_1.' , F'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if F'''layer_{i}.''' in k:
lowercase_ : Tuple = k_new.replace(F'''layer_{i}.''' , F'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
lowercase_ : Any = k_new.replace('.exp_1x1.' , '.expand_1x1.' )
if ".red_1x1." in k:
lowercase_ : str = k_new.replace('.red_1x1.' , '.reduce_1x1.' )
for i in [3, 4, 5]:
if F'''layer_{i}.0.''' in k:
lowercase_ : Tuple = k_new.replace(F'''layer_{i}.0.''' , F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if F'''layer_{i}.1.local_rep.0.''' in k:
lowercase_ : Any = k_new.replace(F'''layer_{i}.1.local_rep.0.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if F'''layer_{i}.1.local_rep.1.''' in k:
lowercase_ : List[Any] = k_new.replace(F'''layer_{i}.1.local_rep.1.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
lowercase_ : Dict = [0, 1]
elif i == 4:
lowercase_ : int = [0, 1, 2, 3]
elif i == 5:
lowercase_ : List[str] = [0, 1, 2]
for j in j_in:
if F'''layer_{i}.1.global_rep.{j}.''' in k:
lowercase_ : List[str] = k_new.replace(
F'''layer_{i}.1.global_rep.{j}.''' , F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
lowercase_ : int = k_new.replace(
F'''layer_{i}.1.global_rep.{j+1}.''' , F'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if F'''layer_{i}.1.conv_proj.''' in k:
lowercase_ : str = k_new.replace(F'''layer_{i}.1.conv_proj.''' , F'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
lowercase_ : Optional[Any] = k_new.replace('pre_norm_attn.0.' , 'layernorm_before.' )
if "pre_norm_attn.1." in k:
lowercase_ : Any = k_new.replace('pre_norm_attn.1.' , 'attention.' )
if "pre_norm_ffn.0." in k:
lowercase_ : List[str] = k_new.replace('pre_norm_ffn.0.' , 'layernorm_after.' )
if "pre_norm_ffn.1." in k:
lowercase_ : int = k_new.replace('pre_norm_ffn.1.' , 'ffn.conv1.' )
if "pre_norm_ffn.3." in k:
lowercase_ : str = k_new.replace('pre_norm_ffn.3.' , 'ffn.conv2.' )
if "classifier.1." in k:
lowercase_ : Union[str, Any] = k_new.replace('classifier.1.' , 'classifier.' )
if "seg_head." in k:
lowercase_ : Optional[int] = k_new.replace('seg_head.' , 'segmentation_head.' )
if ".aspp_layer." in k:
lowercase_ : Dict = k_new.replace('.aspp_layer.' , '.' )
if ".aspp_pool." in k:
lowercase_ : Dict = k_new.replace('.aspp_pool.' , '.' )
rename_keys.append((k, k_new) )
return rename_keys
def lowercase__( __SCREAMING_SNAKE_CASE : Any ):
lowercase_ : str = []
for k in state_dict.keys():
if k.startswith('seg_head.aux_head.' ):
keys_to_ignore.append(__SCREAMING_SNAKE_CASE )
for k in keys_to_ignore:
state_dict.pop(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowercase__( ):
lowercase_ : Union[str, Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
lowercase_ : Any = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def lowercase__( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] ):
lowercase_ : Tuple = get_mobilevitva_config(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# load original state_dict
lowercase_ : Tuple = torch.load(__SCREAMING_SNAKE_CASE , map_location='cpu' )
# load huggingface model
if task_name.startswith('ade20k_' ) or task_name.startswith('voc_' ):
lowercase_ : Tuple = MobileViTVaForSemanticSegmentation(__SCREAMING_SNAKE_CASE ).eval()
lowercase_ : Optional[int] = False
else:
lowercase_ : Any = MobileViTVaForImageClassification(__SCREAMING_SNAKE_CASE ).eval()
lowercase_ : int = False
# remove and rename some keys of load the original model
lowercase_ : Any = checkpoint
remove_unused_keys(__SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = create_rename_keys(__SCREAMING_SNAKE_CASE , base_model=__SCREAMING_SNAKE_CASE )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# load modified state_dict
model.load_state_dict(__SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
lowercase_ : Union[str, Any] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
lowercase_ : Any = image_processor(images=prepare_img() , return_tensors='pt' )
lowercase_ : Optional[int] = model(**__SCREAMING_SNAKE_CASE )
# verify classification model
if task_name.startswith('imagenet' ):
lowercase_ : List[str] = outputs.logits
lowercase_ : int = logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('imagenet1k_256' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
lowercase_ : Optional[int] = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
print(F'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
__SCREAMING_SNAKE_CASE =parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 213
| 0
|
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def a_ ( lowerCAmelCase_ : bytes, lowerCAmelCase_ : int ):
__lowerCAmelCase = F"""{sampling_rate}"""
__lowerCAmelCase = '1'
__lowerCAmelCase = 'f32le'
__lowerCAmelCase = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(lowerCAmelCase_, stdin=subprocess.PIPE, stdout=subprocess.PIPE ) as ffmpeg_process:
__lowerCAmelCase = ffmpeg_process.communicate(lowerCAmelCase_ )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
__lowerCAmelCase = output_stream[0]
__lowerCAmelCase = np.frombuffer(lowerCAmelCase_, np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : float, lowerCAmelCase_ : str = "f32le", ):
__lowerCAmelCase = F"""{sampling_rate}"""
__lowerCAmelCase = '1'
if format_for_conversion == "s16le":
__lowerCAmelCase = 2
elif format_for_conversion == "f32le":
__lowerCAmelCase = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
__lowerCAmelCase = platform.system()
if system == "Linux":
__lowerCAmelCase = 'alsa'
__lowerCAmelCase = 'default'
elif system == "Darwin":
__lowerCAmelCase = 'avfoundation'
__lowerCAmelCase = ':0'
elif system == "Windows":
__lowerCAmelCase = 'dshow'
__lowerCAmelCase = 'default'
__lowerCAmelCase = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
__lowerCAmelCase = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
__lowerCAmelCase = _ffmpeg_stream(lowerCAmelCase_, lowerCAmelCase_ )
for item in iterator:
yield item
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : float, lowerCAmelCase_ : Optional[int] = None, lowerCAmelCase_ : Optional[Union[Tuple[float, float], float]] = None, lowerCAmelCase_ : str = "f32le", ):
if stream_chunk_s is not None:
__lowerCAmelCase = stream_chunk_s
else:
__lowerCAmelCase = chunk_length_s
__lowerCAmelCase = ffmpeg_microphone(lowerCAmelCase_, lowerCAmelCase_, format_for_conversion=lowerCAmelCase_ )
if format_for_conversion == "s16le":
__lowerCAmelCase = np.intaa
__lowerCAmelCase = 2
elif format_for_conversion == "f32le":
__lowerCAmelCase = np.floataa
__lowerCAmelCase = 4
else:
raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )
if stride_length_s is None:
__lowerCAmelCase = chunk_length_s / 6
__lowerCAmelCase = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(lowerCAmelCase_, (int, float) ):
__lowerCAmelCase = [stride_length_s, stride_length_s]
__lowerCAmelCase = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
__lowerCAmelCase = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
__lowerCAmelCase = datetime.datetime.now()
__lowerCAmelCase = datetime.timedelta(seconds=lowerCAmelCase_ )
for item in chunk_bytes_iter(lowerCAmelCase_, lowerCAmelCase_, stride=(stride_left, stride_right), stream=lowerCAmelCase_ ):
# Put everything back in numpy scale
__lowerCAmelCase = np.frombuffer(item['raw'], dtype=lowerCAmelCase_ )
__lowerCAmelCase = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
__lowerCAmelCase = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def a_ ( lowerCAmelCase_ : Optional[int], lowerCAmelCase_ : int, lowerCAmelCase_ : Tuple[int, int], lowerCAmelCase_ : bool = False ):
__lowerCAmelCase = B''
__lowerCAmelCase , __lowerCAmelCase = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}""" )
__lowerCAmelCase = 0
for raw in iterator:
acc += raw
if stream and len(lowerCAmelCase_ ) < chunk_len:
__lowerCAmelCase = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(lowerCAmelCase_ ) >= chunk_len:
# We are flushing the accumulator
__lowerCAmelCase = (_stride_left, stride_right)
__lowerCAmelCase = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
__lowerCAmelCase = False
yield item
__lowerCAmelCase = stride_left
__lowerCAmelCase = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(lowerCAmelCase_ ) > stride_left:
__lowerCAmelCase = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
__lowerCAmelCase = False
yield item
def a_ ( lowerCAmelCase_ : List[str], lowerCAmelCase_ : int ):
__lowerCAmelCase = 2**24 # 16Mo
try:
with subprocess.Popen(lowerCAmelCase_, stdout=subprocess.PIPE, bufsize=lowerCAmelCase_ ) as ffmpeg_process:
while True:
__lowerCAmelCase = ffmpeg_process.stdout.read(lowerCAmelCase_ )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to stream audio files from filename' ) from error
| 207
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : Optional[Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : int = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : Tuple = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : List[str] = [
'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
_snake_case : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 207
| 1
|
from math import ceil
def lowerCAmelCase_ ( __A, __A ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ = list(range(0, __A ) )
UpperCAmelCase__ = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
UpperCAmelCase__ = []
for i in device_map_blocks:
if device_map_blocks.count(__A ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(__A )
# Missing blocks
UpperCAmelCase__ = [i for i in blocks if i not in device_map_blocks]
UpperCAmelCase__ = [i for i in device_map_blocks if i not in blocks]
if len(__A ) != 0:
raise ValueError(
"Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
" These attention blocks were specified more than once: " + str(__A ) )
if len(__A ) != 0:
raise ValueError(
"There are attention blocks for this model that are not specified in the device_map. Add these attention "
"blocks to a device on the device_map: " + str(__A ) )
if len(__A ) != 0:
raise ValueError(
"The device_map contains more attention blocks than this model has. Remove these from the device_map:"
+ str(__A ) )
def lowerCAmelCase_ ( __A, __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ = list(range(__A ) )
UpperCAmelCase__ = int(ceil(n_layers / len(__A ) ) )
UpperCAmelCase__ = [layers[i : i + n_blocks] for i in range(0, __A, __A )]
return dict(zip(__A, __A ) )
| 65
|
def _UpperCAmelCase ( snake_case = 50 ):
"""simple docstring"""
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"{solution() = }")
| 82
| 0
|
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a ( lowerCAmelCase__ ):
def snake_case_ ( self , a__ ):
with open(a__ , encoding='utf-8' ) as input_file:
_lowerCamelCase = re.compile(r'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)' )
_lowerCamelCase = input_file.read()
_lowerCamelCase = regexp.search(a__ )
return match
def snake_case_ ( self , a__ ):
with open(a__ , encoding='utf-8' ) as input_file:
_lowerCamelCase = re.compile(r'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()' , re.DOTALL )
_lowerCamelCase = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
_lowerCamelCase = regexp.finditer(a__ )
_lowerCamelCase = [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 snake_case_ ( self ):
_lowerCamelCase = Path('./datasets' )
_lowerCamelCase = list(dataset_paths.absolute().glob('**/*.py' ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(a__ ) ):
raise AssertionError(F'open(...) must use utf-8 encoding in {dataset}' )
def snake_case_ ( self ):
_lowerCamelCase = Path('./datasets' )
_lowerCamelCase = list(dataset_paths.absolute().glob('**/*.py' ) )
for dataset in dataset_files:
if self._no_print_statements(str(a__ ) ):
raise AssertionError(F'print statement found in {dataset}. Use datasets.logger/logging instead.' )
| 80
|
"""simple docstring"""
import numpy as np
def SCREAMING_SNAKE_CASE_ ( snake_case : np.ndarray )-> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE_ ( snake_case : np.ndarray )-> np.ndarray:
return vector * sigmoid(snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80
| 1
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Dict:
A = tempfile.mkdtemp()
A = 8
# DPR tok
A = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
A = os.path.join(self.tmpdirname ,'dpr_tokenizer' )
os.makedirs(A_ ,exist_ok=A_ )
A = os.path.join(A_ ,DPR_VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
# BART tok
A = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
A = dict(zip(A_ ,range(len(A_ ) ) ) )
A = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
A = {'unk_token': '<unk>'}
A = os.path.join(self.tmpdirname ,'bart_tokenizer' )
os.makedirs(A_ ,exist_ok=A_ )
A = os.path.join(A_ ,BART_VOCAB_FILES_NAMES['vocab_file'] )
A = os.path.join(A_ ,BART_VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file ,'w' ,encoding='utf-8' ) as fp:
fp.write(json.dumps(A_ ) + '\n' )
with open(self.merges_file ,'w' ,encoding='utf-8' ) as fp:
fp.write('\n'.join(A_ ) )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,'dpr_tokenizer' ) )
def _SCREAMING_SNAKE_CASE ( self : int ) -> BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,'bart_tokenizer' ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def _SCREAMING_SNAKE_CASE ( self : int ) -> List[str]:
A = os.path.join(self.tmpdirname ,'rag_tokenizer' )
A = RagConfig(question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() )
A = RagTokenizer(question_encoder=self.get_dpr_tokenizer() ,generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(A_ )
rag_tokenizer.save_pretrained(A_ )
A = RagTokenizer.from_pretrained(A_ ,config=A_ )
self.assertIsInstance(new_rag_tokenizer.question_encoder ,A_ )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() ,rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator ,A_ )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() ,rag_tokenizer.generator.get_vocab() )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
A = RagTokenizer.from_pretrained('facebook/rag-token-nq' )
A = [
'who got the first nobel prize in physics',
'when is the next deadpool movie being released',
'which mode is used for short wave broadcast service',
'who is the owner of reading football club',
'when is the next scandal episode coming out',
'when is the last time the philadelphia won the superbowl',
'what is the most current adobe flash player version',
'how many episodes are there in dragon ball z',
'what is the first step in the evolution of the eye',
'where is gall bladder situated in human body',
'what is the main mineral in lithium batteries',
'who is the president of usa right now',
'where do the greasers live in the outsiders',
'panda is a national animal of which country',
'what is the name of manchester united stadium',
]
A = tokenizer(A_ )
self.assertIsNotNone(A_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : int ) -> Optional[int]:
A = RagTokenizer.from_pretrained('facebook/rag-sequence-nq' )
A = [
'who got the first nobel prize in physics',
'when is the next deadpool movie being released',
'which mode is used for short wave broadcast service',
'who is the owner of reading football club',
'when is the next scandal episode coming out',
'when is the last time the philadelphia won the superbowl',
'what is the most current adobe flash player version',
'how many episodes are there in dragon ball z',
'what is the first step in the evolution of the eye',
'where is gall bladder situated in human body',
'what is the main mineral in lithium batteries',
'who is the president of usa right now',
'where do the greasers live in the outsiders',
'panda is a national animal of which country',
'what is the name of manchester united stadium',
]
A = tokenizer(A_ )
self.assertIsNotNone(A_ )
| 74
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = DDIMPipeline
_a = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
_a = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
_a = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
_a = False
def snake_case ( self : str )-> Optional[Any]:
torch.manual_seed(0 )
lowerCamelCase__ : Union[str, Any] =UNetaDModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''), up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''), )
lowerCamelCase__ : Optional[Any] =DDIMScheduler()
lowerCamelCase__ : List[Any] ={'''unet''': unet, '''scheduler''': scheduler}
return components
def snake_case ( self : Optional[Any], lowerCamelCase : Dict, lowerCamelCase : Optional[Any]=0 )-> Optional[int]:
if str(lowerCamelCase ).startswith('''mps''' ):
lowerCamelCase__ : Dict =torch.manual_seed(lowerCamelCase )
else:
lowerCamelCase__ : Optional[int] =torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
lowerCamelCase__ : Tuple ={
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def snake_case ( self : Dict )-> str:
lowerCamelCase__ : Optional[Any] ='''cpu'''
lowerCamelCase__ : int =self.get_dummy_components()
lowerCamelCase__ : Optional[int] =self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : List[str] =self.get_dummy_inputs(lowerCamelCase )
lowerCamelCase__ : Any =pipe(**lowerCamelCase ).images
lowerCamelCase__ : Optional[Any] =image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 32, 32, 3) )
lowerCamelCase__ : Tuple =np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
lowerCamelCase__ : str =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase, 1E-3 )
def snake_case ( self : Union[str, Any] )-> List[Any]:
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def snake_case ( self : Union[str, Any] )-> int:
super().test_save_load_local(expected_max_difference=3E-3 )
def snake_case ( self : List[Any] )-> List[Any]:
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def snake_case ( self : Optional[Any] )-> Optional[int]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self : Optional[Any] )-> List[str]:
lowerCamelCase__ : Optional[Any] ='''google/ddpm-cifar10-32'''
lowerCamelCase__ : Union[str, Any] =UNetaDModel.from_pretrained(lowerCamelCase )
lowerCamelCase__ : Optional[int] =DDIMScheduler()
lowerCamelCase__ : int =DDIMPipeline(unet=lowerCamelCase, scheduler=lowerCamelCase )
ddim.to(lowerCamelCase )
ddim.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : Tuple =torch.manual_seed(0 )
lowerCamelCase__ : int =ddim(generator=lowerCamelCase, eta=0.0, output_type='''numpy''' ).images
lowerCamelCase__ : Tuple =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase__ : Any =np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self : Optional[int] )-> Any:
lowerCamelCase__ : str ='''google/ddpm-ema-bedroom-256'''
lowerCamelCase__ : Optional[int] =UNetaDModel.from_pretrained(lowerCamelCase )
lowerCamelCase__ : Any =DDIMScheduler.from_pretrained(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =DDIMPipeline(unet=lowerCamelCase, scheduler=lowerCamelCase )
ddpm.to(lowerCamelCase )
ddpm.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : List[str] =torch.manual_seed(0 )
lowerCamelCase__ : Optional[Any] =ddpm(generator=lowerCamelCase, output_type='''numpy''' ).images
lowerCamelCase__ : Any =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowerCamelCase__ : Any =np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 238
| 0
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCamelCase : Optional[Any] =logging.get_logger(__name__)
# General docstring
lowerCamelCase : Optional[Any] ='''RegNetConfig'''
# Base docstring
lowerCamelCase : List[str] ='''facebook/regnet-y-040'''
lowerCamelCase : Any =[1, 1088, 7, 7]
# Image classification docstring
lowerCamelCase : Any ='''facebook/regnet-y-040'''
lowerCamelCase : Dict ='''tabby, tabby cat'''
lowerCamelCase : List[Any] =[
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __a ( nn.Module ):
def __init__( self : Dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int = 3 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : Optional[str] = "relu" , ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : str = nn.Convad(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , padding=kernel_size // 2 , groups=SCREAMING_SNAKE_CASE , bias=SCREAMING_SNAKE_CASE , )
UpperCamelCase__ : Optional[Any] = nn.BatchNormad(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def __lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.convolution(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = self.normalization(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = self.activation(SCREAMING_SNAKE_CASE )
return hidden_state
class __a ( nn.Module ):
def __init__( self : Dict , SCREAMING_SNAKE_CASE : RegNetConfig ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : Union[str, Any] = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
UpperCamelCase__ : Union[str, Any] = config.num_channels
def __lowercase ( self : int , SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ : Any = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
UpperCamelCase__ : Dict = self.embedder(SCREAMING_SNAKE_CASE )
return hidden_state
class __a ( nn.Module ):
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int = 2 ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : List[Any] = nn.Convad(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=1 , stride=SCREAMING_SNAKE_CASE , bias=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = nn.BatchNormad(SCREAMING_SNAKE_CASE )
def __lowercase ( self : Any , SCREAMING_SNAKE_CASE : Tensor ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.convolution(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = self.normalization(SCREAMING_SNAKE_CASE )
return hidden_state
class __a ( nn.Module ):
def __init__( self : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : Any = nn.AdaptiveAvgPoolad((1, 1) )
UpperCamelCase__ : Dict = nn.Sequential(
nn.Convad(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=1 ) , nn.ReLU() , nn.Convad(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=1 ) , nn.Sigmoid() , )
def __lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = self.pooler(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = self.attention(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = hidden_state * attention
return hidden_state
class __a ( nn.Module ):
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : RegNetConfig , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int = 1 ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : Tuple = in_channels != out_channels or stride != 1
UpperCamelCase__ : str = max(1 , out_channels // config.groups_width )
UpperCamelCase__ : Union[str, Any] = (
RegNetShortCut(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE ) if should_apply_shortcut else nn.Identity()
)
UpperCamelCase__ : str = nn.Sequential(
RegNetConvLayer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , groups=SCREAMING_SNAKE_CASE , activation=config.hidden_act ) , RegNetConvLayer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=1 , activation=SCREAMING_SNAKE_CASE ) , )
UpperCamelCase__ : List[str] = ACTaFN[config.hidden_act]
def __lowercase ( self : Dict , SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = hidden_state
UpperCamelCase__ : List[str] = self.layer(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = self.shortcut(SCREAMING_SNAKE_CASE )
hidden_state += residual
UpperCamelCase__ : Dict = self.activation(SCREAMING_SNAKE_CASE )
return hidden_state
class __a ( nn.Module ):
def __init__( self : int , SCREAMING_SNAKE_CASE : RegNetConfig , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int = 1 ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : Tuple = in_channels != out_channels or stride != 1
UpperCamelCase__ : List[Any] = max(1 , out_channels // config.groups_width )
UpperCamelCase__ : Optional[int] = (
RegNetShortCut(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE ) if should_apply_shortcut else nn.Identity()
)
UpperCamelCase__ : Optional[int] = nn.Sequential(
RegNetConvLayer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , groups=SCREAMING_SNAKE_CASE , activation=config.hidden_act ) , RegNetSELayer(SCREAMING_SNAKE_CASE , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , kernel_size=1 , activation=SCREAMING_SNAKE_CASE ) , )
UpperCamelCase__ : Optional[Any] = ACTaFN[config.hidden_act]
def __lowercase ( self : Tuple , SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
UpperCamelCase__ : Tuple = hidden_state
UpperCamelCase__ : List[str] = self.layer(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = self.shortcut(SCREAMING_SNAKE_CASE )
hidden_state += residual
UpperCamelCase__ : Tuple = self.activation(SCREAMING_SNAKE_CASE )
return hidden_state
class __a ( nn.Module ):
def __init__( self : int , SCREAMING_SNAKE_CASE : RegNetConfig , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int = 2 , SCREAMING_SNAKE_CASE : int = 2 , ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : Any = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
UpperCamelCase__ : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , ) , *[layer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for _ in range(depth - 1 )] , )
def __lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ : int = self.layers(SCREAMING_SNAKE_CASE )
return hidden_state
class __a ( nn.Module ):
def __init__( self : List[str] , SCREAMING_SNAKE_CASE : RegNetConfig ):
'''simple docstring'''
super().__init__()
UpperCamelCase__ : Optional[Any] = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
SCREAMING_SNAKE_CASE , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
UpperCamelCase__ : str = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE , config.depths[1:] ):
self.stages.append(RegNetStage(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , depth=SCREAMING_SNAKE_CASE ) )
def __lowercase ( self : Dict , SCREAMING_SNAKE_CASE : Tensor , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = True ):
'''simple docstring'''
UpperCamelCase__ : Tuple = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
UpperCamelCase__ : Union[str, Any] = hidden_states + (hidden_state,)
UpperCamelCase__ : Optional[Any] = stage_module(SCREAMING_SNAKE_CASE )
if output_hidden_states:
UpperCamelCase__ : int = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE , hidden_states=SCREAMING_SNAKE_CASE )
class __a ( A__ ):
_lowerCAmelCase : Optional[Any] = RegNetConfig
_lowerCAmelCase : List[str] = '''regnet'''
_lowerCAmelCase : int = '''pixel_values'''
_lowerCAmelCase : List[str] = True
def __lowercase ( self : List[str] , SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(SCREAMING_SNAKE_CASE , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def __lowercase ( self : Dict , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Union[str, Any]=False ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : List[Any] = value
lowerCamelCase : Optional[int] =R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
lowerCamelCase : List[Any] =R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
'''The bare RegNet model outputting raw features without any specific head on top.''' , A__ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __a ( A__ ):
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = config
UpperCamelCase__ : str = RegNetEmbeddings(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[Any] = RegNetEncoder(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[Any] = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __lowercase ( self : str , SCREAMING_SNAKE_CASE : Tensor , SCREAMING_SNAKE_CASE : Optional[bool] = None , SCREAMING_SNAKE_CASE : Optional[bool] = None ):
'''simple docstring'''
UpperCamelCase__ : List[str] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCamelCase__ : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
UpperCamelCase__ : Union[str, Any] = self.embedder(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = self.encoder(
SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE , return_dict=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = encoder_outputs[0]
UpperCamelCase__ : Optional[Any] = self.pooler(SCREAMING_SNAKE_CASE )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE , pooler_output=SCREAMING_SNAKE_CASE , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'''
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , A__ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __a ( A__ ):
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[Any] = config.num_labels
UpperCamelCase__ : Dict = RegNetModel(SCREAMING_SNAKE_CASE )
# classification head
UpperCamelCase__ : Dict = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __lowercase ( self : str , SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , SCREAMING_SNAKE_CASE : Optional[torch.LongTensor] = None , SCREAMING_SNAKE_CASE : Optional[bool] = None , SCREAMING_SNAKE_CASE : Optional[bool] = None , ):
'''simple docstring'''
UpperCamelCase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
UpperCamelCase__ : Tuple = self.regnet(SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE , return_dict=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = outputs.pooler_output if return_dict else outputs[1]
UpperCamelCase__ : Any = self.classifier(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCamelCase__ : List[str] = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCamelCase__ : Any = "single_label_classification"
else:
UpperCamelCase__ : List[str] = "multi_label_classification"
if self.config.problem_type == "regression":
UpperCamelCase__ : Union[str, Any] = MSELoss()
if self.num_labels == 1:
UpperCamelCase__ : Dict = loss_fct(logits.squeeze() , labels.squeeze() )
else:
UpperCamelCase__ : str = loss_fct(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
elif self.config.problem_type == "single_label_classification":
UpperCamelCase__ : List[str] = CrossEntropyLoss()
UpperCamelCase__ : List[str] = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCamelCase__ : Dict = BCEWithLogitsLoss()
UpperCamelCase__ : List[str] = loss_fct(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if not return_dict:
UpperCamelCase__ : Any = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE , logits=SCREAMING_SNAKE_CASE , hidden_states=outputs.hidden_states )
| 350
|
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCamelCase__ : int = (boundary[1] - boundary[0]) / steps
UpperCamelCase__ : Optional[Any] = boundary[0]
UpperCamelCase__ : List[Any] = boundary[1]
UpperCamelCase__ : List[Any] = make_points(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
UpperCamelCase__ : int = 0.0
y += (h / 2.0) * f(__lowerCAmelCase )
for i in x_i:
# print(i)
y += h * f(__lowerCAmelCase )
y += (h / 2.0) * f(__lowerCAmelCase )
return y
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
UpperCamelCase__ : Optional[int] = a + h
while x < (b - h):
yield x
UpperCamelCase__ : Union[str, Any] = x + h
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Any: # enter your function here
UpperCamelCase__ : Dict = (x - 0) * (x - 0)
return y
def SCREAMING_SNAKE_CASE ( ) -> Dict:
UpperCamelCase__ : List[Any] = 0.0 # Lower bound of integration
UpperCamelCase__ : Tuple = 1.0 # Upper bound of integration
UpperCamelCase__ : Any = 1_0.0 # define number of steps or resolution
UpperCamelCase__ : List[str] = [a, b] # define boundary of integration
UpperCamelCase__ : Any = method_a(__lowerCAmelCase , __lowerCAmelCase )
print(f'y = {y}' )
if __name__ == "__main__":
main()
| 196
| 0
|
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase=() , _UpperCamelCase=None , _UpperCamelCase="no" , _UpperCamelCase="29500" ):
'''simple docstring'''
__lowerCAmelCase = False
__lowerCAmelCase = False
if any(key.startswith("KAGGLE" ) for key in os.environ.keys() ):
__lowerCAmelCase = True
elif "IPython" in sys.modules:
__lowerCAmelCase = "google.colab" in str(sys.modules["IPython"].get_ipython() )
try:
__lowerCAmelCase = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}." )
if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME" , _UpperCamelCase ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
"your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`." )
if num_processes is None:
__lowerCAmelCase = 8
__lowerCAmelCase = PrepareForLaunch(_UpperCamelCase , distributed_type="TPU" )
print(f"Launching a training on {num_processes} TPU cores." )
xmp.spawn(_UpperCamelCase , args=_UpperCamelCase , nprocs=_UpperCamelCase , start_method="fork" )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print("Launching training on one GPU." )
else:
print("Launching training on one CPU." )
function(*_UpperCamelCase )
else:
if num_processes is None:
raise ValueError(
"You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call." )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
"inside your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`." )
if torch.cuda.is_initialized():
raise ValueError(
"To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
"using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
"function." )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=_UpperCamelCase , master_addr="127.0.01" , master_port=_UpperCamelCase , mixed_precision=_UpperCamelCase ):
__lowerCAmelCase = PrepareForLaunch(_UpperCamelCase , distributed_type="MULTI_GPU" )
print(f"Launching training on {num_processes} GPUs." )
try:
start_processes(_UpperCamelCase , args=_UpperCamelCase , nprocs=_UpperCamelCase , start_method="fork" )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
"CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
"This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
"Please review your imports and test them when running the `notebook_launcher()` to identify "
"which one is problematic." ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
__lowerCAmelCase = "1"
print("Launching training on MPS." )
elif torch.cuda.is_available():
print("Launching training on one GPU." )
else:
print("Launching training on CPU." )
function(*_UpperCamelCase )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase=() , _UpperCamelCase=2 ):
'''simple docstring'''
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=_UpperCamelCase , master_addr="127.0.01" , master_port="29500" , accelerate_mixed_precision="no" , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu="yes" , ):
__lowerCAmelCase = PrepareForLaunch(_UpperCamelCase , debug=_UpperCamelCase )
start_processes(_UpperCamelCase , args=_UpperCamelCase , nprocs=_UpperCamelCase , start_method="fork" )
| 57
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Tuple = StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
__snake_case : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('''sample_euler''' )
__snake_case : Tuple = '''A painting of a squirrel eating a burger'''
__snake_case : str = torch.manual_seed(0 )
__snake_case : int = sd_pipe([prompt] , generator=a_ , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
__snake_case : List[str] = output.images
__snake_case : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Dict = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__snake_case : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('''sample_euler''' )
__snake_case : str = '''A painting of a squirrel eating a burger'''
__snake_case : List[str] = torch.manual_seed(0 )
__snake_case : Dict = sd_pipe([prompt] , generator=a_ , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
__snake_case : Any = output.images
__snake_case : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Tuple = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Any = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__snake_case : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('''sample_dpmpp_2m''' )
__snake_case : int = '''A painting of a squirrel eating a burger'''
__snake_case : Optional[Any] = torch.manual_seed(0 )
__snake_case : Dict = sd_pipe(
[prompt] , generator=a_ , guidance_scale=7.5 , num_inference_steps=15 , output_type='''np''' , use_karras_sigmas=a_ , )
__snake_case : Optional[int] = output.images
__snake_case : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
__snake_case : Any = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 102
| 0
|
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict=13 , _UpperCAmelCase : Dict=2 , _UpperCAmelCase : Optional[Any]=24 , _UpperCAmelCase : int=16 , _UpperCAmelCase : Any=True , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Any=32 , _UpperCAmelCase : str=5 , _UpperCAmelCase : str=4 , _UpperCAmelCase : Any=37 , _UpperCAmelCase : Optional[int]="gelu" , _UpperCAmelCase : List[str]=0.1 , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : Optional[int]=10 , _UpperCAmelCase : List[str]=0.02 , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Optional[int]=2 , _UpperCAmelCase : Union[str, Any]=2 , ):
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = patch_size
UpperCAmelCase__ = max_length
UpperCAmelCase__ = num_mel_bins
UpperCAmelCase__ = is_training
UpperCAmelCase__ = use_labels
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = type_sequence_label_size
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = scope
UpperCAmelCase__ = frequency_stride
UpperCAmelCase__ = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
UpperCAmelCase__ = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
UpperCAmelCase__ = (self.max_length - self.patch_size) // self.time_stride + 1
UpperCAmelCase__ = frequency_out_dimension * time_out_dimension
UpperCAmelCase__ = num_patches + 2
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
UpperCAmelCase__ = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = self.get_config()
return config, input_values, labels
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , 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=_UpperCAmelCase , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = ASTModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
UpperCAmelCase__ = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = config_and_inputs
UpperCAmelCase__ = {"""input_values""": input_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ : Dict = (
{"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
if is_torch_available()
else {}
)
lowerCAmelCase_ : Tuple = False
lowerCAmelCase_ : int = False
lowerCAmelCase_ : Any = False
lowerCAmelCase_ : str = False
def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[str] ):
"""simple docstring"""
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
UpperCAmelCase__ = ASTModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""AST does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ = model_class(_UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_UpperCAmelCase , nn.Linear ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ = model_class(_UpperCAmelCase )
UpperCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ = [*signature.parameters.keys()]
UpperCAmelCase__ = ["""input_values"""]
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ = ASTModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" )
UpperCAmelCase__ , UpperCAmelCase__ = torchaudio.load(SCREAMING_SNAKE_CASE__ )
return audio, sampling_rate
@require_torch
@require_torchaudio
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
return (
ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" )
if is_torchaudio_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = self.default_feature_extractor
UpperCAmelCase__ = ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" ).to(_UpperCAmelCase )
UpperCAmelCase__ = self.default_feature_extractor
UpperCAmelCase__ , UpperCAmelCase__ = prepare_audio()
UpperCAmelCase__ = audio.squeeze().numpy()
UpperCAmelCase__ = feature_extractor(_UpperCAmelCase , sampling_rate=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
UpperCAmelCase__ = model(**_UpperCAmelCase )
# verify the logits
UpperCAmelCase__ = torch.Size((1, 5_27) )
self.assertEqual(outputs.logits.shape , _UpperCAmelCase )
UpperCAmelCase__ = torch.tensor([-0.8760, -7.0042, -8.6602] ).to(_UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) )
| 61
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_xmod': [
'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XmodConfig',
'XmodOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
'XmodForCausalLM',
'XmodForMaskedLM',
'XmodForMultipleChoice',
'XmodForQuestionAnswering',
'XmodForSequenceClassification',
'XmodForTokenClassification',
'XmodModel',
'XmodPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 61
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a__ : List[str] ={
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple =[
'''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AdaptiveEmbedding''',
'''TransfoXLForSequenceClassification''',
'''TransfoXLLMHeadModel''',
'''TransfoXLModel''',
'''TransfoXLPreTrainedModel''',
'''load_tf_weights_in_transfo_xl''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple =[
'''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFAdaptiveEmbedding''',
'''TFTransfoXLForSequenceClassification''',
'''TFTransfoXLLMHeadModel''',
'''TFTransfoXLMainLayer''',
'''TFTransfoXLModel''',
'''TFTransfoXLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
a__ : List[str] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 53
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
pass
class snake_case :
"""simple docstring"""
def __init__( self : List[Any] , __A : Any ):
__UpperCamelCase = data
__UpperCamelCase = None
def __iter__( self : Optional[Any] ):
__UpperCamelCase = self
__UpperCamelCase = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(__A )
yield node.data
__UpperCamelCase = node.next_node
@property
def _lowerCamelCase ( self : List[str] ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
a__ : Dict =Node(1)
a__ : Optional[int] =Node(2)
a__ : List[str] =Node(3)
a__ : Optional[int] =Node(4)
print(root_node.has_loop) # False
a__ : str =root_node.next_node
print(root_node.has_loop) # True
a__ : Optional[int] =Node(5)
a__ : List[Any] =Node(6)
a__ : int =Node(5)
a__ : Tuple =Node(6)
print(root_node.has_loop) # False
a__ : str =Node(1)
print(root_node.has_loop) # False
| 53
| 1
|
'''simple docstring'''
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 a ( unittest.TestCase ):
def __init__( self , __magic_name__ , __magic_name__=1_00 , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.0_2 , __magic_name__=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 ) -> Union[str, Any]:
_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=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
return config, pixel_values, labels
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
_a = FlaxBeitModel(config=SCREAMING_SNAKE_CASE_ )
_a = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
_a = FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_ )
_a = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
_a = self.type_sequence_label_size
_a = FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE_ )
_a = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_a = 1
_a = FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE_ )
_a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_a = model(SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
_a = self.prepare_config_and_inputs()
(
_a
) = config_and_inputs
_a = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class a ( _lowerCAmelCase , unittest.TestCase ):
_lowerCAmelCase = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def __UpperCAmelCase ( self ) -> None:
_a = FlaxBeitModelTester(self )
_a = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def __UpperCAmelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self ) -> Union[str, Any]:
_a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(SCREAMING_SNAKE_CASE_ )
_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] , SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self ) -> Optional[Any]:
_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(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_a = model_class(SCREAMING_SNAKE_CASE_ )
@jax.jit
def model_jitted(__magic_name__ , **__magic_name__ ):
return model(pixel_values=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
with self.subTest('JIT Enabled' ):
_a = model_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
_a = model_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(jitted_output.shape , output.shape )
def __UpperCAmelCase ( self ) -> Any:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self ) -> Dict:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def __UpperCAmelCase ( self ) -> int:
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, 2_24, 2_24) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def _A () -> Any:
'''simple docstring'''
_a = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@require_flax
class a ( unittest.TestCase ):
@cached_property
def __UpperCAmelCase ( self ) -> Optional[int]:
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def __UpperCAmelCase ( self ) -> int:
_a = FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' )
_a = self.default_image_processor
_a = prepare_img()
_a = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='np' ).pixel_values
# prepare bool_masked_pos
_a = np.ones((1, 1_96) , dtype=SCREAMING_SNAKE_CASE_ )
# forward pass
_a = model(pixel_values=SCREAMING_SNAKE_CASE_ , bool_masked_pos=SCREAMING_SNAKE_CASE_ )
_a = outputs.logits
# verify the logits
_a = (1, 1_96, 81_92)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE_ )
_a = np.array(
[[-3.2_4_3_7, 0.5_0_7_2, -13.91_74], [-3.2_4_5_6, 0.4_9_4_8, -13.94_01], [-3.2_0_3_3, 0.5_1_2_1, -13.85_50]] )
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] , SCREAMING_SNAKE_CASE_ , 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=SCREAMING_SNAKE_CASE_ , return_tensors='np' )
# forward pass
_a = model(**SCREAMING_SNAKE_CASE_ )
_a = outputs.logits
# verify the logits
_a = (1, 10_00)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE_ )
_a = np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
_a = 2_81
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE_ )
@slow
def __UpperCAmelCase ( self ) -> Optional[Any]:
_a = FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' )
_a = self.default_image_processor
_a = prepare_img()
_a = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='np' )
# forward pass
_a = model(**SCREAMING_SNAKE_CASE_ )
_a = outputs.logits
# verify the logits
_a = (1, 2_18_41)
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE_ )
_a = np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1] )
self.assertTrue(np.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
_a = 23_96
self.assertEqual(logits.argmax(-1 ).item() , SCREAMING_SNAKE_CASE_ )
| 366
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : Optional[int] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Dict = ["DeiTFeatureExtractor"]
a_ : List[Any] = ["DeiTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Tuple = [
"DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DeiTForImageClassification",
"DeiTForImageClassificationWithTeacher",
"DeiTForMaskedImageModeling",
"DeiTModel",
"DeiTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = [
"TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDeiTForImageClassification",
"TFDeiTForImageClassificationWithTeacher",
"TFDeiTForMaskedImageModeling",
"TFDeiTModel",
"TFDeiTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
a_ : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 104
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : str ="efficientnet"
def __init__( self : Any , a : int = 3 , a : int = 6_00 , a : float = 2.0 , a : float = 3.1 , a : int = 8 , a : List[int] = [3, 3, 5, 3, 5, 5, 3] , a : List[int] = [32, 16, 24, 40, 80, 1_12, 1_92] , a : List[int] = [16, 24, 40, 80, 1_12, 1_92, 3_20] , a : List[int] = [] , a : List[int] = [1, 2, 2, 2, 1, 2, 1] , a : List[int] = [1, 2, 2, 3, 3, 4, 1] , a : List[int] = [1, 6, 6, 6, 6, 6, 6] , a : float = 0.25 , a : str = "swish" , a : int = 25_60 , a : str = "mean" , a : float = 0.02 , a : float = 0.0_01 , a : float = 0.99 , a : float = 0.5 , a : float = 0.2 , **a : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**a )
__lowerCamelCase = num_channels
__lowerCamelCase = image_size
__lowerCamelCase = width_coefficient
__lowerCamelCase = depth_coefficient
__lowerCamelCase = depth_divisor
__lowerCamelCase = kernel_sizes
__lowerCamelCase = in_channels
__lowerCamelCase = out_channels
__lowerCamelCase = depthwise_padding
__lowerCamelCase = strides
__lowerCamelCase = num_block_repeats
__lowerCamelCase = expand_ratios
__lowerCamelCase = squeeze_expansion_ratio
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dim
__lowerCamelCase = pooling_type
__lowerCamelCase = initializer_range
__lowerCamelCase = batch_norm_eps
__lowerCamelCase = batch_norm_momentum
__lowerCamelCase = dropout_rate
__lowerCamelCase = drop_connect_rate
__lowerCamelCase = sum(a ) * 4
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : int =version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
return 1e-5
| 67
|
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> Optional[Any]:
__lowerCamelCase = []
__lowerCamelCase = set({'''(''', '''[''', '''{'''} )
__lowerCamelCase = set({''')''', ''']''', '''}'''} )
__lowerCamelCase = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(UpperCamelCase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(UpperCamelCase__ ) == 0 or (len(UpperCamelCase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(UpperCamelCase__ ) == 0
def __lowerCAmelCase ( ) -> str:
__lowerCamelCase = input('''Enter sequence of brackets: ''' )
if is_balanced(UpperCamelCase__ ):
print(UpperCamelCase__ , '''is balanced''' )
else:
print(UpperCamelCase__ , '''is not balanced''' )
if __name__ == "__main__":
main()
| 67
| 1
|
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
lowerCAmelCase = imread(R'digital_image_processing/image_data/lena_small.jpg')
lowerCAmelCase = cvtColor(img, COLOR_BGR2GRAY)
def _a ( ):
"""simple docstring"""
lowercase__ = cn.convert_to_negative(SCREAMING_SNAKE_CASE )
# assert negative_img array for at least one True
assert negative_img.any()
def _a ( ):
"""simple docstring"""
with Image.open('''digital_image_processing/image_data/lena_small.jpg''' ) as img:
# Work around assertion for response
assert str(cc.change_contrast(SCREAMING_SNAKE_CASE , 1_10 ) ).startswith(
'''<PIL.Image.Image image mode=RGB size=100x100 at''' )
def _a ( ):
"""simple docstring"""
lowercase__ = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def _a ( ):
"""simple docstring"""
lowercase__ = imread('''digital_image_processing/image_data/lena_small.jpg''' , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
lowercase__ = canny.canny(SCREAMING_SNAKE_CASE )
# assert canny array for at least one True
assert canny_array.any()
def _a ( ):
"""simple docstring"""
assert gg.gaussian_filter(SCREAMING_SNAKE_CASE , 5 , sigma=0.9 ).all()
def _a ( ):
"""simple docstring"""
lowercase__ = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
lowercase__ = conv.img_convolve(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).astype(SCREAMING_SNAKE_CASE )
assert res.any()
def _a ( ):
"""simple docstring"""
assert med.median_filter(SCREAMING_SNAKE_CASE , 3 ).any()
def _a ( ):
"""simple docstring"""
lowercase__ , lowercase__ = sob.sobel_filter(SCREAMING_SNAKE_CASE )
assert grad.any() and theta.any()
def _a ( ):
"""simple docstring"""
lowercase__ = sp.make_sepia(SCREAMING_SNAKE_CASE , 20 )
assert sepia.all()
def _a ( SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg" ):
"""simple docstring"""
lowercase__ = bs.Burkes(imread(SCREAMING_SNAKE_CASE , 1 ) , 1_20 )
burkes.process()
assert burkes.output_img.any()
def _a ( SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg" , ):
"""simple docstring"""
lowercase__ = rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE , 1 ) , 4_00 , 2_00 )
nn.process()
assert nn.output.any()
def _a ( ):
"""simple docstring"""
lowercase__ = '''digital_image_processing/image_data/lena.jpg'''
# Reading the image and converting it to grayscale.
lowercase__ = imread(SCREAMING_SNAKE_CASE , 0 )
# Test for get_neighbors_pixel function() return not None
lowercase__ = 0
lowercase__ = 0
lowercase__ = image[x_coordinate][y_coordinate]
lowercase__ = lbp.get_neighbors_pixel(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
lowercase__ = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
lowercase__ = lbp.local_binary_value(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
assert lbp_image.any()
| 354
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase = [
'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
'LukeForEntityClassification',
'LukeForEntityPairClassification',
'LukeForEntitySpanClassification',
'LukeForMultipleChoice',
'LukeForQuestionAnswering',
'LukeForSequenceClassification',
'LukeForTokenClassification',
'LukeForMaskedLM',
'LukeModel',
'LukePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 93
| 0
|
import os
from math import logaa
def lowercase__ ( __snake_case : str = "base_exp.txt" ):
'''simple docstring'''
UpperCAmelCase_ : float = 0
UpperCAmelCase_ : Tuple = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__snake_case ) , __snake_case ) ) ):
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = list(map(__snake_case , line.split(',' ) ) )
if x * logaa(__snake_case ) > largest:
UpperCAmelCase_ : Union[str, Any] = x * logaa(__snake_case )
UpperCAmelCase_ : Dict = i + 1
return result
if __name__ == "__main__":
print(solution())
| 29
|
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ : Any = head.next, head
while fast and fast.next:
UpperCAmelCase_ : str = fast.next.next
UpperCAmelCase_ : Union[str, Any] = slow.next
UpperCAmelCase_ : int = slow.next
UpperCAmelCase_ : List[Any] = None # Don't forget here! But forget still works!
# reverse the second part
UpperCAmelCase_ : Tuple = None
while second:
UpperCAmelCase_ : int = second.next
UpperCAmelCase_ : Any = node
UpperCAmelCase_ : Optional[Any] = second
UpperCAmelCase_ : Tuple = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
UpperCAmelCase_ : Optional[Any] = node.next
UpperCAmelCase_ : Dict = head.next
return True
def lowercase__ ( __snake_case : Union[str, Any] ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
UpperCAmelCase_ : Any = head
while fast and fast.next:
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = fast.next.next, slow.next
# 2. Push the second half into the stack
UpperCAmelCase_ : List[str] = [slow.val]
while slow.next:
UpperCAmelCase_ : List[str] = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
UpperCAmelCase_ : int = cur.next
return True
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if not head or not head.next:
return True
UpperCAmelCase_ : Tuple = {}
UpperCAmelCase_ : int = 0
while head:
if head.val in d:
d[head.val].append(__snake_case )
else:
UpperCAmelCase_ : List[Any] = [pos]
UpperCAmelCase_ : Any = head.next
pos += 1
UpperCAmelCase_ : Dict = pos - 1
UpperCAmelCase_ : Optional[int] = 0
for v in d.values():
if len(__snake_case ) % 2 != 0:
middle += 1
else:
UpperCAmelCase_ : int = 0
for i in range(0 , len(__snake_case ) ):
if v[i] + v[len(__snake_case ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 29
| 1
|
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase__ = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
lowerCAmelCase__ = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Tuple = SavedModel()
lowercase__ : Optional[int] = []
with open(os.path.join(lowerCamelCase__ , "utils" , "tf_ops" , "onnx.json" ) ) as f:
lowercase__ : List[str] = json.load(lowerCamelCase__ )["opsets"]
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(lowerCamelCase__ )] )
with open(lowerCamelCase__ , "rb" ) as f:
saved_model.ParseFromString(f.read() )
lowercase__ : Tuple = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
lowercase__ : Optional[int] = sorted(lowerCamelCase__ )
lowercase__ : str = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(lowerCamelCase__ )
if strict and len(lowerCamelCase__ ) > 0:
raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops )
elif len(lowerCamelCase__ ) > 0:
print(F"""Found the following incompatible ops for the opset {opset}:""" )
print(*lowerCamelCase__ , sep="\n" )
else:
print(F"""The saved model {saved_model_path} can properly be converted with ONNX.""" )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
parser.add_argument(
'''--opset''', default=1_2, type=int, help='''The ONNX opset against which the model has to be tested.'''
)
parser.add_argument(
'''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
)
parser.add_argument(
'''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
)
lowerCAmelCase__ = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 121
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
'''tokenization_deberta''': ['''DebertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''DebertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DebertaForMaskedLM''',
'''DebertaForQuestionAnswering''',
'''DebertaForSequenceClassification''',
'''DebertaForTokenClassification''',
'''DebertaModel''',
'''DebertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDebertaForMaskedLM''',
'''TFDebertaForQuestionAnswering''',
'''TFDebertaForSequenceClassification''',
'''TFDebertaForTokenClassification''',
'''TFDebertaModel''',
'''TFDebertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 121
| 1
|
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = 0
lowercase__ = len(lowerCamelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCamelCase_ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def a ( lowerCamelCase_ ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return arr, 0
lowercase__ = len(lowerCamelCase_ ) // 2
lowercase__ = arr[0:mid]
lowercase__ = arr[mid:]
lowercase__ , lowercase__ = count_inversions_recursive(lowerCamelCase_ )
lowercase__ , lowercase__ = count_inversions_recursive(lowerCamelCase_ )
lowercase__ , lowercase__ = _count_cross_inversions(lowerCamelCase_ , lowerCamelCase_ )
lowercase__ = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = []
lowercase__ = lowercase__ = lowercase__ = 0
while i < len(lowerCamelCase_ ) and j < len(lowerCamelCase_ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(lowerCamelCase_ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(lowerCamelCase_ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def a ( ):
'''simple docstring'''
lowercase__ = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
lowercase__ = count_inversions_bf(lowerCamelCase_ )
lowercase__ , lowercase__ = count_inversions_recursive(lowerCamelCase_ )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''' , lowerCamelCase_ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
lowercase__ = count_inversions_bf(lowerCamelCase_ )
lowercase__ , lowercase__ = count_inversions_recursive(lowerCamelCase_ )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , lowerCamelCase_ )
# an empty list should also have zero inversions
lowercase__ = []
lowercase__ = count_inversions_bf(lowerCamelCase_ )
lowercase__ , lowercase__ = count_inversions_recursive(lowerCamelCase_ )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , lowerCamelCase_ )
if __name__ == "__main__":
main()
| 207
|
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
def lowercase__ ( self : Any ):
'''simple docstring'''
lowercase__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCamelCase, '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(lowerCamelCase, '''num_attention_heads''' ) )
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Tuple, lowerCamelCase : str, lowerCamelCase : str=13, lowerCamelCase : Union[str, Any]=64, lowerCamelCase : str=3, lowerCamelCase : int=3, lowerCamelCase : Dict=2, lowerCamelCase : int=1, lowerCamelCase : Optional[Any]=16, lowerCamelCase : Dict=[128, 256, 384], lowerCamelCase : Tuple=[4, 6, 8], lowerCamelCase : Optional[Any]=[2, 3, 4], lowerCamelCase : str=[16, 16, 16], lowerCamelCase : Dict=0, lowerCamelCase : List[str]=[2, 2, 2], lowerCamelCase : str=[2, 2, 2], lowerCamelCase : List[Any]=0.02, lowerCamelCase : Any=True, lowerCamelCase : Tuple=True, lowerCamelCase : Optional[Any]=2, ):
'''simple docstring'''
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = kernel_size
lowercase__ = stride
lowercase__ = padding
lowercase__ = hidden_sizes
lowercase__ = num_attention_heads
lowercase__ = depths
lowercase__ = key_dim
lowercase__ = drop_path_rate
lowercase__ = patch_size
lowercase__ = attention_ratio
lowercase__ = mlp_ratio
lowercase__ = initializer_range
lowercase__ = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = num_labels
lowercase__ = initializer_range
def lowercase__ ( self : Tuple ):
'''simple docstring'''
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size], self.num_labels )
lowercase__ = self.get_config()
return config, pixel_values, labels
def lowercase__ ( self : List[str] ):
'''simple docstring'''
return LevitConfig(
image_size=self.image_size, num_channels=self.num_channels, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, patch_size=self.patch_size, hidden_sizes=self.hidden_sizes, num_attention_heads=self.num_attention_heads, depths=self.depths, key_dim=self.key_dim, drop_path_rate=self.drop_path_rate, mlp_ratio=self.mlp_ratio, attention_ratio=self.attention_ratio, initializer_range=self.initializer_range, down_ops=self.down_ops, )
def lowercase__ ( self : Any, lowerCamelCase : List[Any], lowerCamelCase : int, lowerCamelCase : int ):
'''simple docstring'''
lowercase__ = LevitModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowercase__ = model(lowerCamelCase )
lowercase__ = (self.image_size, self.image_size)
lowercase__ , lowercase__ = image_size[0], image_size[1]
for _ in range(4 ):
lowercase__ = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
lowercase__ = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]), )
def lowercase__ ( self : Union[str, Any], lowerCamelCase : int, lowerCamelCase : List[Any], lowerCamelCase : List[Any] ):
'''simple docstring'''
lowercase__ = self.num_labels
lowercase__ = LevitForImageClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowercase__ = model(lowerCamelCase, labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def lowercase__ ( self : int ):
'''simple docstring'''
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( A__ ,A__ ,unittest.TestCase ):
"""simple docstring"""
lowercase__ = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
lowercase__ = (
{
"""feature-extraction""": LevitModel,
"""image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def lowercase__ ( self : List[Any] ):
'''simple docstring'''
lowercase__ = LevitModelTester(self )
lowercase__ = ConfigTester(self, config_class=lowerCamelCase, has_text_modality=lowerCamelCase, hidden_size=37 )
def lowercase__ ( self : str ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowercase__ ( self : Tuple ):
'''simple docstring'''
return
@unittest.skip(reason='''Levit does not use inputs_embeds''' )
def lowercase__ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason='''Levit does not support input and output embeddings''' )
def lowercase__ ( self : Tuple ):
'''simple docstring'''
pass
@unittest.skip(reason='''Levit does not output attentions''' )
def lowercase__ ( self : Dict ):
'''simple docstring'''
pass
def lowercase__ ( self : List[Any] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(lowerCamelCase )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1], lowerCamelCase )
def lowercase__ ( self : Tuple ):
'''simple docstring'''
def check_hidden_states_output(lowerCamelCase : Optional[int], lowerCamelCase : str, lowerCamelCase : Tuple ):
lowercase__ = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(lowerCamelCase, lowerCamelCase ) )
lowercase__ = outputs.hidden_states
lowercase__ = len(self.model_tester.depths ) + 1
self.assertEqual(len(lowerCamelCase ), lowerCamelCase )
lowercase__ = (self.model_tester.image_size, self.model_tester.image_size)
lowercase__ , lowercase__ = image_size[0], image_size[1]
for _ in range(4 ):
lowercase__ = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
lowercase__ = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ), [
height * width,
self.model_tester.hidden_sizes[0],
], )
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = True
check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowercase__ ( self : Optional[Any] ):
'''simple docstring'''
pass
def lowercase__ ( self : Union[str, Any], lowerCamelCase : List[Any], lowerCamelCase : Any, lowerCamelCase : Any=False ):
'''simple docstring'''
lowercase__ = super()._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def lowercase__ ( self : int ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def lowercase__ ( self : List[str] ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )
def lowercase__ ( self : Optional[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowerCamelCase )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
lowercase__ = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase )
lowercase__ = model(**lowerCamelCase ).loss
loss.backward()
def lowercase__ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowercase__ = False
lowercase__ = True
for model_class in self.all_model_classes:
if model_class in get_values(lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
lowercase__ = model_class(lowerCamelCase )
model.gradient_checkpointing_enable()
model.to(lowerCamelCase )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase )
lowercase__ = model(**lowerCamelCase ).loss
loss.backward()
def lowercase__ ( self : List[str] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = [
{'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
{'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
{'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowerCamelCase ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}""" ):
lowercase__ = problem_type['''title''']
lowercase__ = problem_type['''num_labels''']
lowercase__ = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.train()
lowercase__ = self._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase )
if problem_type["num_labels"] > 1:
lowercase__ = inputs['''labels'''].unsqueeze(1 ).repeat(1, problem_type['''num_labels'''] )
lowercase__ = inputs['''labels'''].to(problem_type['''dtype'''] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowerCamelCase ) as warning_list:
lowercase__ = model(**lowerCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def lowercase__ ( self : Optional[int] ):
'''simple docstring'''
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = LevitModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def a ( ):
'''simple docstring'''
lowercase__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase__ ( self : int ):
'''simple docstring'''
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowercase__ ( self : List[Any] ):
'''simple docstring'''
lowercase__ = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
lowerCamelCase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=lowerCamelCase, return_tensors='''pt''' ).to(lowerCamelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(**lowerCamelCase )
# verify the logits
lowercase__ = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape, lowerCamelCase )
lowercase__ = torch.tensor([1.0448, -0.3745, -1.8317] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3], lowerCamelCase, atol=1E-4 ) )
| 207
| 1
|
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = ["""input_values""", """padding_mask"""]
def __init__( self : int, __A : int = 1, __A : int = 2_4_0_0_0, __A : float = 0.0, __A : float = None, __A : float = None, **__A : int, ):
super().__init__(feature_size=__A, sampling_rate=__A, padding_value=__A, **__A )
UpperCAmelCase : List[str] = chunk_length_s
UpperCAmelCase : int = overlap
@property
def __magic_name__ ( self : List[str] ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def __magic_name__ ( self : Tuple ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1, int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self : Dict, __A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], __A : Optional[Union[bool, str, PaddingStrategy]] = None, __A : Optional[bool] = False, __A : Optional[int] = None, __A : Optional[Union[str, TensorType]] = None, __A : Optional[int] = None, ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
if padding and truncation:
raise ValueError('''Both padding and truncation were set. Make sure you only set one.''' )
elif padding is None:
# by default let's pad the inputs
UpperCAmelCase : Optional[int] = True
UpperCAmelCase : str = bool(
isinstance(__A, (list, tuple) ) and (isinstance(raw_audio[0], (np.ndarray, tuple, list) )) )
if is_batched:
UpperCAmelCase : int = [np.asarray(__A, dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(__A, np.ndarray ):
UpperCAmelCase : List[Any] = np.asarray(__A, dtype=np.floataa )
elif isinstance(__A, np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
UpperCAmelCase : Dict = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase : Union[str, Any] = [np.asarray(__A ).T]
# verify inputs are valid
for idx, example in enumerate(__A ):
if example.ndim > 2:
raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}''' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels''' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels''' )
UpperCAmelCase : Tuple = None
UpperCAmelCase : str = BatchFeature({'''input_values''': raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
UpperCAmelCase : Optional[Any] = min(array.shape[0] for array in raw_audio )
UpperCAmelCase : Tuple = int(np.floor(max_length / self.chunk_stride ) )
UpperCAmelCase : Dict = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
UpperCAmelCase : Union[str, Any] = max(array.shape[0] for array in raw_audio )
UpperCAmelCase : Union[str, Any] = int(np.ceil(max_length / self.chunk_stride ) )
UpperCAmelCase : List[str] = (nb_step - 1) * self.chunk_stride + self.chunk_length
UpperCAmelCase : Optional[int] = '''max_length'''
else:
UpperCAmelCase : str = input_values
# normal padding on batch
if padded_inputs is None:
UpperCAmelCase : Optional[int] = self.pad(
__A, max_length=__A, truncation=__A, padding=__A, return_attention_mask=__A, )
if padding:
UpperCAmelCase : Optional[Any] = padded_inputs.pop('''attention_mask''' )
UpperCAmelCase : Dict = []
for example in padded_inputs.pop('''input_values''' ):
if self.feature_size == 1:
UpperCAmelCase : Optional[int] = example[..., None]
input_values.append(example.T )
UpperCAmelCase : List[Any] = input_values
if return_tensors is not None:
UpperCAmelCase : List[Any] = padded_inputs.convert_to_tensors(__A )
return padded_inputs
| 353
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
_lowerCamelCase : List[Any] = None
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : str = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
_lowerCamelCase : str = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
_lowerCamelCase : Any = {
"facebook/nllb-large-en-ro": 1_0_2_4,
"facebook/nllb-200-distilled-600M": 1_0_2_4,
}
# fmt: off
_lowerCamelCase : int = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = ["""input_ids""", """attention_mask"""]
UpperCamelCase = NllbTokenizer
UpperCamelCase = []
UpperCamelCase = []
def __init__( self : Optional[Any], __A : Tuple=None, __A : int=None, __A : List[Any]="<s>", __A : Tuple="</s>", __A : Any="</s>", __A : Optional[Any]="<s>", __A : Tuple="<unk>", __A : str="<pad>", __A : Dict="<mask>", __A : Optional[Any]=None, __A : List[Any]=None, __A : List[Any]=None, __A : str=False, **__A : Tuple, ):
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : int = AddedToken(__A, lstrip=__A, rstrip=__A ) if isinstance(__A, __A ) else mask_token
UpperCAmelCase : str = legacy_behaviour
super().__init__(
vocab_file=__A, tokenizer_file=__A, bos_token=__A, eos_token=__A, sep_token=__A, cls_token=__A, unk_token=__A, pad_token=__A, mask_token=__A, src_lang=__A, tgt_lang=__A, additional_special_tokens=__A, legacy_behaviour=__A, **__A, )
UpperCAmelCase : Optional[int] = vocab_file
UpperCAmelCase : Optional[Any] = False if not self.vocab_file else True
UpperCAmelCase : Optional[int] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
UpperCAmelCase : List[Any] = {
lang_code: self.convert_tokens_to_ids(__A ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
UpperCAmelCase : List[Any] = src_lang if src_lang is not None else '''eng_Latn'''
UpperCAmelCase : List[Any] = self.convert_tokens_to_ids(self._src_lang )
UpperCAmelCase : List[str] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __magic_name__ ( self : Optional[int] ):
return self._src_lang
@src_lang.setter
def __magic_name__ ( self : Union[str, Any], __A : str ):
UpperCAmelCase : Tuple = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __magic_name__ ( self : Any, __A : List[int], __A : Optional[List[int]] = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __magic_name__ ( self : Tuple, __A : List[int], __A : Optional[List[int]] = None ):
UpperCAmelCase : Union[str, Any] = [self.sep_token_id]
UpperCAmelCase : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __magic_name__ ( self : str, __A : Optional[int], __A : str, __A : Optional[str], __A : Optional[str], **__A : Union[str, Any] ):
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
UpperCAmelCase : Optional[Any] = src_lang
UpperCAmelCase : Optional[Any] = self(__A, add_special_tokens=__A, return_tensors=__A, **__A )
UpperCAmelCase : Union[str, Any] = self.convert_tokens_to_ids(__A )
UpperCAmelCase : int = tgt_lang_id
return inputs
def __magic_name__ ( self : List[str], __A : List[str], __A : str = "eng_Latn", __A : Optional[List[str]] = None, __A : str = "fra_Latn", **__A : Tuple, ):
UpperCAmelCase : Any = src_lang
UpperCAmelCase : Tuple = tgt_lang
return super().prepare_seqaseq_batch(__A, __A, **__A )
def __magic_name__ ( self : List[str] ):
return self.set_src_lang_special_tokens(self.src_lang )
def __magic_name__ ( self : Optional[Any] ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __magic_name__ ( self : Union[str, Any], __A : List[str] ):
UpperCAmelCase : int = self.convert_tokens_to_ids(__A )
if self.legacy_behaviour:
UpperCAmelCase : Tuple = []
UpperCAmelCase : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
UpperCAmelCase : Any = [self.cur_lang_code]
UpperCAmelCase : Optional[Any] = [self.eos_token_id]
UpperCAmelCase : Dict = self.convert_ids_to_tokens(self.prefix_tokens )
UpperCAmelCase : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
UpperCAmelCase : int = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str, pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens ) ), )
def __magic_name__ ( self : Dict, __A : str ):
UpperCAmelCase : Optional[int] = self.convert_tokens_to_ids(__A )
if self.legacy_behaviour:
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Any = [self.eos_token_id, self.cur_lang_code]
else:
UpperCAmelCase : int = [self.cur_lang_code]
UpperCAmelCase : List[Any] = [self.eos_token_id]
UpperCAmelCase : str = self.convert_ids_to_tokens(self.prefix_tokens )
UpperCAmelCase : List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
UpperCAmelCase : List[str] = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str, pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens ) ), )
def __magic_name__ ( self : Dict, __A : str, __A : Optional[str] = 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(__A ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory.''' )
return
UpperCAmelCase : Tuple = os.path.join(
__A, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ):
copyfile(self.vocab_file, __A )
return (out_vocab_file,)
| 99
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
'WavLMForAudioFrameClassification',
'WavLMForCTC',
'WavLMForSequenceClassification',
'WavLMForXVector',
'WavLMModel',
'WavLMPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 303
|
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Dict = []
__lowerCAmelCase , __lowerCAmelCase : Any = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
__lowerCAmelCase : int = result + left + right
return input_list
def _lowercase ( __snake_case ) -> list:
if len(__snake_case ) <= 1:
return input_list
__lowerCAmelCase : int = list(__snake_case )
# iteration for two-way merging
__lowerCAmelCase : Optional[int] = 2
while p <= len(__snake_case ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 ,len(__snake_case ) ,__snake_case ):
__lowerCAmelCase : Union[str, Any] = i
__lowerCAmelCase : Tuple = i + p - 1
__lowerCAmelCase : Optional[Any] = (low + high + 1) // 2
__lowerCAmelCase : Any = merge(__snake_case ,__snake_case ,__snake_case ,__snake_case )
# final merge of last two parts
if p * 2 >= len(__snake_case ):
__lowerCAmelCase : Optional[Any] = i
__lowerCAmelCase : Union[str, Any] = merge(__snake_case ,0 ,__snake_case ,len(__snake_case ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
__snake_case : Union[str, Any] = input('Enter numbers separated by a comma:\n').strip()
if user_input == "":
__snake_case : Optional[int] = []
else:
__snake_case : int = [int(item.strip()) for item in user_input.split(',')]
print(iter_merge_sort(unsorted))
| 269
| 0
|
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCAmelCase_ = logging.get_logger(__name__)
class _A ( _lowerCamelCase ):
_UpperCamelCase : int = '''maskformer'''
_UpperCamelCase : Union[str, Any] = {'''hidden_size''': '''mask_feature_size'''}
_UpperCamelCase : Dict = ['''resnet''', '''swin''']
_UpperCamelCase : Optional[int] = ['''detr''']
def __init__( self : Any , _A : int = 256 , _A : int = 256 , _A : float = 0.1 , _A : bool = False , _A : Optional[Dict] = None , _A : Optional[Dict] = None , _A : float = 0.02 , _A : float = 1.0 , _A : float = 1.0 , _A : float = 1.0 , _A : float = 20.0 , _A : Optional[bool] = None , **_A : str , ) -> str:
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase : List[str] = SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(_A , _A ):
lowercase : Optional[int] = backbone_config.pop('''model_type''' )
lowercase : List[str] = CONFIG_MAPPING[backbone_model_type]
lowercase : Union[str, Any] = config_class.from_dict(_A )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {','.join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase : Any = DetrConfig()
else:
# verify that the decoder is supported
lowercase : Union[str, Any] = (
decoder_config.pop('''model_type''' ) if isinstance(_A , _A ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {','.join(self.decoders_supported )}""" )
if isinstance(_A , _A ):
lowercase : str = CONFIG_MAPPING[decoder_type]
lowercase : Dict = config_class.from_dict(_A )
lowercase : Tuple = backbone_config
lowercase : List[Any] = decoder_config
# main feature dimension for the model
lowercase : Optional[int] = fpn_feature_size
lowercase : List[Any] = mask_feature_size
# initializer
lowercase : Union[str, Any] = init_std
lowercase : Tuple = init_xavier_std
# Hungarian matcher && loss
lowercase : List[str] = cross_entropy_weight
lowercase : int = dice_weight
lowercase : List[Any] = mask_weight
lowercase : Tuple = use_auxiliary_loss
lowercase : Tuple = no_object_weight
lowercase : int = output_auxiliary_logits
lowercase : List[Any] = self.decoder_config.encoder_attention_heads
lowercase : List[Any] = self.decoder_config.num_hidden_layers
super().__init__(**_A )
@classmethod
def __a ( cls : Optional[int] , _A : PretrainedConfig , _A : PretrainedConfig , **_A : str ) -> Any:
"""simple docstring"""
return cls(
backbone_config=_A , decoder_config=_A , **_A , )
def __a ( self : Optional[int] ) -> Dict[str, any]:
"""simple docstring"""
lowercase : str = copy.deepcopy(self.__dict__ )
lowercase : Optional[Any] = self.backbone_config.to_dict()
lowercase : List[Any] = self.decoder_config.to_dict()
lowercase : Optional[int] = self.__class__.model_type
return output
| 356
|
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCAmelCase_ = logging.get_logger(__name__)
class _A ( _lowerCamelCase ):
_UpperCamelCase : str = ['''input_values''', '''attention_mask''']
def __init__( self : Optional[Any] , _A : int = 1 , _A : int = 16_000 , _A : float = 0.0 , _A : bool = False , _A : int = 80 , _A : int = 16 , _A : int = 64 , _A : str = "hann_window" , _A : float = 1.0 , _A : float = 80 , _A : float = 7_600 , _A : float = 1E-10 , _A : int = 2 , _A : bool = True , **_A : int , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(feature_size=_A , sampling_rate=_A , padding_value=_A , **_A )
lowercase : str = do_normalize
lowercase : int = return_attention_mask
lowercase : Union[str, Any] = num_mel_bins
lowercase : Union[str, Any] = hop_length
lowercase : Dict = win_length
lowercase : Union[str, Any] = win_function
lowercase : int = frame_signal_scale
lowercase : Dict = fmin
lowercase : Optional[Any] = fmax
lowercase : str = mel_floor
lowercase : Dict = reduction_factor
lowercase : List[Any] = win_length * sampling_rate // 1_000
lowercase : Union[str, Any] = hop_length * sampling_rate // 1_000
lowercase : Optional[Any] = optimal_fft_length(self.sample_size )
lowercase : Dict = (self.n_fft // 2) + 1
lowercase : Any = window_function(window_length=self.sample_size , name=self.win_function , periodic=_A )
lowercase : Dict = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm='''slaney''' , mel_scale='''slaney''' , )
if frame_signal_scale != 1.0:
warnings.warn(
'''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers''' , _A , )
if reduction_factor != 2.0:
warnings.warn(
'''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers''' , _A , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def __a ( _A : List[np.ndarray] , _A : List[np.ndarray] , _A : float = 0.0 ) -> List[np.ndarray]:
"""simple docstring"""
if attention_mask is not None:
lowercase : Optional[int] = np.array(_A , np.intaa )
lowercase : Dict = []
for vector, length in zip(_A , attention_mask.sum(-1 ) ):
lowercase : Dict = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
lowercase : List[str] = padding_value
normed_input_values.append(_A )
else:
lowercase : Dict = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __a ( self : Any , _A : np.ndarray , ) -> np.ndarray:
"""simple docstring"""
lowercase : Tuple = spectrogram(
_A , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel='''log10''' , )
return log_mel_spec.T
def __call__( self : List[Any] , _A : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _A : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , _A : Union[bool, str, PaddingStrategy] = False , _A : Optional[int] = None , _A : bool = False , _A : Optional[int] = None , _A : Optional[bool] = None , _A : Optional[Union[str, TensorType]] = None , _A : Optional[int] = None , **_A : Tuple , ) -> BatchFeature:
"""simple docstring"""
if audio is None and audio_target is None:
raise ValueError('''You must provide either `audio` or `audio_target` values.''' )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
f""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
if audio is not None:
lowercase : Any = self._process_audio(
_A , _A , _A , _A , _A , _A , _A , _A , **_A , )
else:
lowercase : Any = None
if audio_target is not None:
lowercase : Tuple = self._process_audio(
_A , _A , _A , _A , _A , _A , _A , _A , **_A , )
if inputs is None:
return inputs_target
else:
lowercase : Any = inputs_target['''input_values''']
lowercase : Dict = inputs_target.get('''attention_mask''' )
if decoder_attention_mask is not None:
lowercase : Union[str, Any] = decoder_attention_mask
return inputs
def __a ( self : List[Any] , _A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _A : bool = False , _A : Union[bool, str, PaddingStrategy] = False , _A : Optional[int] = None , _A : bool = False , _A : Optional[int] = None , _A : Optional[bool] = None , _A : Optional[Union[str, TensorType]] = None , **_A : Any , ) -> BatchFeature:
"""simple docstring"""
lowercase : Optional[int] = isinstance(_A , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
lowercase : int = is_batched_numpy or (
isinstance(_A , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowercase : Optional[int] = [np.asarray(_A , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(_A , np.ndarray ):
lowercase : List[str] = np.asarray(_A , dtype=np.floataa )
elif isinstance(_A , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
lowercase : List[str] = speech.astype(np.floataa )
# always return batch
if not is_batched:
lowercase : Union[str, Any] = [speech]
# needed to make pad() work on spectrogram inputs
lowercase : Any = self.feature_size
# convert into correct format for padding
if is_target:
lowercase : int = [self._extract_mel_features(_A ) for waveform in speech]
lowercase : Any = BatchFeature({'''input_values''': features} )
lowercase : Optional[Any] = self.num_mel_bins
else:
lowercase : Optional[Any] = BatchFeature({'''input_values''': speech} )
lowercase : Optional[int] = self.pad(
_A , padding=_A , max_length=_A , truncation=_A , pad_to_multiple_of=_A , return_attention_mask=_A , **_A , )
lowercase : str = feature_size_hack
# convert input values to correct format
lowercase : List[Any] = padded_inputs['''input_values''']
if not isinstance(input_values[0] , np.ndarray ):
lowercase : List[str] = [np.asarray(_A , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(_A , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
lowercase : List[str] = [array.astype(np.floataa ) for array in input_values]
elif isinstance(_A , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
lowercase : Optional[Any] = input_values.astype(np.floataa )
# convert attention_mask to correct format
lowercase : int = padded_inputs.get('''attention_mask''' )
if attention_mask is not None:
lowercase : Union[str, Any] = [np.asarray(_A , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
lowercase : Any = (
attention_mask
if self._get_padding_strategies(_A , max_length=_A ) is not PaddingStrategy.DO_NOT_PAD
else None
)
lowercase : Optional[int] = self.zero_mean_unit_var_norm(
padded_inputs['''input_values'''] , attention_mask=_A , padding_value=self.padding_value )
if return_tensors is not None:
lowercase : Tuple = padded_inputs.convert_to_tensors(_A )
return padded_inputs
def __a ( self : Optional[Any] ) -> Dict[str, Any]:
"""simple docstring"""
lowercase : Optional[Any] = super().to_dict()
# Don't serialize these as they are derived from the other properties.
lowercase : Optional[int] = ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
for name in names:
if name in output:
del output[name]
return output
| 116
| 0
|
'''simple docstring'''
import os
import string
import sys
UpperCamelCase__ : int = 1 << 8
UpperCamelCase__ : Tuple = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''left''': 68 + ARROW_KEY_FLAG,
'''mod_int''': 91,
'''undefined''': sys.maxsize,
'''interrupt''': 3,
'''insert''': 50,
'''delete''': 51,
'''pg_up''': 53,
'''pg_down''': 54,
}
UpperCamelCase__ : str = KEYMAP['''up''']
UpperCamelCase__ : Any = KEYMAP['''left''']
if sys.platform == "win32":
UpperCamelCase__ : Tuple = []
UpperCamelCase__ : List[str] = {
B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
}
for i in range(10):
UpperCamelCase__ : Optional[Any] = ord(str(i))
def lowerCAmelCase_ ( ):
if os.name == "nt":
import msvcrt
__SCREAMING_SNAKE_CASE : Dict = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_lowerCamelCase ) == 0:
# Read the keystroke
__SCREAMING_SNAKE_CASE : str = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
__SCREAMING_SNAKE_CASE : List[Any] = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
__SCREAMING_SNAKE_CASE : List[Any] = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
WIN_CH_BUFFER.append(_lowerCamelCase )
if ord(_lowerCamelCase ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
__SCREAMING_SNAKE_CASE : List[str] = chr(KEYMAP["""esc"""] )
except KeyError:
__SCREAMING_SNAKE_CASE : Dict = cha[1]
else:
__SCREAMING_SNAKE_CASE : Tuple = ch.decode(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
__SCREAMING_SNAKE_CASE : Optional[Any] = sys.stdin.fileno()
__SCREAMING_SNAKE_CASE : int = termios.tcgetattr(_lowerCamelCase )
try:
tty.setraw(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = sys.stdin.read(1 )
finally:
termios.tcsetattr(_lowerCamelCase , termios.TCSADRAIN , _lowerCamelCase )
return ch
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : Dict = get_raw_chars()
if ord(_lowerCamelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_lowerCamelCase ) == KEYMAP["esc"]:
__SCREAMING_SNAKE_CASE : List[Any] = get_raw_chars()
if ord(_lowerCamelCase ) == KEYMAP["mod_int"]:
__SCREAMING_SNAKE_CASE : str = get_raw_chars()
if ord(_lowerCamelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_lowerCamelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_lowerCamelCase ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 112
|
__lowerCAmelCase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def snake_case_ ( snake_case , snake_case , snake_case ) -> list[str]:
lowercase__: int = set()
# keep track of all the paths to be checked
lowercase__: Optional[int] = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
lowercase__: List[Any] = queue.pop(0 )
# get the last node from the path
lowercase__: Optional[int] = path[-1]
if node not in explored:
lowercase__: Optional[Any] = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
lowercase__: Tuple = list(snake_case )
new_path.append(snake_case )
queue.append(snake_case )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(snake_case )
# in case there's no path between the 2 nodes
return []
def snake_case_ ( snake_case , snake_case , snake_case ) -> int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
lowercase__: Tuple = [start]
lowercase__: List[Any] = set(snake_case )
# Keep tab on distances from `start` node.
lowercase__: Tuple = {start: 0, target: -1}
while queue:
lowercase__: Dict = queue.pop(0 )
if node == target:
lowercase__: str = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(snake_case )
queue.append(snake_case )
lowercase__: List[str] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 196
| 0
|
from PIL import Image
def _UpperCamelCase (a__ :Image , a__ :int ):
"""simple docstring"""
UpperCamelCase__ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(a__ :int ) -> int:
return int(128 + factor * (c - 128) )
return img.point(a__ )
if __name__ == "__main__":
# Load image
with Image.open("image_data/lena.jpg") as img:
# Change contrast to 170
UpperCamelCase__ = change_contrast(img, 170)
cont_img.save("image_data/lena_high_contrast.png", format="png")
| 87
|
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
UpperCamelCase__ = logging.getLogger(__name__)
UpperCamelCase__ = "pytorch_model.bin"
@dataclasses.dataclass
class __SCREAMING_SNAKE_CASE :
snake_case : str = dataclasses.field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""} )
snake_case : Optional[str] = dataclasses.field(
default=_a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""} , )
@dataclasses.dataclass
class __SCREAMING_SNAKE_CASE :
snake_case : str = dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""} )
snake_case : str = dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""} )
snake_case : Optional[str] = dataclasses.field(
default=_a , metadata={"""help""": """A csv or a json file containing the validation data."""} )
snake_case : Optional[str] = dataclasses.field(
default=_a , metadata={"""help""": """The name of the task to train on."""} , )
snake_case : Optional[List[str]] = dataclasses.field(
default=_a , metadata={"""help""": """The list of labels for the task."""} )
@dataclasses.dataclass
class __SCREAMING_SNAKE_CASE :
snake_case : str = dataclasses.field(
metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""} )
snake_case : Optional[str] = dataclasses.field(
default="""accuracy""" , metadata={"""help""": """The evaluation metric used for the task."""} )
snake_case : Optional[str] = dataclasses.field(
default="""no""" , metadata={
"""help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
} , )
snake_case : Optional[int] = dataclasses.field(
default=10 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
snake_case : Optional[float] = dataclasses.field(
default=0.0 , metadata={
"""help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
} , )
snake_case : Optional[bool] = dataclasses.field(
default=_a , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""} , )
snake_case : Optional[bool] = dataclasses.field(
default=_a , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""} , )
snake_case : Optional[bool] = dataclasses.field(
default=_a , metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""} , )
snake_case : Optional[float] = dataclasses.field(
default=0.0 , metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""} , )
snake_case : Optional[int] = dataclasses.field(
default=1_00 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
snake_case : Optional[int] = dataclasses.field(
default=_a , metadata={"""help""": """Random seed for initialization."""} , )
def _UpperCamelCase (a__ :List[str] , a__ :str , a__ :Any , a__ :Any , a__ :List[str] , a__ :Union[str, Any] ):
"""simple docstring"""
UpperCamelCase__ = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
UpperCamelCase__ = dataset.filter(lambda a__ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
UpperCamelCase__ = int(eval_result * len(a__ ) )
print(a__ )
UpperCamelCase__ = dataset.sort("""probability""" , reverse=a__ )
UpperCamelCase__ = dataset.select(range(a__ ) )
UpperCamelCase__ = dataset.remove_columns(["""label""", """probability"""] )
UpperCamelCase__ = dataset.rename_column("""prediction""" , """label""" )
UpperCamelCase__ = dataset.map(lambda a__ : {"label": idalabel[example["label"]]} )
UpperCamelCase__ = dataset.shuffle(seed=args.seed )
UpperCamelCase__ = os.path.join(a__ , f"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(a__ , index=a__ )
else:
dataset.to_json(a__ )
def _UpperCamelCase (a__ :Union[str, Any] , a__ :Any , a__ :Optional[int] , a__ :Union[str, Any] , **a__ :Union[str, Any] ):
"""simple docstring"""
UpperCamelCase__ = Accelerator()
# 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.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
UpperCamelCase__ = STModelArguments(model_name_or_path=a__ )
UpperCamelCase__ = STDataArguments(train_file=a__ , infer_file=a__ )
UpperCamelCase__ = STTrainingArguments(output_dir=a__ )
UpperCamelCase__ = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(a__ ).items():
setattr(a__ , a__ , a__ )
for key, value in kwargs.items():
if hasattr(a__ , a__ ):
setattr(a__ , a__ , a__ )
# Sanity checks
UpperCamelCase__ = {}
UpperCamelCase__ = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
UpperCamelCase__ = args.train_file
UpperCamelCase__ = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
UpperCamelCase__ = args.eval_file
for key in data_files:
UpperCamelCase__ = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
UpperCamelCase__ = extension
else:
assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
UpperCamelCase__ = f"""{args.output_dir}/self-train_iter-{{}}""".format
UpperCamelCase__ = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=a__ )
os.makedirs(a__ , exist_ok=a__ )
accelerator.wait_for_everyone()
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = 0
UpperCamelCase__ = False
# Show the progress bar
UpperCamelCase__ = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
UpperCamelCase__ = data_dir_format(a__ )
assert os.path.exists(a__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
UpperCamelCase__ = os.path.join(a__ , """stage-1""" )
UpperCamelCase__ = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(a__ , a__ ):
arguments_dict.update({key: value} )
UpperCamelCase__ = os.path.join(a__ , """best-checkpoint""" , a__ )
if os.path.exists(a__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , a__ , a__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , a__ )
finetune(**a__ )
accelerator.wait_for_everyone()
assert os.path.exists(a__ )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , a__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
UpperCamelCase__ = os.path.join(a__ , """best-checkpoint""" )
UpperCamelCase__ = os.path.join(a__ , """stage-2""" )
# Update arguments_dict
UpperCamelCase__ = model_path
UpperCamelCase__ = data_files["""train"""]
UpperCamelCase__ = current_output_dir
UpperCamelCase__ = os.path.join(a__ , """best-checkpoint""" , a__ )
if os.path.exists(a__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , a__ , a__ , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , a__ )
finetune(**a__ )
accelerator.wait_for_everyone()
assert os.path.exists(a__ )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , a__ )
UpperCamelCase__ = iteration
UpperCamelCase__ = data_dir_format(iteration + 1 )
UpperCamelCase__ = AutoConfig.from_pretrained(os.path.join(a__ , """best-checkpoint""" ) )
UpperCamelCase__ = config.idalabel
UpperCamelCase__ = os.path.join(a__ , """eval_results_best-checkpoint.json""" )
UpperCamelCase__ = os.path.join(a__ , """test_results_best-checkpoint.json""" )
assert os.path.exists(a__ )
with open(a__ , """r""" ) as f:
UpperCamelCase__ = float(json.load(a__ )[args.eval_metric] )
UpperCamelCase__ = os.path.join(a__ , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(a__ )
# Loading the dataset from local csv or json files.
UpperCamelCase__ = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
UpperCamelCase__ = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(a__ , exist_ok=a__ )
shutil.copy(a__ , os.path.join(a__ , f"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(a__ ):
shutil.copy(a__ , os.path.join(a__ , f"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(a__ , a__ , a__ , a__ , a__ , a__ )
accelerator.wait_for_everyone()
UpperCamelCase__ = os.path.join(a__ , f"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
UpperCamelCase__ = eval_result
if best_iteration is None:
UpperCamelCase__ = new_iteration
UpperCamelCase__ = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
UpperCamelCase__ = new_iteration
UpperCamelCase__ = new_eval_result
UpperCamelCase__ = 0
else:
if new_eval_result == best_eval_result:
UpperCamelCase__ = new_iteration
UpperCamelCase__ = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
UpperCamelCase__ = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , a__ )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , a__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(a__ , f"""eval_results_iter-{iteration}.json""" ) , os.path.join(a__ , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , a__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(a__ , f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(a__ , """eval_results_best-iteration.json""" ) , )
| 87
| 1
|
from __future__ import annotations
__UpperCAmelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, ):
SCREAMING_SNAKE_CASE_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__lowerCamelCase ) )
] # the reference grid
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__lowerCamelCase ) )
] # the action grid
SCREAMING_SNAKE_CASE_ = init[0]
SCREAMING_SNAKE_CASE_ = init[1]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = g + heuristic[x][y] # cost from starting cell to destination cell
SCREAMING_SNAKE_CASE_ = [[f, g, x, y]]
SCREAMING_SNAKE_CASE_ = False # flag that is set when search is complete
SCREAMING_SNAKE_CASE_ = False # flag set if we can't find expand
while not found and not resign:
if len(__lowerCamelCase ) == 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()
SCREAMING_SNAKE_CASE_ = cell.pop()
SCREAMING_SNAKE_CASE_ = next_cell[2]
SCREAMING_SNAKE_CASE_ = next_cell[3]
SCREAMING_SNAKE_CASE_ = next_cell[1]
if x == goal[0] and y == goal[1]:
SCREAMING_SNAKE_CASE_ = True
else:
for i in range(len(__lowerCamelCase ) ): # to try out different valid actions
SCREAMING_SNAKE_CASE_ = x + DIRECTIONS[i][0]
SCREAMING_SNAKE_CASE_ = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(__lowerCamelCase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
SCREAMING_SNAKE_CASE_ = g + cost
SCREAMING_SNAKE_CASE_ = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = i
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = goal[0]
SCREAMING_SNAKE_CASE_ = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
SCREAMING_SNAKE_CASE_ = x - DIRECTIONS[action[x][y]][0]
SCREAMING_SNAKE_CASE_ = y - DIRECTIONS[action[x][y]][1]
SCREAMING_SNAKE_CASE_ = xa
SCREAMING_SNAKE_CASE_ = ya
invpath.append([x, y] )
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__lowerCamelCase ) ):
path.append(invpath[len(__lowerCamelCase ) - 1 - i] )
return path, action
if __name__ == "__main__":
__UpperCAmelCase = [
[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],
]
__UpperCAmelCase = [0, 0]
# all coordinates are given in format [y,x]
__UpperCAmelCase = [len(grid) - 1, len(grid[0]) - 1]
__UpperCAmelCase = 1
# the cost map which pushes the path closer to the goal
__UpperCAmelCase = [[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])):
__UpperCAmelCase = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__UpperCAmelCase = 99
__UpperCAmelCase , __UpperCAmelCase = 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])
| 299
|
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ =["input_features", "is_longer"]
def __init__( self , _A=64 , _A=48000 , _A=480 , _A=10 , _A=1024 , _A=0.0 , _A=False , _A = 0 , _A = 14000 , _A = None , _A = "fusion" , _A = "repeatpad" , **_A , ) -> Dict:
super().__init__(
feature_size=_A , sampling_rate=_A , padding_value=_A , return_attention_mask=_A , **_A , )
SCREAMING_SNAKE_CASE_ = top_db
SCREAMING_SNAKE_CASE_ = truncation
SCREAMING_SNAKE_CASE_ = padding
SCREAMING_SNAKE_CASE_ = fft_window_size
SCREAMING_SNAKE_CASE_ = (fft_window_size >> 1) + 1
SCREAMING_SNAKE_CASE_ = hop_length
SCREAMING_SNAKE_CASE_ = max_length_s
SCREAMING_SNAKE_CASE_ = max_length_s * sampling_rate
SCREAMING_SNAKE_CASE_ = sampling_rate
SCREAMING_SNAKE_CASE_ = frequency_min
SCREAMING_SNAKE_CASE_ = frequency_max
SCREAMING_SNAKE_CASE_ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=_A , min_frequency=_A , max_frequency=_A , sampling_rate=_A , norm=_A , mel_scale='''htk''' , )
SCREAMING_SNAKE_CASE_ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=_A , min_frequency=_A , max_frequency=_A , sampling_rate=_A , norm='''slaney''' , mel_scale='''slaney''' , )
def _UpperCamelCase ( self ) -> Dict[str, Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _UpperCamelCase ( self , _A , _A = None ) -> np.ndarray:
SCREAMING_SNAKE_CASE_ = spectrogram(
_A , window_function(self.fft_window_size , '''hann''' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=_A , log_mel='''dB''' , )
return log_mel_spectrogram.T
def _UpperCamelCase ( self , _A , _A , _A ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
SCREAMING_SNAKE_CASE_ = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
SCREAMING_SNAKE_CASE_ = [0]
# randomly choose index for each part
SCREAMING_SNAKE_CASE_ = np.random.choice(ranges[0] )
SCREAMING_SNAKE_CASE_ = np.random.choice(ranges[1] )
SCREAMING_SNAKE_CASE_ = np.random.choice(ranges[2] )
SCREAMING_SNAKE_CASE_ = mel[idx_front : idx_front + chunk_frames, :]
SCREAMING_SNAKE_CASE_ = mel[idx_middle : idx_middle + chunk_frames, :]
SCREAMING_SNAKE_CASE_ = mel[idx_back : idx_back + chunk_frames, :]
SCREAMING_SNAKE_CASE_ = torch.tensor(mel[None, None, :] )
SCREAMING_SNAKE_CASE_ = torch.nn.functional.interpolate(
_A , size=[chunk_frames, 64] , mode='''bilinear''' , align_corners=_A )
SCREAMING_SNAKE_CASE_ = mel_shrink[0][0].numpy()
SCREAMING_SNAKE_CASE_ = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def _UpperCamelCase ( self , _A , _A , _A , _A ) -> np.array:
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
SCREAMING_SNAKE_CASE_ = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
SCREAMING_SNAKE_CASE_ = len(_A ) - max_length
SCREAMING_SNAKE_CASE_ = np.random.randint(0 , overflow + 1 )
SCREAMING_SNAKE_CASE_ = waveform[idx : idx + max_length]
SCREAMING_SNAKE_CASE_ = self._np_extract_fbank_features(_A , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
SCREAMING_SNAKE_CASE_ = self._np_extract_fbank_features(_A , self.mel_filters )
SCREAMING_SNAKE_CASE_ = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
SCREAMING_SNAKE_CASE_ = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
SCREAMING_SNAKE_CASE_ = np.stack([mel, mel, mel, mel] , axis=0 )
SCREAMING_SNAKE_CASE_ = False
else:
SCREAMING_SNAKE_CASE_ = self._random_mel_fusion(_A , _A , _A )
SCREAMING_SNAKE_CASE_ = True
else:
raise NotImplementedError(F'''data_truncating {truncation} not implemented''' )
else:
SCREAMING_SNAKE_CASE_ = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
SCREAMING_SNAKE_CASE_ = int(max_length / len(_A ) )
SCREAMING_SNAKE_CASE_ = np.stack(np.tile(_A , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
SCREAMING_SNAKE_CASE_ = int(max_length / len(_A ) )
SCREAMING_SNAKE_CASE_ = np.stack(np.tile(_A , _A ) )
SCREAMING_SNAKE_CASE_ = np.pad(_A , (0, max_length - waveform.shape[0]) , mode='''constant''' , constant_values=0 )
if truncation == "fusion":
SCREAMING_SNAKE_CASE_ = self._np_extract_fbank_features(_A , self.mel_filters )
SCREAMING_SNAKE_CASE_ = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
SCREAMING_SNAKE_CASE_ = self._np_extract_fbank_features(_A , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self , _A , _A = None , _A = None , _A = None , _A = None , _A = None , **_A , ) -> BatchFeature:
SCREAMING_SNAKE_CASE_ = truncation if truncation is not None else self.truncation
SCREAMING_SNAKE_CASE_ = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
F''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
SCREAMING_SNAKE_CASE_ = isinstance(_A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
SCREAMING_SNAKE_CASE_ = is_batched_numpy or (
isinstance(_A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
SCREAMING_SNAKE_CASE_ = [np.asarray(_A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_A , np.ndarray ):
SCREAMING_SNAKE_CASE_ = np.asarray(_A , dtype=np.floataa )
elif isinstance(_A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE_ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
SCREAMING_SNAKE_CASE_ = [np.asarray(_A )]
# convert to mel spectrogram, truncate and pad if needed.
SCREAMING_SNAKE_CASE_ = [
self._get_input_mel(_A , max_length if max_length else self.nb_max_samples , _A , _A )
for waveform in raw_speech
]
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
for mel, longer in padded_inputs:
input_mel.append(_A )
is_longer.append(_A )
if truncation == "fusion" and sum(_A ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
SCREAMING_SNAKE_CASE_ = np.random.randint(0 , len(_A ) )
SCREAMING_SNAKE_CASE_ = True
if isinstance(input_mel[0] , _A ):
SCREAMING_SNAKE_CASE_ = [np.asarray(_A , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
SCREAMING_SNAKE_CASE_ = [[longer] for longer in is_longer]
SCREAMING_SNAKE_CASE_ = {'''input_features''': input_mel, '''is_longer''': is_longer}
SCREAMING_SNAKE_CASE_ = BatchFeature(_A )
if return_tensors is not None:
SCREAMING_SNAKE_CASE_ = input_features.convert_to_tensors(_A )
return input_features
| 299
| 1
|
from __future__ import annotations
def UpperCamelCase ( __lowerCamelCase : list , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int ):
snake_case : Optional[int] = []
snake_case , snake_case : int = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
snake_case : int = result + left + right
return input_list
def UpperCamelCase ( __lowerCamelCase : list ):
if len(__lowerCamelCase ) <= 1:
return input_list
snake_case : Optional[int] = list(__lowerCamelCase )
# iteration for two-way merging
snake_case : Optional[Any] = 2
while p <= len(__lowerCamelCase ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(__lowerCamelCase ) , __lowerCamelCase ):
snake_case : int = i
snake_case : Optional[Any] = i + p - 1
snake_case : Any = (low + high + 1) // 2
snake_case : List[str] = merge(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# final merge of last two parts
if p * 2 >= len(__lowerCamelCase ):
snake_case : List[str] = i
snake_case : Optional[Any] = merge(__lowerCamelCase , 0 , __lowerCamelCase , len(__lowerCamelCase ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
__lowerCamelCase = input("""Enter numbers separated by a comma:\n""").strip()
if user_input == "":
__lowerCamelCase = []
else:
__lowerCamelCase = [int(item.strip()) for item in user_input.split(""",""")]
print(iter_merge_sort(unsorted))
| 10
|
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
__lowerCamelCase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def UpperCamelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Any ):
for attribute in key.split("." ):
snake_case : Tuple = getattr(__lowerCamelCase , __lowerCamelCase )
if weight_type is not None:
snake_case : int = getattr(__lowerCamelCase , __lowerCamelCase ).shape
else:
snake_case : Dict = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
snake_case : Dict = value
elif weight_type == "weight_g":
snake_case : Optional[int] = value
elif weight_type == "weight_v":
snake_case : Optional[int] = value
elif weight_type == "bias":
snake_case : Tuple = value
else:
snake_case : Optional[int] = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : List[str] ):
snake_case : int = []
snake_case : List[Any] = fairseq_model.state_dict()
snake_case : int = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
snake_case : List[str] = False
if "conv_layers" in name:
load_conv_layer(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , hf_model.config.feat_extract_norm == "group" , )
snake_case : str = True
else:
for key, mapped_key in MAPPING.items():
snake_case : Tuple = "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split("." )[:-1] ) != key):
# special case since naming is very similar
continue
snake_case : Tuple = True
if "*" in mapped_key:
snake_case : Union[str, Any] = name.split(__lowerCamelCase )[0].split("." )[-2]
snake_case : Any = mapped_key.replace("*" , __lowerCamelCase )
if "weight_g" in name:
snake_case : Optional[int] = "weight_g"
elif "weight_v" in name:
snake_case : Tuple = "weight_v"
elif "bias" in name:
snake_case : Dict = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case : str = "weight"
else:
snake_case : str = None
set_recursively(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
continue
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def UpperCamelCase ( __lowerCamelCase : Any , __lowerCamelCase : Any , __lowerCamelCase : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Any ):
snake_case : str = full_name.split("conv_layers." )[-1]
snake_case : int = name.split("." )
snake_case : Optional[int] = int(items[0] )
snake_case : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
snake_case : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
snake_case : List[str] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
snake_case : Dict = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
snake_case : Optional[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__lowerCamelCase )
@torch.no_grad()
def UpperCamelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : Dict , __lowerCamelCase : List[Any]=None , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : Dict=True ):
if config_path is not None:
snake_case : str = UniSpeechSatConfig.from_pretrained(__lowerCamelCase )
else:
snake_case : str = UniSpeechSatConfig()
snake_case : Tuple = ""
if is_finetuned:
snake_case : Tuple = UniSpeechSatForCTC(__lowerCamelCase )
else:
snake_case : List[Any] = UniSpeechSatForPreTraining(__lowerCamelCase )
snake_case , snake_case , snake_case : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
snake_case : Dict = model[0].eval()
recursively_load_weights(__lowerCamelCase , __lowerCamelCase )
hf_wavavec.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
__lowerCamelCase = 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 fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
__lowerCamelCase = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 10
| 1
|
'''simple docstring'''
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
while b:
SCREAMING_SNAKE_CASE : Dict = b, a % b
return a
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(__snake_case , a % b )
def __A ( ):
"""simple docstring"""
print(f'''euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}''' )
print(f'''euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}''' )
print(f'''euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}''' )
print(f'''euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}''' )
print(f'''euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}''' )
print(f'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}''' )
print(f'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}''' )
print(f'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}''' )
print(f'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}''' )
print(f'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}''' )
if __name__ == "__main__":
main()
| 323
|
"""simple docstring"""
def __magic_name__ ( __snake_case : list ) -> list:
if len(__snake_case ) < 2:
return collection
def circle_sort_util(__snake_case : list , __snake_case : int , __snake_case : int ) -> bool:
lowercase : List[Any] = False
if low == high:
return swapped
lowercase : Union[str, Any] = low
lowercase : str = high
while left < right:
if collection[left] > collection[right]:
lowercase , lowercase : Optional[Any] = (
collection[right],
collection[left],
)
lowercase : Tuple = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
lowercase , lowercase : str = (
collection[right + 1],
collection[left],
)
lowercase : Union[str, Any] = True
lowercase : Any = low + int((high - low) / 2 )
lowercase : Tuple = circle_sort_util(__snake_case , __snake_case , __snake_case )
lowercase : List[Any] = circle_sort_util(__snake_case , mid + 1 , __snake_case )
return swapped or left_swap or right_swap
lowercase : int = True
while is_not_sorted is True:
lowercase : int = circle_sort_util(__snake_case , 0 , len(__snake_case ) - 1 )
return collection
if __name__ == "__main__":
_A : str = input("""Enter numbers separated by a comma:\n""").strip()
_A : Dict = [int(item) for item in user_input.split(""",""")]
print(circle_sort(unsorted))
| 202
| 0
|
"""simple docstring"""
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
__lowercase = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class _A ( _a ,unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase : Optional[int] = XGLMTokenizer
UpperCAmelCase : List[Any] = XGLMTokenizerFast
UpperCAmelCase : Union[str, Any] = True
UpperCAmelCase : List[str] = True
def __snake_case ( self : Dict):
super().setUp()
# We have a SentencePiece fixture for testing
a : Union[str, Any] = XGLMTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase)
tokenizer.save_pretrained(self.tmpdirname)
def __snake_case ( self : str):
a : List[Any] = "<pad>"
a : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase) , __UpperCAmelCase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase) , __UpperCAmelCase)
def __snake_case ( self : Any):
a : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , "<s>")
self.assertEqual(vocab_keys[1] , "<pad>")
self.assertEqual(len(__UpperCAmelCase) , 1008)
def __snake_case ( self : Union[str, Any]):
self.assertEqual(self.get_tokenizer().vocab_size , 1008)
def __snake_case ( self : int):
a : List[Any] = XGLMTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase)
a : Dict = tokenizer.tokenize("This is a test")
self.assertListEqual(__UpperCAmelCase , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
a : Optional[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
__UpperCAmelCase , [
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 : Dict = tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
self.assertListEqual(
__UpperCAmelCase , [
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]
] , )
a : Optional[Any] = tokenizer.convert_ids_to_tokens(__UpperCAmelCase)
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def __snake_case ( self : int):
return XGLMTokenizer.from_pretrained("facebook/xglm-564M")
def __snake_case ( self : Any):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__UpperCAmelCase , f.name)
a : Optional[Any] = XGLMTokenizer(f.name , keep_accents=__UpperCAmelCase)
a : Tuple = pickle.dumps(__UpperCAmelCase)
pickle.loads(__UpperCAmelCase)
def __snake_case ( self : Optional[int]):
if not self.test_rust_tokenizer:
return
a : Any = self.get_tokenizer()
a : Tuple = self.get_rust_tokenizer()
a : Tuple = "I was born in 92000, and this is falsé."
a : Union[str, Any] = tokenizer.tokenize(__UpperCAmelCase)
a : str = rust_tokenizer.tokenize(__UpperCAmelCase)
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase)
a : List[str] = tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase)
a : Union[str, Any] = rust_tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase)
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase)
a : str = self.get_rust_tokenizer()
a : int = tokenizer.encode(__UpperCAmelCase)
a : Any = rust_tokenizer.encode(__UpperCAmelCase)
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase)
@slow
def __snake_case ( self : Optional[int]):
a : str = "Hello World!"
a : Dict = [2, 31227, 4447, 35]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase))
@slow
def __snake_case ( self : Any):
a : List[Any] = (
"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
a : Dict = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase))
@slow
def __snake_case ( self : Optional[Any]):
# fmt: off
a : Optional[Any] = {
"input_ids": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 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=__UpperCAmelCase , model_name="facebook/xglm-564M" , padding=__UpperCAmelCase , )
| 226
|
"""simple docstring"""
import sys
import turtle
def lowercase ( A_ , A_ )-> tuple[float, float]:
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase ( A_ , A_ , A_ , A_ , )-> None:
'''simple docstring'''
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
if depth == 0:
return
triangle(A_ , get_mid(A_ , A_ ) , get_mid(A_ , A_ ) , depth - 1 )
triangle(A_ , get_mid(A_ , A_ ) , get_mid(A_ , A_ ) , depth - 1 )
triangle(A_ , get_mid(A_ , A_ ) , get_mid(A_ , A_ ) , depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
"""Correct format for using this script: """
"""python fractals.py <int:depth_for_fractal>"""
)
__lowercase = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor("""red""")
__lowercase = [(-175, -125), (0, 175), (175, -125)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 226
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :Dict = [
'''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''UniSpeechForCTC''',
'''UniSpeechForPreTraining''',
'''UniSpeechForSequenceClassification''',
'''UniSpeechModel''',
'''UniSpeechPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE :List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 159
|
def _lowerCAmelCase ( lowerCAmelCase_ :int = 1_000 )->int:
'''simple docstring'''
snake_case_ , snake_case_ = 1, 1
snake_case_ = 2
while True:
snake_case_ = 0
snake_case_ = fa + fa
snake_case_ , snake_case_ = fa, f
index += 1
for _ in str(lowerCAmelCase_ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 159
| 1
|
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 , _a ) -> str:
'''simple docstring'''
lowercase_ :int = []
for part_id in partition_order:
lowercase_ :List[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 ( ) -> List[Any]:
'''simple docstring'''
lowercase_ :List[str] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
lowercase_ :List[str] = spark.range(1_0_0 ).repartition(1 )
lowercase_ :List[str] = 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 ( ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ :List[str] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
lowercase_ :Union[str, Any] = spark.range(1_0 ).repartition(2 )
lowercase_ :Dict = [1, 0]
lowercase_ :Any = _generate_iterable_examples(_a , _a ) # Reverse the partitions.
lowercase_ :Union[str, Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , _a )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
lowercase_ :str = 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:
'''simple docstring'''
lowercase_ :str = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
lowercase_ :Dict = spark.range(1_0 ).repartition(1 )
lowercase_ :List[str] = 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 ( ) -> Optional[Any]:
'''simple docstring'''
lowercase_ :Tuple = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
lowercase_ :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:
lowercase_ :Any = lambda _a : x.reverse()
lowercase_ :int = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [2, 1, 0] )
lowercase_ :int = SparkExamplesIterable(_a ).shuffle_data_sources(_a )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_a ):
lowercase_ :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 ( ) -> Any:
'''simple docstring'''
lowercase_ :List[str] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
lowercase_ :Optional[int] = spark.range(2_0 ).repartition(4 )
# Partitions 0 and 2
lowercase_ :Dict = SparkExamplesIterable(_a ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowercase_ :Tuple = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [0, 2] )
for i, (row_id, row_dict) in enumerate(_a ):
lowercase_ :str = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
lowercase_ :Optional[int] = SparkExamplesIterable(_a ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowercase_ :Union[str, Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [1, 3] )
for i, (row_id, row_dict) in enumerate(_a ):
lowercase_ :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 ( ) -> List[Any]:
'''simple docstring'''
lowercase_ :List[str] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
lowercase_ :int = spark.range(1_0_0 ).repartition(1 )
lowercase_ :Union[str, Any] = 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
| 354
|
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
SCREAMING_SNAKE_CASE : Dict = 16
SCREAMING_SNAKE_CASE : str = 32
def UpperCamelCase ( _a ) -> Any:
'''simple docstring'''
return int(x / 2**2_0 )
class UpperCamelCase :
'''simple docstring'''
def __enter__( self ):
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowercase_ :List[str] = torch.cuda.memory_allocated()
return self
def __exit__( self , *UpperCamelCase_ ):
gc.collect()
torch.cuda.empty_cache()
lowercase_ :Any = torch.cuda.memory_allocated()
lowercase_ :Union[str, Any] = torch.cuda.max_memory_allocated()
lowercase_ :Optional[int] = bamb(self.end - self.begin )
lowercase_ :List[str] = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def UpperCamelCase ( _a , _a = 1_6 , _a = "bert-base-cased" , _a = 3_2_0 , _a = 1_6_0 , ) -> Optional[Any]:
'''simple docstring'''
lowercase_ :Optional[Any] = AutoTokenizer.from_pretrained(_a )
lowercase_ :int = load_dataset(
'''glue''' , '''mrpc''' , split={'''train''': f"train[:{n_train}]", '''validation''': f"validation[:{n_val}]"} )
def tokenize_function(_a ):
# max_length=None => use the model max length (it's actually the default)
lowercase_ :Any = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_a , max_length=_a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase_ :Tuple = datasets.map(
_a , batched=_a , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=_a )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase_ :int = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_a ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_a , padding='''max_length''' , max_length=1_2_8 , return_tensors='''pt''' )
return tokenizer.pad(_a , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
lowercase_ :Union[str, Any] = DataLoader(
tokenized_datasets['''train'''] , shuffle=_a , collate_fn=_a , batch_size=_a )
lowercase_ :str = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_a , collate_fn=_a , batch_size=_a )
return train_dataloader, eval_dataloader
def UpperCamelCase ( _a , _a ) -> List[Any]:
'''simple docstring'''
lowercase_ :Tuple = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase_ :Dict = config['''lr''']
lowercase_ :List[Any] = int(config['''num_epochs'''] )
lowercase_ :Tuple = int(config['''seed'''] )
lowercase_ :List[str] = int(config['''batch_size'''] )
lowercase_ :Optional[Any] = args.model_name_or_path
set_seed(_a )
lowercase_ , lowercase_ :Any = get_dataloaders(_a , _a , _a , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase_ :Tuple = AutoModelForSequenceClassification.from_pretrained(_a , return_dict=_a )
# Instantiate optimizer
lowercase_ :Any = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase_ :str = optimizer_cls(params=model.parameters() , lr=_a )
if accelerator.state.deepspeed_plugin is not None:
lowercase_ :str = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
lowercase_ :List[str] = 1
lowercase_ :Union[str, Any] = (len(_a ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase_ :int = get_linear_schedule_with_warmup(
optimizer=_a , num_warmup_steps=0 , num_training_steps=_a , )
else:
lowercase_ :str = DummyScheduler(_a , total_num_steps=_a , warmup_num_steps=0 )
# 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.
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ :Optional[Any] = accelerator.prepare(
_a , _a , _a , _a , _a )
# We need to keep track of how many total steps we have iterated over
lowercase_ :Dict = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase_ :int = 0
# Now we train the model
lowercase_ :str = {}
for epoch in range(_a , _a ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(_a ):
lowercase_ :Optional[Any] = model(**_a )
lowercase_ :Dict = outputs.loss
lowercase_ :Dict = loss / gradient_accumulation_steps
accelerator.backward(_a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin ) ) )
accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used ) )
accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked ) )
accelerator.print(
'''Total Peak Memory consumed during the train (max): {}'''.format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowercase_ :Union[str, Any] = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f"epoch-{epoch}"] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , '''peak_memory_utilization.json''' ) , '''w''' ) as f:
json.dump(_a , _a )
def UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ :List[str] = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' , type=_a , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=_a , )
parser.add_argument(
'''--output_dir''' , type=_a , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--peak_memory_upper_bound''' , type=_a , default=_a , help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.''' , )
parser.add_argument(
'''--n_train''' , type=_a , default=3_2_0 , help='''Number of training examples to use.''' , )
parser.add_argument(
'''--n_val''' , type=_a , default=1_6_0 , help='''Number of validation examples to use.''' , )
parser.add_argument(
'''--num_epochs''' , type=_a , default=1 , help='''Number of train epochs.''' , )
lowercase_ :Dict = parser.parse_args()
lowercase_ :Dict = {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
training_function(_a , _a )
if __name__ == "__main__":
main()
| 252
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|
import string
def A_ ( snake_case : str ) -> None:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
__UpperCamelCase = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
__UpperCamelCase = string.ascii_uppercase.find(snake_case )
__UpperCamelCase = num - key
if num < 0:
__UpperCamelCase = num + len(string.ascii_uppercase )
__UpperCamelCase = translated + string.ascii_uppercase[num]
else:
__UpperCamelCase = translated + symbol
print(f"Decryption using Key #{key}: {translated}" )
def A_ ( ) -> None:
'''simple docstring'''
__UpperCamelCase = input('''Encrypted message: ''' )
__UpperCamelCase = message.upper()
decrypt(snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 328
|
from __future__ import annotations
import math
def A_ ( snake_case : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
lowercase__ : int = [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]
def A_ ( snake_case : int ) -> list[int]:
'''simple docstring'''
if not isinstance(snake_case , snake_case ):
raise ValueError('''n must be an integer''' )
if n <= 0:
raise ValueError('''n must be >= 0''' )
__UpperCamelCase = []
for num in range(len(snake_case ) ):
__UpperCamelCase = 0
while 2 * i * i <= odd_composites[num]:
__UpperCamelCase = odd_composites[num] - 2 * i * i
if is_prime(snake_case ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(snake_case ) == n:
return list_nums
return []
def A_ ( ) -> int:
'''simple docstring'''
return compute_nums(1 )[0]
if __name__ == "__main__":
print(F"{solution() = }")
| 328
| 1
|
__A : Optional[int] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__A : Any = [{'type': 'code', 'content': INSTALL_CONTENT}]
__A : List[str] = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 356
|
"""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
| 57
| 0
|
'''simple docstring'''
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
snake_case_ = credit_card_number
snake_case_ = 0
snake_case_ = len(snake_case ) - 2
for i in range(snake_case , -1 , -2 ):
# double the value of every second digit
snake_case_ = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 1_0
digit += 1
snake_case_ = cc_number[:i] + str(snake_case ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(snake_case ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 1_0 == 0
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
snake_case_ = f'{credit_card_number} is an invalid credit card number because'
if not credit_card_number.isdigit():
print(f'{error_message} it has nonnumerical characters.' )
return False
if not 1_3 <= len(snake_case ) <= 1_6:
print(f'{error_message} of its length.' )
return False
if not validate_initial_digits(snake_case ):
print(f'{error_message} of its first two digits.' )
return False
if not luhn_validation(snake_case ):
print(f'{error_message} it fails the Luhn check.' )
return False
print(f'{credit_card_number} is a valid credit card number.' )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 85
|
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
_SCREAMING_SNAKE_CASE : int = {
"gpt-neox-20b": 2048,
}
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : str = VOCAB_FILES_NAMES
lowerCAmelCase_ : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ : str = ["input_ids", "attention_mask"]
def __init__( self , a__=None , a__=None , a__=None , a__="<|endoftext|>" , a__="<|endoftext|>" , a__="<|endoftext|>" , a__=False , **a__ , ) -> Tuple:
'''simple docstring'''
super().__init__(
a__ , a__ , tokenizer_file=a__ , unk_token=a__ , bos_token=a__ , eos_token=a__ , add_prefix_space=a__ , **a__ , )
snake_case_ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , a__ ) != add_prefix_space:
snake_case_ = getattr(a__ , pre_tok_state.pop("type" ) )
snake_case_ = add_prefix_space
snake_case_ = pre_tok_class(**a__ )
snake_case_ = add_prefix_space
def lowerCAmelCase__ ( self , a__ , a__ = None ) -> Tuple[str]:
'''simple docstring'''
snake_case_ = self._tokenizer.model.save(a__ , name=a__ )
return tuple(a__ )
def lowerCAmelCase__ ( self , a__ ) -> List[int]:
'''simple docstring'''
snake_case_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(a__ , add_special_tokens=a__ ) + [self.eos_token_id] )
if len(a__ ) > self.model_max_length:
snake_case_ = input_ids[-self.model_max_length :]
return input_ids
| 85
| 1
|
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__A ):
'''simple docstring'''
lowerCamelCase_ = ['''torch''', '''torchsde''']
def __init__( self , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(self , ['torch', 'torchsde'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'torchsde'] )
@classmethod
def lowerCAmelCase_ ( cls , *lowercase , **lowercase ):
"""simple docstring"""
requires_backends(cls , ['torch', 'torchsde'] )
| 360
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AutoformerForPrediction""",
"""AutoformerModel""",
"""AutoformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 192
| 0
|
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self : List[str] ) ->Union[str, Any]:
snake_case__ : Dict = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case__ : int = AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' ).to(__lowerCAmelCase )
snake_case__ : Union[str, Any] = -1
snake_case__ : Dict = ids_tensor((1, 5), vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
snake_case__ : int = model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase )
snake_case__ : List[str] = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
snake_case__ : List[str] = TextStreamer(__lowerCAmelCase )
model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase, streamer=__lowerCAmelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
snake_case__ : int = cs.out[:-1]
self.assertEqual(__lowerCAmelCase, __lowerCAmelCase )
def lowercase_ ( self : Dict ) ->Union[str, Any]:
snake_case__ : Union[str, Any] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case__ : Optional[Any] = AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' ).to(__lowerCAmelCase )
snake_case__ : Optional[Any] = -1
snake_case__ : List[str] = ids_tensor((1, 5), vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
snake_case__ : Tuple = model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase )
snake_case__ : Optional[int] = tokenizer.decode(greedy_ids[0] )
snake_case__ : Optional[int] = TextIteratorStreamer(__lowerCAmelCase )
snake_case__ : Optional[int] = {'input_ids': input_ids, 'max_new_tokens': 1_0, 'do_sample': False, 'streamer': streamer}
snake_case__ : List[str] = Thread(target=model.generate, kwargs=__lowerCAmelCase )
thread.start()
snake_case__ : Union[str, Any] = ''
for new_text in streamer:
streamer_text += new_text
self.assertEqual(__lowerCAmelCase, __lowerCAmelCase )
def lowercase_ ( self : Tuple ) ->Union[str, Any]:
snake_case__ : List[Any] = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case__ : Any = AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' ).to(__lowerCAmelCase )
snake_case__ : Optional[int] = -1
snake_case__ : List[str] = ids_tensor((1, 5), vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
snake_case__ : List[Any] = model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase )
snake_case__ : Union[str, Any] = greedy_ids[:, input_ids.shape[1] :]
snake_case__ : Any = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
snake_case__ : str = TextStreamer(__lowerCAmelCase, skip_prompt=__lowerCAmelCase )
model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase, streamer=__lowerCAmelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
snake_case__ : Optional[int] = cs.out[:-1]
self.assertEqual(__lowerCAmelCase, __lowerCAmelCase )
def lowercase_ ( self : str ) ->Union[str, Any]:
snake_case__ : Any = AutoTokenizer.from_pretrained('distilgpt2' )
snake_case__ : Any = AutoModelForCausalLM.from_pretrained('distilgpt2' ).to(__lowerCAmelCase )
snake_case__ : Optional[int] = -1
snake_case__ : Union[str, Any] = torch.ones((1, 5), device=__lowerCAmelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
snake_case__ : Optional[int] = TextStreamer(__lowerCAmelCase, skip_special_tokens=__lowerCAmelCase )
model.generate(__lowerCAmelCase, max_new_tokens=1, do_sample=__lowerCAmelCase, streamer=__lowerCAmelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
snake_case__ : List[Any] = cs.out[:-1] # Remove the final "\n"
snake_case__ : Any = tokenizer(__lowerCAmelCase, return_tensors='pt' )
self.assertEqual(streamer_text_tokenized.input_ids.shape, (1, 1) )
def lowercase_ ( self : Tuple ) ->Optional[int]:
snake_case__ : str = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case__ : Tuple = AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' ).to(__lowerCAmelCase )
snake_case__ : List[Any] = -1
snake_case__ : Any = ids_tensor((1, 5), vocab_size=model.config.vocab_size ).to(__lowerCAmelCase )
snake_case__ : str = TextIteratorStreamer(__lowerCAmelCase, timeout=0.0_0_1 )
snake_case__ : List[Any] = {'input_ids': input_ids, 'max_new_tokens': 1_0, 'do_sample': False, 'streamer': streamer}
snake_case__ : Dict = Thread(target=model.generate, kwargs=__lowerCAmelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(__lowerCAmelCase ):
snake_case__ : List[str] = ''
for new_text in streamer:
streamer_text += new_text
| 277
|
def lowerCAmelCase__(__snake_case ) -> list:
'''simple docstring'''
lowerCamelCase__ = len(__snake_case )
for _ in range(__snake_case ):
for i in range(_ % 2 ,arr_size - 1 ,2 ):
if arr[i + 1] < arr[i]:
lowerCamelCase__ , lowerCamelCase__ = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
_a = list(range(10, 0, -1))
print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
| 209
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class UpperCAmelCase_ ( A_ ):
def __init__( self : str , *snake_case_ : List[Any] , **snake_case_ : Optional[Any] ) -> None:
'''simple docstring'''
warnings.warn(
"The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use CLIPImageProcessor instead." , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 230
|
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class UpperCAmelCase_ ( A_ ):
lowercase__ = '''dpt'''
def __init__( self : List[Any] , snake_case_ : Union[str, Any]=768 , snake_case_ : Tuple=12 , snake_case_ : Tuple=12 , snake_case_ : List[Any]=3_072 , snake_case_ : Dict="gelu" , snake_case_ : Tuple=0.0 , snake_case_ : int=0.0 , snake_case_ : Optional[int]=0.02 , snake_case_ : Union[str, Any]=1e-12 , snake_case_ : Tuple=384 , snake_case_ : Tuple=16 , snake_case_ : Optional[Any]=3 , snake_case_ : Dict=False , snake_case_ : Any=True , snake_case_ : Any=[2, 5, 8, 11] , snake_case_ : Union[str, Any]="project" , snake_case_ : Union[str, Any]=[4, 2, 1, 0.5] , snake_case_ : List[str]=[96, 192, 384, 768] , snake_case_ : int=256 , snake_case_ : Tuple=-1 , snake_case_ : List[str]=False , snake_case_ : int=True , snake_case_ : List[Any]=0.4 , snake_case_ : Optional[Any]=255 , snake_case_ : List[str]=0.1 , snake_case_ : List[str]=[1, 1_024, 24, 24] , snake_case_ : Union[str, Any]=[0, 1] , snake_case_ : Any=None , **snake_case_ : Optional[Any] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**snake_case_ )
A__ = hidden_size
A__ = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("Initializing the config with a `BiT` backbone." )
A__ = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
}
A__ = BitConfig(**snake_case_ )
elif isinstance(snake_case_ , snake_case_ ):
logger.info("Initializing the config with a `BiT` backbone." )
A__ = BitConfig(**snake_case_ )
elif isinstance(snake_case_ , snake_case_ ):
A__ = backbone_config
else:
raise ValueError(
F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )
A__ = backbone_featmap_shape
A__ = neck_ignore_stages
if readout_type != "project":
raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode." )
else:
A__ = None
A__ = None
A__ = []
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = initializer_range
A__ = layer_norm_eps
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = qkv_bias
A__ = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']" )
A__ = readout_type
A__ = reassemble_factors
A__ = neck_hidden_sizes
A__ = fusion_hidden_size
A__ = head_in_index
A__ = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
A__ = use_auxiliary_head
A__ = auxiliary_loss_weight
A__ = semantic_loss_ignore_index
A__ = semantic_classifier_dropout
def __magic_name__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
A__ = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
A__ = self.backbone_config.to_dict()
A__ = self.__class__.model_type
return output
| 230
| 1
|
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowercase_ ( _lowerCamelCase : Features):
lowercase__ : List[Any] = np.inf
def set_batch_size(_lowerCamelCase : FeatureType) -> None:
nonlocal batch_size
if isinstance(_lowerCamelCase , _lowerCamelCase):
lowercase__ : Any = min(_lowerCamelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS)
elif isinstance(_lowerCamelCase , _lowerCamelCase):
lowercase__ : Union[str, Any] = min(_lowerCamelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS)
elif isinstance(_lowerCamelCase , _lowerCamelCase) and feature.dtype == "binary":
lowercase__ : Dict = min(_lowerCamelCase , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS)
_visit(_lowerCamelCase , _lowerCamelCase)
return None if batch_size is np.inf else batch_size
class snake_case_ ( __A ):
def __init__( self : List[str] , lowercase_ : NestedDataStructureLike[PathLike] , lowercase_ : Optional[NamedSplit] = None , lowercase_ : Optional[Features] = None , lowercase_ : str = None , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : Optional[int] = None , **lowercase_ : Union[str, Any] , ) -> Union[str, Any]:
super().__init__(
lowercase_ , split=lowercase_ , features=lowercase_ , cache_dir=lowercase_ , keep_in_memory=lowercase_ , streaming=lowercase_ , num_proc=lowercase_ , **lowercase_ , )
lowercase__ : List[str] = path_or_paths if isinstance(lowercase_ , lowercase_ ) else {self.split: path_or_paths}
lowercase__ : Optional[int] = _PACKAGED_DATASETS_MODULES["parquet"][1]
lowercase__ : Union[str, Any] = Parquet(
cache_dir=lowercase_ , data_files=lowercase_ , features=lowercase_ , hash=lowercase_ , **lowercase_ , )
def __UpperCamelCase ( self : Tuple ) -> Dict:
# Build iterable dataset
if self.streaming:
lowercase__ : List[Any] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
lowercase__ : Any = None
lowercase__ : int = None
lowercase__ : Dict = None
lowercase__ : Union[str, Any] = None
self.builder.download_and_prepare(
download_config=lowercase_ , download_mode=lowercase_ , verification_mode=lowercase_ , base_path=lowercase_ , num_proc=self.num_proc , )
lowercase__ : Optional[Any] = self.builder.as_dataset(
split=self.split , verification_mode=lowercase_ , in_memory=self.keep_in_memory )
return dataset
class snake_case_ :
def __init__( self : str , lowercase_ : Dataset , lowercase_ : Union[PathLike, BinaryIO] , lowercase_ : Optional[int] = None , **lowercase_ : Optional[Any] , ) -> int:
lowercase__ : Dict = dataset
lowercase__ : List[Any] = path_or_buf
lowercase__ : Any = batch_size or get_writer_batch_size(dataset.features )
lowercase__ : Any = parquet_writer_kwargs
def __UpperCamelCase ( self : List[Any] ) -> int:
lowercase__ : Tuple = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , "wb+" ) as buffer:
lowercase__ : str = self._write(file_obj=lowercase_ , batch_size=lowercase_ , **self.parquet_writer_kwargs )
else:
lowercase__ : Tuple = self._write(file_obj=self.path_or_buf , batch_size=lowercase_ , **self.parquet_writer_kwargs )
return written
def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : BinaryIO , lowercase_ : int , **lowercase_ : Optional[int] ) -> int:
lowercase__ : List[Any] = 0
lowercase__ : List[Any] = parquet_writer_kwargs.pop("path_or_buf" , lowercase_ )
lowercase__ : List[Any] = self.dataset.features.arrow_schema
lowercase__ : Optional[Any] = pq.ParquetWriter(lowercase_ , schema=lowercase_ , **lowercase_ )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , lowercase_ ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
lowercase__ : Any = query_table(
table=self.dataset._data , key=slice(lowercase_ , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(lowercase_ )
written += batch.nbytes
writer.close()
return written
| 87
|
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : List[Any] , _lowerCamelCase : Dict):
# Initialise PyTorch model
lowercase__ : List[str] = BertConfig.from_json_file(_lowerCamelCase)
print(f'''Building PyTorch model from configuration: {config}''')
lowercase__ : Optional[Any] = BertForPreTraining(_lowerCamelCase)
# Load weights from tf checkpoint
load_tf_weights_in_bert(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''')
torch.save(model.state_dict() , _lowerCamelCase)
if __name__ == "__main__":
UpperCamelCase = 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(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT 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.'''
)
UpperCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 87
| 1
|
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
A : List[str] = logging.get_logger(__name__)
def __lowerCAmelCase ( a__ ) -> List[int]:
if isinstance(a__ , np.ndarray ):
return list(tensor.shape )
__a = tf.shape(a__ )
if tensor.shape == tf.TensorShape(a__ ):
return dynamic
__a = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(a__ )]
def __lowerCAmelCase ( a__ , a__ = None , a__ = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=a__ , name=a__ )
def __lowerCAmelCase ( a__ , a__ , a__ , a__=1e-5 , a__=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a__ , a__ ):
raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )
# Get mean and variance on the axis to be normalized
__a , __a = tf.nn.moments(a__ , axes=[axis] , keepdims=a__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
__a = [1] * inputs.shape.rank
__a = shape_list(a__ )[axis]
__a = tf.reshape(a__ , a__ )
__a = tf.reshape(a__ , a__ )
# Compute layer normalization using the batch_normalization
# function.
__a = tf.nn.batch_normalization(
a__ , a__ , a__ , offset=a__ , scale=a__ , variance_epsilon=a__ , )
return outputs
def __lowerCAmelCase ( a__ , a__=0 , a__=-1 ) -> List[str]:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
__a = tf.shape(a__ )
__a = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__a = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(a__ , a__ )
def __lowerCAmelCase ( a__ ) -> tf.Tensor:
if not isinstance(a__ , tf.Tensor ):
__a = tf.convert_to_tensor(a__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__a = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__a = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
__a = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __lowerCAmelCase ( a__ , a__ , a__ = "input_ids" ) -> None:
tf.debugging.assert_less(
a__ , tf.cast(a__ , dtype=tensor.dtype ) , message=(
F"""The maximum value of {tensor_name} ({tf.math.reduce_max(a__ )}) must be smaller than the embedding """
F"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
) , )
def __lowerCAmelCase ( a__ , a__ , a__ ) -> int:
__a = 6_4512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
__a = [x for x in data if len(a__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'''The following attributes cannot be saved to HDF5 file because '''
F"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
F"""bytes: {bad_attributes}""" )
__a = np.asarray(a__ )
__a = 1
__a = np.array_split(a__ , a__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
__a = np.array_split(a__ , a__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(a__ ):
__a = chunk_data
else:
__a = data
def __lowerCAmelCase ( a__ , a__ ) -> str:
if name in group.attrs:
__a = [n.decode('''utf8''' ) if hasattr(a__ , '''decode''' ) else n for n in group.attrs[name]]
else:
__a = []
__a = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(a__ , '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def __lowerCAmelCase ( a__ ) -> List[str]:
def _expand_single_ad_tensor(a__ ):
if isinstance(a__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(a__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , a__ )
| 33
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : str = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimeSeriesTransformerForPrediction',
'TimeSeriesTransformerModel',
'TimeSeriesTransformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 33
| 1
|
from __future__ import annotations
def lowerCAmelCase_ ( __a , __a , __a , __a ) -> list:
"""simple docstring"""
lowerCamelCase__: Any =[]
lowerCamelCase__ , lowerCamelCase__: Any =input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
lowerCamelCase__: str =result + left + right
return input_list
def lowerCAmelCase_ ( __a ) -> list:
"""simple docstring"""
if len(__a ) <= 1:
return input_list
lowerCamelCase__: Any =list(__a )
# iteration for two-way merging
lowerCamelCase__: str =2
while p <= len(__a ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(__a ) , __a ):
lowerCamelCase__: Dict =i
lowerCamelCase__: List[str] =i + p - 1
lowerCamelCase__: int =(low + high + 1) // 2
lowerCamelCase__: Optional[int] =merge(__a , __a , __a , __a )
# final merge of last two parts
if p * 2 >= len(__a ):
lowerCamelCase__: List[Any] =i
lowerCamelCase__: Optional[int] =merge(__a , 0 , __a , len(__a ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
__A = input("Enter numbers separated by a comma:\n").strip()
if user_input == "":
__A = []
else:
__A = [int(item.strip()) for item in user_input.split(",")]
print(iter_merge_sort(unsorted))
| 10
|
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
__A = logging.get_logger(__name__)
@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__(self : Tuple , **UpperCAmelCase_ : Tuple) ->Any:
'''simple docstring'''
super().__init__(**UpperCAmelCase_)
if self.framework == "tf":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""")
requires_backends(self , "vision")
self.check_model_type(UpperCAmelCase_)
def __call__(self : Optional[int] , UpperCAmelCase_ : Union[str, "Image.Image", List[Dict[str, Any]]] , UpperCAmelCase_ : Union[str, List[str]] = None , **UpperCAmelCase_ : List[str] , ) ->Union[str, Any]:
'''simple docstring'''
if "text_queries" in kwargs:
lowerCamelCase__: Any =kwargs.pop("text_queries")
if isinstance(UpperCAmelCase_ , (str, Image.Image)):
lowerCamelCase__: List[Any] ={"image": image, "candidate_labels": candidate_labels}
else:
lowerCamelCase__: Any =image
lowerCamelCase__: Dict =super().__call__(UpperCAmelCase_ , **UpperCAmelCase_)
return results
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , **UpperCAmelCase_ : Union[str, Any]) ->Dict:
'''simple docstring'''
lowerCamelCase__: List[str] ={}
if "threshold" in kwargs:
lowerCamelCase__: List[Any] =kwargs["threshold"]
if "top_k" in kwargs:
lowerCamelCase__: Any =kwargs["top_k"]
return {}, {}, postprocess_params
def SCREAMING_SNAKE_CASE_ (self : str , UpperCAmelCase_ : List[Any]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: List[Any] =load_image(inputs["image"])
lowerCamelCase__: Dict =inputs["candidate_labels"]
if isinstance(UpperCAmelCase_ , UpperCAmelCase_):
lowerCamelCase__: Any =candidate_labels.split(",")
lowerCamelCase__: Optional[int] =torch.tensor([[image.height, image.width]] , dtype=torch.intaa)
for i, candidate_label in enumerate(UpperCAmelCase_):
lowerCamelCase__: Dict =self.tokenizer(UpperCAmelCase_ , return_tensors=self.framework)
lowerCamelCase__: Union[str, Any] =self.image_processor(UpperCAmelCase_ , return_tensors=self.framework)
yield {
"is_last": i == len(UpperCAmelCase_) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def SCREAMING_SNAKE_CASE_ (self : Optional[Any] , UpperCAmelCase_ : Tuple) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Dict =model_inputs.pop("target_size")
lowerCamelCase__: Dict =model_inputs.pop("candidate_label")
lowerCamelCase__: Dict =model_inputs.pop("is_last")
lowerCamelCase__: Union[str, Any] =self.model(**UpperCAmelCase_)
lowerCamelCase__: Dict ={"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : Any=0.1 , UpperCAmelCase_ : str=None) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =[]
for model_output in model_outputs:
lowerCamelCase__: Optional[Any] =model_output["candidate_label"]
lowerCamelCase__: Tuple =BaseModelOutput(UpperCAmelCase_)
lowerCamelCase__: Dict =self.image_processor.post_process_object_detection(
outputs=UpperCAmelCase_ , threshold=UpperCAmelCase_ , target_sizes=model_output["target_size"])[0]
for index in outputs["scores"].nonzero():
lowerCamelCase__: Dict =outputs["scores"][index].item()
lowerCamelCase__: Dict =self._get_bounding_box(outputs["boxes"][index][0])
lowerCamelCase__: Optional[Any] ={"score": score, "label": label, "box": box}
results.append(UpperCAmelCase_)
lowerCamelCase__: List[str] =sorted(UpperCAmelCase_ , key=lambda UpperCAmelCase_: x["score"] , reverse=UpperCAmelCase_)
if top_k:
lowerCamelCase__: Dict =results[:top_k]
return results
def SCREAMING_SNAKE_CASE_ (self : str , UpperCAmelCase_ : "torch.Tensor") ->Dict[str, int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Optional[Any] =box.int().tolist()
lowerCamelCase__: Optional[int] ={
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 10
| 1
|
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
__lowerCAmelCase = """detr"""
__lowerCAmelCase = ["""past_key_values"""]
__lowerCAmelCase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : List[Any] , lowerCamelCase_ : Optional[Any]=True , lowerCamelCase_ : Optional[Any]=None , lowerCamelCase_ : Dict=3 , lowerCamelCase_ : Any=100 , lowerCamelCase_ : Any=6 , lowerCamelCase_ : Tuple=2048 , lowerCamelCase_ : List[Any]=8 , lowerCamelCase_ : Optional[int]=6 , lowerCamelCase_ : Tuple=2048 , lowerCamelCase_ : int=8 , lowerCamelCase_ : Dict=0.0 , lowerCamelCase_ : Optional[Any]=0.0 , lowerCamelCase_ : List[str]=True , lowerCamelCase_ : Union[str, Any]="relu" , lowerCamelCase_ : Tuple=256 , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : Dict=0.0 , lowerCamelCase_ : Any=0.0 , lowerCamelCase_ : Tuple=0.0_2 , lowerCamelCase_ : Any=1.0 , lowerCamelCase_ : List[str]=False , lowerCamelCase_ : str="sine" , lowerCamelCase_ : Union[str, Any]="resnet50" , lowerCamelCase_ : Any=True , lowerCamelCase_ : int=False , lowerCamelCase_ : Union[str, Any]=1 , lowerCamelCase_ : List[str]=5 , lowerCamelCase_ : List[Any]=2 , lowerCamelCase_ : Optional[Any]=1 , lowerCamelCase_ : int=1 , lowerCamelCase_ : Union[str, Any]=5 , lowerCamelCase_ : Union[str, Any]=2 , lowerCamelCase_ : List[str]=0.1 , **lowerCamelCase_ : List[Any] , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCamelCase = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
UpperCamelCase = backbone_config.get("""model_type""" )
UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
UpperCamelCase = config_class.from_dict(lowerCamelCase_ )
# set timm attributes to None
UpperCamelCase , UpperCamelCase , UpperCamelCase = None, None, None
UpperCamelCase = use_timm_backbone
UpperCamelCase = backbone_config
UpperCamelCase = num_channels
UpperCamelCase = num_queries
UpperCamelCase = d_model
UpperCamelCase = encoder_ffn_dim
UpperCamelCase = encoder_layers
UpperCamelCase = encoder_attention_heads
UpperCamelCase = decoder_ffn_dim
UpperCamelCase = decoder_layers
UpperCamelCase = decoder_attention_heads
UpperCamelCase = dropout
UpperCamelCase = attention_dropout
UpperCamelCase = activation_dropout
UpperCamelCase = activation_function
UpperCamelCase = init_std
UpperCamelCase = init_xavier_std
UpperCamelCase = encoder_layerdrop
UpperCamelCase = decoder_layerdrop
UpperCamelCase = encoder_layers
UpperCamelCase = auxiliary_loss
UpperCamelCase = position_embedding_type
UpperCamelCase = backbone
UpperCamelCase = use_pretrained_backbone
UpperCamelCase = dilation
# Hungarian matcher
UpperCamelCase = class_cost
UpperCamelCase = bbox_cost
UpperCamelCase = giou_cost
# Loss coefficients
UpperCamelCase = mask_loss_coefficient
UpperCamelCase = dice_loss_coefficient
UpperCamelCase = bbox_loss_coefficient
UpperCamelCase = giou_loss_coefficient
UpperCamelCase = eos_coefficient
super().__init__(is_encoder_decoder=lowerCamelCase_ , **lowerCamelCase_ )
@property
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
return self.d_model
@classmethod
def lowerCamelCase_ ( cls : Tuple , lowerCamelCase_ : PretrainedConfig , **lowerCamelCase_ : List[str] ):
"""simple docstring"""
return cls(backbone_config=lowerCamelCase_ , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCamelCase = self.backbone_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
__lowerCAmelCase = version.parse("""1.11""" )
@property
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
return 1E-5
@property
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
return 12
| 165
|
from math import pi
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 165
| 1
|
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_A : int = _symbol_database.Default()
_A : Optional[int] = _descriptor_pool.Default().AddSerializedFile(
b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)
_A : Union[str, Any] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_A : List[str] = None
_A : str = b'''H\003'''
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_A : Optional[Any] = 45
_A : Optional[int] = 1581
_A : Optional[int] = 1517
_A : Dict = 1570
_A : List[Any] = 1584
_A : Union[str, Any] = 1793
_A : Any = 1795
_A : List[str] = 1916
_A : Dict = 1864
_A : str = 1905
_A : Optional[Any] = 1919
_A : List[str] = 2429
_A : int = 2208
_A : Dict = 2418
_A : str = 2323
_A : Dict = 2407
# @@protoc_insertion_point(module_scope)
| 229
|
'''simple docstring'''
def UpperCamelCase_ ( snake_case_ : Union[str, Any]=2_81_23 ) -> str:
'''simple docstring'''
__lowerCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
__lowerCAmelCase = set()
__lowerCAmelCase = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(snake_case_ )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 229
| 1
|
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=5 ) -> Optional[Any]:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('''<mask>''' ) == 1
lowercase__: str = torch.tensor(tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ ) ).unsqueeze(0 ) # Batch size 1
lowercase__: int = model(lowercase_ )[0] # The last hidden-state is the first element of the output tuple
lowercase__: Union[str, Any] = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
lowercase__: Optional[Any] = logits[0, masked_index, :]
lowercase__: List[str] = logits.softmax(dim=0 )
lowercase__, lowercase__: Union[str, Any] = prob.topk(k=lowercase_ , dim=0 )
lowercase__: List[str] = ''' '''.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(lowercase_ ) )] )
lowercase__: str = tokenizer.mask_token
lowercase__: Optional[int] = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''' ) ):
lowercase__: Optional[Any] = predicted_token_bpe.replace('''\u2581''' , ''' ''' )
if " {0}".format(lowercase_ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(''' {0}'''.format(lowercase_ ) , lowercase_ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(lowercase_ , lowercase_ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
__A = CamembertTokenizer.from_pretrained("camembert-base")
__A = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
__A = '''Le camembert est <mask> :)'''
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 356
|
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase :Union[str, Any] = CTRLTokenizer
_UpperCAmelCase :Any = False
_UpperCAmelCase :List[Any] = False
def _snake_case ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase__: Dict = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
lowercase__: Any = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) )
lowercase__: Optional[int] = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
lowercase__: Optional[Any] = {'''unk_token''': '''<unk>'''}
lowercase__: Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase__: int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_UpperCAmelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_UpperCAmelCase ) )
def _snake_case ( self , **_UpperCAmelCase ):
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def _snake_case ( self , _UpperCAmelCase ):
lowercase__: Optional[int] = '''adapt react readapt apt'''
lowercase__: Optional[int] = '''adapt react readapt apt'''
return input_text, output_text
def _snake_case ( self ):
lowercase__: List[str] = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowercase__: Optional[int] = '''adapt react readapt apt'''
lowercase__: Any = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
lowercase__: Optional[Any] = tokenizer.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: int = tokens + [tokenizer.unk_token]
lowercase__: str = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , _UpperCAmelCase )
| 2
| 0
|
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def __UpperCAmelCase ( A : jnp.ndarray , A : int , A : float = 1 , A : float = 1 , A : float = 1.0e4 , A : bool = False , A : float = 1.0 , ) -> int:
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, F"Embedding dimension {embedding_dim} should be even"
UpperCAmelCase_ : Optional[int] = float(embedding_dim // 2 )
UpperCAmelCase_ : Tuple = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
UpperCAmelCase_ : List[str] = min_timescale * jnp.exp(jnp.arange(lowerCamelCase__ , dtype=jnp.floataa ) * -log_timescale_increment )
UpperCAmelCase_ : Any = jnp.expand_dims(lowerCamelCase__ , 1 ) * jnp.expand_dims(lowerCamelCase__ , 0 )
# scale embeddings
UpperCAmelCase_ : List[str] = scale * emb
if flip_sin_to_cos:
UpperCAmelCase_ : Optional[int] = jnp.concatenate([jnp.cos(lowerCamelCase__ ), jnp.sin(lowerCamelCase__ )] , axis=1 )
else:
UpperCAmelCase_ : str = jnp.concatenate([jnp.sin(lowerCamelCase__ ), jnp.cos(lowerCamelCase__ )] , axis=1 )
UpperCAmelCase_ : Optional[int] = jnp.reshape(lowerCamelCase__ , [jnp.shape(lowerCamelCase__ )[0], embedding_dim] )
return signal
class snake_case__ ( nn.Module):
a_ = 32
a_ = jnp.floataa
@nn.compact
def __call__( self : Dict , _A : Tuple ) -> int:
UpperCAmelCase_ : str = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_1''' )(_A )
UpperCAmelCase_ : Optional[int] = nn.silu(_A )
UpperCAmelCase_ : Any = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_2''' )(_A )
return temb
class snake_case__ ( nn.Module):
a_ = 32
a_ = False
a_ = 1
@nn.compact
def __call__( self : Any , _A : Union[str, Any] ) -> int:
return get_sinusoidal_embeddings(
_A , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 304
|
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
UpperCAmelCase : Optional[int] = random.Random()
def __lowerCamelCase ( lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Optional[Any]=1.0 , lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : Dict=None ):
'''simple docstring'''
if rng is None:
lowerCamelCase = global_rng
lowerCamelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , A , A=7 , A=4_00 , A=20_00 , A=1 , A=0.0 , A=1_60_00 , A=True , A=True , ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase = parent
lowerCamelCase = batch_size
lowerCamelCase = min_seq_length
lowerCamelCase = max_seq_length
lowerCamelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase = feature_size
lowerCamelCase = padding_value
lowerCamelCase = sampling_rate
lowerCamelCase = return_attention_mask
lowerCamelCase = do_normalize
def __A ( self ) -> Any:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def __A ( self , A=False , A=False ) -> Any:
'''simple docstring'''
def _flatten(A ):
return list(itertools.chain(*A ) )
if equal_length:
lowerCamelCase = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
lowerCamelCase = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowerCamelCase = [np.asarray(A ) for x in speech_inputs]
return speech_inputs
class __lowercase ( a_ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : Optional[int] = WavaVecaFeatureExtractor
def __A ( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase = WavaVecaFeatureExtractionTester(self )
def __A ( self , A ) -> Any:
'''simple docstring'''
self.assertTrue(np.all(np.mean(A , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(A , axis=0 ) - 1 ) < 1e-3 ) )
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = [np.asarray(A ) for speech_input in speech_inputs]
# Test not batched input
lowerCamelCase = feat_extract(speech_inputs[0] , return_tensors="""np""" ).input_values
lowerCamelCase = feat_extract(np_speech_inputs[0] , return_tensors="""np""" ).input_values
self.assertTrue(np.allclose(A , A , atol=1e-3 ) )
# Test batched
lowerCamelCase = feat_extract(A , return_tensors="""np""" ).input_values
lowerCamelCase = feat_extract(A , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(A , A ):
self.assertTrue(np.allclose(A , A , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
lowerCamelCase = np.asarray(A )
lowerCamelCase = feat_extract(A , return_tensors="""np""" ).input_values
lowerCamelCase = feat_extract(A , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(A , A ):
self.assertTrue(np.allclose(A , A , atol=1e-3 ) )
def __A ( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = ["""longest""", """max_length""", """do_not_pad"""]
lowerCamelCase = [None, 16_00, None]
for max_length, padding in zip(A , A ):
lowerCamelCase = feat_extract(A , padding=A , max_length=A , return_tensors="""np""" )
lowerCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00] )
self.assertTrue(input_values[0][8_00:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:10_00] )
self.assertTrue(input_values[0][10_00:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:12_00] )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = range(8_00 , 14_00 , 2_00 )
lowerCamelCase = [floats_list((1, x) )[0] for x in lengths]
lowerCamelCase = ["""longest""", """max_length""", """do_not_pad"""]
lowerCamelCase = [None, 16_00, None]
for max_length, padding in zip(A , A ):
lowerCamelCase = feat_extract(A , max_length=A , padding=A )
lowerCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_00] )
self._check_zero_mean_unit_variance(input_values[1][:10_00] )
self._check_zero_mean_unit_variance(input_values[2][:12_00] )
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = feat_extract(
A , truncation=A , max_length=10_00 , padding="""max_length""" , return_tensors="""np""" )
lowerCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = feat_extract(
A , truncation=A , max_length=10_00 , padding="""longest""" , return_tensors="""np""" )
lowerCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00] )
self._check_zero_mean_unit_variance(input_values[1, :10_00] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 10_00) )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = feat_extract(
A , truncation=A , max_length=20_00 , padding="""longest""" , return_tensors="""np""" )
lowerCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_00] )
self._check_zero_mean_unit_variance(input_values[1, :10_00] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 12_00) )
@require_torch
def __A ( self ) -> Optional[int]:
'''simple docstring'''
import torch
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = np.random.rand(1_00 ).astype(np.floataa )
lowerCamelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
lowerCamelCase = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def __A ( self ) -> str:
'''simple docstring'''
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
lowerCamelCase = WavaVecaConfig.from_pretrained(A )
lowerCamelCase = WavaVecaFeatureExtractor.from_pretrained(A )
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == """layer""" )
| 252
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|
def lowerCAmelCase_ ( __lowerCamelCase ):
__snake_case : Union[str, Any] = [0] * len(__lowerCamelCase )
for i in range(1 , len(__lowerCamelCase ) ):
# use last results for better performance - dynamic programming
__snake_case : Dict = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
__snake_case : List[Any] = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
__snake_case : Optional[int] = j
return prefix_result
def lowerCAmelCase_ ( __lowerCamelCase ):
return max(prefix_function(__lowerCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 134
|
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Union[str, Any] = logging.get_logger(__name__)
_snake_case : List[str] = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
"facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = "encodec"
def __init__( self : Any , lowerCamelCase : Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowerCamelCase : List[str]=24000 , lowerCamelCase : int=1 , lowerCamelCase : Optional[int]=False , lowerCamelCase : Dict=None , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[int]=128 , lowerCamelCase : Optional[int]=32 , lowerCamelCase : List[str]=1 , lowerCamelCase : str=[8, 5, 4, 2] , lowerCamelCase : List[str]="weight_norm" , lowerCamelCase : Any=7 , lowerCamelCase : Tuple=7 , lowerCamelCase : int=3 , lowerCamelCase : int=2 , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[Any]="reflect" , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : int=1.0 , lowerCamelCase : Optional[Any]=1024 , lowerCamelCase : Optional[Any]=None , lowerCamelCase : str=True , **lowerCamelCase : Dict , ) -> Any:
__snake_case : Tuple = target_bandwidths
__snake_case : Union[str, Any] = sampling_rate
__snake_case : Union[str, Any] = audio_channels
__snake_case : Dict = normalize
__snake_case : List[Any] = chunk_length_s
__snake_case : Tuple = overlap
__snake_case : Optional[int] = hidden_size
__snake_case : List[Any] = num_filters
__snake_case : Union[str, Any] = num_residual_layers
__snake_case : Optional[int] = upsampling_ratios
__snake_case : List[str] = norm_type
__snake_case : Optional[int] = kernel_size
__snake_case : Dict = last_kernel_size
__snake_case : Tuple = residual_kernel_size
__snake_case : List[Any] = dilation_growth_rate
__snake_case : Optional[int] = use_causal_conv
__snake_case : Tuple = pad_mode
__snake_case : Union[str, Any] = compress
__snake_case : Union[str, Any] = num_lstm_layers
__snake_case : int = trim_right_ratio
__snake_case : Tuple = codebook_size
__snake_case : Optional[Any] = codebook_dim if codebook_dim is not None else hidden_size
__snake_case : int = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' )
super().__init__(**lowerCamelCase )
@property
def __snake_case ( self : int ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def __snake_case ( self : Union[str, Any] ) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def __snake_case ( self : Optional[Any] ) -> int:
__snake_case : Union[str, Any] = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def __snake_case ( self : Optional[Any] ) -> int:
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 134
| 1
|
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a_ :Optional[Any] = logging.getLogger(__name__)
def lowercase_ (A : List[Any] , A : List[Any] ):
# save results
if os.path.exists(A ):
if os.path.exists(os.path.join(A , 'config.json' ) ) and os.path.isfile(
os.path.join(A , 'config.json' ) ):
os.remove(os.path.join(A , 'config.json' ) )
if os.path.exists(os.path.join(A , 'pytorch_model.bin' ) ) and os.path.isfile(
os.path.join(A , 'pytorch_model.bin' ) ):
os.remove(os.path.join(A , 'pytorch_model.bin' ) )
else:
os.makedirs(A )
model.save_pretrained(A )
def lowercase_ (A : Any , A : Optional[Any]=False ):
snake_case__ : str = 2
if unlogit:
snake_case__ : Dict = torch.pow(A , A )
snake_case__ : Any = p * torch.log(A )
snake_case__ : Tuple = 0
return -plogp.sum(dim=-1 )
def lowercase_ (A : List[str] ):
logger.info('lv, h >\t' + '\t'.join(F'''{x + 1}''' for x in range(len(A ) ) ) )
for row in range(len(A ) ):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:d}''' for x in tensor[row].cpu().data ) )
def lowercase_ (A : Tuple , A : Optional[Any] , A : str , A : int=True , A : Optional[int]=True , A : Any=None , A : int=False ):
snake_case__ , snake_case__ : Optional[Any] = model.config.num_hidden_layers, model.config.num_attention_heads
snake_case__ : int = torch.zeros(A , A ).to(args.device )
snake_case__ : Any = torch.zeros(A , A ).to(args.device )
if head_mask is None:
snake_case__ : Dict = torch.ones(A , A ).to(args.device )
head_mask.requires_grad_(requires_grad=A )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
snake_case__ : Optional[int] = None
snake_case__ : List[Any] = 0.0
snake_case__ : str = 0.0
for step, inputs in enumerate(tqdm(A , desc='Iteration' , disable=args.local_rank not in [-1, 0] ) ):
snake_case__ : Union[str, Any] = tuple(t.to(args.device ) for t in inputs )
((snake_case__) , ) : Optional[Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
snake_case__ : Union[str, Any] = model(A , labels=A , head_mask=A )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
snake_case__ , snake_case__ , snake_case__ : Dict = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A ):
snake_case__ : Optional[Any] = entropy(attn.detach() , A )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
snake_case__ : Union[str, Any] = 2
snake_case__ : List[Any] = torch.pow(torch.pow(A , A ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
snake_case__ : Tuple = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('Attention entropies' )
print_ad_tensor(A )
if compute_importance:
logger.info('Head importance scores' )
print_ad_tensor(A )
logger.info('Head ranked by importance scores' )
snake_case__ : Tuple = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
snake_case__ : Union[str, Any] = torch.arange(
head_importance.numel() , device=args.device )
snake_case__ : str = head_ranks.view_as(A )
print_ad_tensor(A )
return attn_entropy, head_importance, total_loss
def lowercase_ (A : Optional[int] , A : Dict , A : Optional[int] ):
snake_case__ , snake_case__ , snake_case__ : Any = compute_heads_importance(A , A , A , compute_entropy=A )
snake_case__ : Tuple = 1 / loss # instead of downsteam score use the LM loss
logger.info('Pruning: original score: %f, threshold: %f' , A , original_score * args.masking_threshold )
snake_case__ : Optional[Any] = torch.ones_like(A )
snake_case__ : Union[str, Any] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
snake_case__ : Dict = original_score
while current_score >= original_score * args.masking_threshold:
snake_case__ : int = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
snake_case__ : List[Any] = float('Inf' )
snake_case__ : Union[str, Any] = head_importance.view(-1 ).sort()[1]
if len(A ) <= num_to_mask:
print('BREAK BY num_to_mask' )
break
# mask heads
snake_case__ : int = current_heads_to_mask[:num_to_mask]
logger.info('Heads to mask: %s' , str(current_heads_to_mask.tolist() ) )
snake_case__ : int = new_head_mask.view(-1 )
snake_case__ : int = 0.0
snake_case__ : Union[str, Any] = new_head_mask.view_as(A )
snake_case__ : List[str] = new_head_mask.clone().detach()
print_ad_tensor(A )
# Compute metric and head importance again
snake_case__ , snake_case__ , snake_case__ : Any = compute_heads_importance(
A , A , A , compute_entropy=A , head_mask=A )
snake_case__ : Dict = 1 / loss
logger.info(
'Masking: current score: %f, remaining heads %d (%.1f percents)' , A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_0_0 , )
logger.info('Final head mask' )
print_ad_tensor(A )
np.save(os.path.join(args.output_dir , 'head_mask.npy' ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowercase_ (A : List[str] , A : Tuple , A : Optional[Any] , A : int ):
snake_case__ : Any = datetime.now()
snake_case__ , snake_case__ , snake_case__ : str = compute_heads_importance(
A , A , A , compute_entropy=A , compute_importance=A , head_mask=A )
snake_case__ : Tuple = 1 / loss
snake_case__ : Dict = datetime.now() - before_time
snake_case__ : Union[str, Any] = sum(p.numel() for p in model.parameters() )
snake_case__ : Optional[Any] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A ) )
}
for k, v in heads_to_prune.items():
if isinstance(A , A ):
snake_case__ : Any = [
v,
]
assert sum(len(A ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A )
snake_case__ : Dict = sum(p.numel() for p in model.parameters() )
snake_case__ : Tuple = datetime.now()
snake_case__ , snake_case__ , snake_case__ : Dict = compute_heads_importance(
A , A , A , compute_entropy=A , compute_importance=A , head_mask=A , actually_pruned=A , )
snake_case__ : Any = 1 / loss
snake_case__ : int = datetime.now() - before_time
logger.info(
'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)' , A , A , pruned_num_params / original_num_params * 1_0_0 , )
logger.info('Pruning: score with masking: %f score with pruning: %f' , A , A )
logger.info('Pruning: speed ratio (original timing / new timing): %f percents' , original_time / new_time * 1_0_0 )
save_model(A , args.output_dir )
def lowercase_ ():
snake_case__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--data_dir' , default=A , type=A , required=A , help='The input data dir. Should contain the .tsv files (or other data files) for the task.' , )
parser.add_argument(
'--model_name_or_path' , default=A , type=A , required=A , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--output_dir' , default=A , type=A , required=A , help='The output directory where the model predictions and checkpoints will be written.' , )
# Other parameters
parser.add_argument(
'--config_name' , default='' , type=A , help='Pretrained config name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--tokenizer_name' , default='' , type=A , help='Pretrained tokenizer name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--cache_dir' , default=A , type=A , help='Where do you want to store the pre-trained models downloaded from s3' , )
parser.add_argument(
'--data_subset' , type=A , default=-1 , help='If > 0: limit the data to a subset of data_subset instances.' )
parser.add_argument(
'--overwrite_output_dir' , action='store_true' , help='Whether to overwrite data in output directory' )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
parser.add_argument(
'--dont_normalize_importance_by_layer' , action='store_true' , help='Don\'t normalize importance score by layers' )
parser.add_argument(
'--dont_normalize_global_importance' , action='store_true' , help='Don\'t normalize all importance scores between 0 and 1' , )
parser.add_argument(
'--try_masking' , action='store_true' , help='Whether to try to mask head until a threshold of accuracy.' )
parser.add_argument(
'--masking_threshold' , default=0.9 , type=A , help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).' , )
parser.add_argument(
'--masking_amount' , default=0.1 , type=A , help='Amount to heads to masking at each masking step.' )
parser.add_argument('--metric_name' , default='acc' , type=A , help='Metric to use for head masking.' )
parser.add_argument(
'--max_seq_length' , default=1_2_8 , type=A , help=(
'The maximum total input sequence length after WordPiece tokenization. \n'
'Sequences longer than this will be truncated, sequences shorter padded.'
) , )
parser.add_argument('--batch_size' , default=1 , type=A , help='Batch size.' )
parser.add_argument('--seed' , type=A , default=4_2 )
parser.add_argument('--local_rank' , type=A , default=-1 , help='local_rank for distributed training on gpus' )
parser.add_argument('--no_cuda' , action='store_true' , help='Whether not to use CUDA when available' )
parser.add_argument('--server_ip' , type=A , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=A , default='' , help='Can be used for distant debugging.' )
snake_case__ : Optional[int] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
snake_case__ : List[Any] = torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
snake_case__ : Optional[Any] = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
snake_case__ : int = torch.device('cuda' , args.local_rank )
snake_case__ : List[str] = 1
torch.distributed.init_process_group(backend='nccl' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
snake_case__ : Any = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
snake_case__ : List[str] = nn.parallel.DistributedDataParallel(
A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A )
elif args.n_gpu > 1:
snake_case__ : Optional[int] = nn.DataParallel(A )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A )
torch.save(A , os.path.join(args.output_dir , 'run_args.bin' ) )
logger.info('Training/evaluation parameters %s' , A )
# Prepare dataset
snake_case__ : Optional[Any] = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
snake_case__ : List[str] = (torch.from_numpy(A ),)
snake_case__ : int = TensorDataset(*A )
snake_case__ : Union[str, Any] = RandomSampler(A )
snake_case__ : Any = DataLoader(A , sampler=A , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A , A , A )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
snake_case__ : Dict = mask_heads(A , A , A )
prune_heads(A , A , A , A )
if __name__ == "__main__":
main()
| 277
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ :Optional[int] = logging.get_logger(__name__)
a_ :Dict = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """openai-gpt"""
_SCREAMING_SNAKE_CASE = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Optional[int], _snake_case : Dict=4_0_4_7_8, _snake_case : str=5_1_2, _snake_case : int=7_6_8, _snake_case : Tuple=1_2, _snake_case : Any=1_2, _snake_case : str="gelu", _snake_case : List[str]=0.1, _snake_case : Any=0.1, _snake_case : Dict=0.1, _snake_case : int=1e-5, _snake_case : Optional[Any]=0.0_2, _snake_case : List[Any]="cls_index", _snake_case : Any=True, _snake_case : Any=None, _snake_case : int=True, _snake_case : Optional[Any]=0.1, **_snake_case : List[Any], ) ->Optional[int]:
snake_case__ : int = vocab_size
snake_case__ : Dict = n_positions
snake_case__ : str = n_embd
snake_case__ : str = n_layer
snake_case__ : List[Any] = n_head
snake_case__ : List[Any] = afn
snake_case__ : Optional[Any] = resid_pdrop
snake_case__ : List[str] = embd_pdrop
snake_case__ : List[Any] = attn_pdrop
snake_case__ : Optional[int] = layer_norm_epsilon
snake_case__ : str = initializer_range
snake_case__ : List[str] = summary_type
snake_case__ : Optional[int] = summary_use_proj
snake_case__ : List[str] = summary_activation
snake_case__ : Optional[Any] = summary_first_dropout
snake_case__ : int = summary_proj_to_labels
super().__init__(**_snake_case )
| 277
| 1
|
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
UpperCamelCase_ =[
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def a_ ( ):
_UpperCamelCase : str = Github(os.environ['''GITHUB_TOKEN'''] )
_UpperCamelCase : List[Any] = g.get_repo('''huggingface/diffusers''' )
_UpperCamelCase : Any = repo.get_issues(state='''open''' )
for issue in open_issues:
_UpperCamelCase : Optional[int] = sorted(issue.get_comments() , key=lambda _lowercase : i.created_at , reverse=_lowercase )
_UpperCamelCase : Optional[Any] = comments[0] if len(_lowercase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 128
|
"""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 a_ ( _lowercase , _lowercase , _lowercase ):
# Initialise PyTorch model
_UpperCamelCase : List[Any] = MobileBertConfig.from_json_file(_lowercase )
print(F"""Building PyTorch model from configuration: {config}""" )
_UpperCamelCase : List[str] = MobileBertForPreTraining(_lowercase )
# Load weights from tf checkpoint
_UpperCamelCase : Union[str, Any] = load_tf_weights_in_mobilebert(_lowercase , _lowercase , _lowercase )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , _lowercase )
if __name__ == "__main__":
UpperCamelCase_ =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."""
)
UpperCamelCase_ =parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 128
| 1
|
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> int:
snake_case : str = len(__lowerCamelCase )
snake_case : Any = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
snake_case : List[Any] = True
# sum is not zero and set is empty then false
for i in range(1 ,required_sum + 1 ):
snake_case : Tuple = False
for i in range(1 ,arr_len + 1 ):
for j in range(1 ,required_sum + 1 ):
if arr[i - 1] > j:
snake_case : Dict = subset[i - 1][j]
if arr[i - 1] <= j:
snake_case : Dict = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 124
|
from __future__ import annotations
from collections.abc import Callable
__UpperCAmelCase = list[list[float | int]]
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = [[0 for _ in range(size + 1 )] for _ in range(__lowerCamelCase )]
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for row in range(__lowerCamelCase ):
for col in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = matrix[row][col]
SCREAMING_SNAKE_CASE_ = vector[row][0]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE_ = max((abs(augmented[rowa][col] ), rowa) for rowa in range(__lowerCamelCase, __lowerCamelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = augmented[pivot_row], augmented[row]
for rowa in range(row + 1, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE_ = 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, __lowerCamelCase ):
for row in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = augmented[row][col] / augmented[col][col]
for cola in range(__lowerCamelCase, 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(__lowerCamelCase )
]
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = [[0 for _ in range(__lowerCamelCase )] for _ in range(__lowerCamelCase )]
SCREAMING_SNAKE_CASE_ = [[0] for _ in range(__lowerCamelCase )]
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for x_val, y_val in enumerate(__lowerCamelCase ):
for col in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = (x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE_ = y_val
SCREAMING_SNAKE_CASE_ = solve(__lowerCamelCase, __lowerCamelCase )
def interpolated_func(__lowerCamelCase ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__lowerCamelCase ) )
return interpolated_func
def A__ ( __lowerCamelCase ):
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def A__ ( __lowerCamelCase = question_function, __lowerCamelCase = 10 ):
SCREAMING_SNAKE_CASE_ = [func(__lowerCamelCase ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE_ = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for poly in polynomials:
SCREAMING_SNAKE_CASE_ = 1
while func(__lowerCamelCase ) == poly(__lowerCamelCase ):
x_val += 1
ret += poly(__lowerCamelCase )
return ret
if __name__ == "__main__":
print(F"""{solution() = }""")
| 299
| 0
|
"""simple docstring"""
from __future__ import annotations
__A : List[str] = tuple[int, int, int]
__A : Dict = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__A : Any = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default selection --------------------------
# rotors --------------------------
__A : List[Any] = 'EGZWVONAHDCLFQMSIPJBYUKXTR'
__A : List[str] = 'FOBHMDKEXQNRAULPGSJVTYICZW'
__A : Union[str, Any] = 'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
__A : Optional[Any] = {
'A': 'N',
'N': 'A',
'B': 'O',
'O': 'B',
'C': 'P',
'P': 'C',
'D': 'Q',
'Q': 'D',
'E': 'R',
'R': 'E',
'F': 'S',
'S': 'F',
'G': 'T',
'T': 'G',
'H': 'U',
'U': 'H',
'I': 'V',
'V': 'I',
'J': 'W',
'W': 'J',
'K': 'X',
'X': 'K',
'L': 'Y',
'Y': 'L',
'M': 'Z',
'Z': 'M',
}
# -------------------------- extra rotors --------------------------
__A : int = 'RMDJXFUWGISLHVTCQNKYPBEZOA'
__A : Dict = 'SGLCPQWZHKXAREONTFBVIYJUDM'
__A : Optional[Any] = 'HVSICLTYKQUBXDWAJZOMFGPREN'
__A : Optional[int] = 'RZWQHFMVDBKICJLNTUXAGYPSOE'
__A : str = 'LFKIJODBEGAMQPXVUHYSTCZRWN'
__A : Union[str, Any] = 'KOAEGVDHXPQZMLFTYWJNBRCIUS'
def __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , lowercase__ ):
"""simple docstring"""
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(lowercase__ ) )) < 3:
A = F"""Please use 3 unique rotors (not {unique_rotsel})"""
raise Exception(lowercase__ )
# Checks if rotor positions are valid
A , A , A = rotpos
if not 0 < rotorposa <= len(lowercase__ ):
A = F"""First rotor position is not within range of 1..26 ({rotorposa}"""
raise ValueError(lowercase__ )
if not 0 < rotorposa <= len(lowercase__ ):
A = F"""Second rotor position is not within range of 1..26 ({rotorposa})"""
raise ValueError(lowercase__ )
if not 0 < rotorposa <= len(lowercase__ ):
A = F"""Third rotor position is not within range of 1..26 ({rotorposa})"""
raise ValueError(lowercase__ )
# Validates string and returns dict
A = _plugboard(lowercase__ )
return rotpos, rotsel, pbdict
def __SCREAMING_SNAKE_CASE ( lowercase__ ):
"""simple docstring"""
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(lowercase__ , lowercase__ ):
A = F"""Plugboard setting isn't type string ({type(lowercase__ )})"""
raise TypeError(lowercase__ )
elif len(lowercase__ ) % 2 != 0:
A = F"""Odd number of symbols ({len(lowercase__ )})"""
raise Exception(lowercase__ )
elif pbstring == "":
return {}
pbstring.replace(" " , "" )
# Checks if all characters are unique
A = set()
for i in pbstring:
if i not in abc:
A = F"""'{i}' not in list of symbols"""
raise Exception(lowercase__ )
elif i in tmppbl:
A = F"""Duplicate symbol ({i})"""
raise Exception(lowercase__ )
else:
tmppbl.add(lowercase__ )
del tmppbl
# Created the dictionary
A = {}
for j in range(0 , len(lowercase__ ) - 1 , 2 ):
A = pbstring[j + 1]
A = pbstring[j]
return pb
def __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , lowercase__ = (rotora, rotora, rotora) , lowercase__ = "" , ):
"""simple docstring"""
A = text.upper()
A , A , A = _validator(
lowercase__ , lowercase__ , plugb.upper() )
A , A , A = rotor_position
A , A , A = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
A = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
A = plugboard[symbol]
# rotor ra --------------------------
A = abc.index(lowercase__ ) + rotorposa
A = rotora[index % len(lowercase__ )]
# rotor rb --------------------------
A = abc.index(lowercase__ ) + rotorposa
A = rotora[index % len(lowercase__ )]
# rotor rc --------------------------
A = abc.index(lowercase__ ) + rotorposa
A = rotora[index % len(lowercase__ )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
A = reflector[symbol]
# 2nd rotors
A = abc[rotora.index(lowercase__ ) - rotorposa]
A = abc[rotora.index(lowercase__ ) - rotorposa]
A = abc[rotora.index(lowercase__ ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
A = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(lowercase__ ):
A = 0
rotorposa += 1
if rotorposa >= len(lowercase__ ):
A = 0
rotorposa += 1
if rotorposa >= len(lowercase__ ):
A = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(lowercase__ )
return "".join(lowercase__ )
if __name__ == "__main__":
__A : Any = 'This is my Python script that emulates the Enigma machine from WWII.'
__A : str = (1, 1, 1)
__A : str = 'pictures'
__A : Tuple = (rotora, rotora, rotora)
__A : Any = enigma(message, rotor_pos, rotor_sel, pb)
print('Encrypted message:', en)
print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))
| 57
|
"""simple docstring"""
from __future__ import annotations
class __UpperCamelCase :
def __init__(self : Tuple , __SCREAMING_SNAKE_CASE : int = 0):
A = key
def SCREAMING_SNAKE_CASE__ (self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int):
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) and isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
A = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_5_5
return [chr(ord(__SCREAMING_SNAKE_CASE) ^ key) for ch in content]
def SCREAMING_SNAKE_CASE__ (self : Optional[int] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int):
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) and isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
A = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_5_5
return [chr(ord(__SCREAMING_SNAKE_CASE) ^ key) for ch in content]
def SCREAMING_SNAKE_CASE__ (self : Optional[int] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int = 0):
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) and isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
A = key or self.__key or 1
# make sure key can be any size
while key > 2_5_5:
key -= 2_5_5
# This will be returned
A = ""
for ch in content:
ans += chr(ord(__SCREAMING_SNAKE_CASE) ^ key)
return ans
def SCREAMING_SNAKE_CASE__ (self : Optional[int] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int = 0):
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) and isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
A = key or self.__key or 1
# make sure key can be any size
while key > 2_5_5:
key -= 2_5_5
# This will be returned
A = ""
for ch in content:
ans += chr(ord(__SCREAMING_SNAKE_CASE) ^ key)
return ans
def SCREAMING_SNAKE_CASE__ (self : List[str] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int = 0):
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) and isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
try:
with open(__SCREAMING_SNAKE_CASE) as fin, open("encrypt.out" , "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
except OSError:
return False
return True
def SCREAMING_SNAKE_CASE__ (self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int):
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) and isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
try:
with open(__SCREAMING_SNAKE_CASE) as fin, open("decrypt.out" , "w+") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE))
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 57
| 1
|
def UpperCAmelCase_ ( __snake_case ) -> str:
"""simple docstring"""
_lowercase =0
# if input_string is "aba" than new_input_string become "a|b|a"
_lowercase =''''''
_lowercase =''''''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__snake_case ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
_lowercase , _lowercase =0, 0
# length[i] shows the length of palindromic substring with center i
_lowercase =[1 for i in range(len(__snake_case ) )]
# for each character in new_string find corresponding palindromic string
_lowercase =0
for j in range(len(__snake_case ) ):
_lowercase =1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__snake_case )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
_lowercase =2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
_lowercase =j - k + 1 # noqa: E741
_lowercase =j + k - 1
# update max_length and start position
if max_length < length[j]:
_lowercase =length[j]
_lowercase =j
# create that string
_lowercase =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5
|
"""simple docstring"""
from __future__ import annotations
def snake_case_ ( A_ : str ):
'''simple docstring'''
return [ord(A_ ) - 96 for elem in plain]
def snake_case_ ( A_ : list[int] ):
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Dict = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''', A_ )
print('''Decoded:''', decode(A_ ) )
if __name__ == "__main__":
main()
| 72
| 0
|
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
@property
def _a ( self ):
torch.manual_seed(0 )
UpperCamelCase_: Dict = UNetaDModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def _a ( self ):
UpperCamelCase_: Tuple = self.dummy_uncond_unet
UpperCamelCase_: int = PNDMScheduler()
UpperCamelCase_: Any = PNDMPipeline(unet=a_ , scheduler=a_ )
pndm.to(a_ )
pndm.set_progress_bar_config(disable=a_ )
UpperCamelCase_: Union[str, Any] = torch.manual_seed(0 )
UpperCamelCase_: Dict = pndm(generator=a_ , num_inference_steps=2_0 , output_type='numpy' ).images
UpperCamelCase_: Optional[int] = torch.manual_seed(0 )
UpperCamelCase_: Dict = pndm(generator=a_ , num_inference_steps=2_0 , output_type='numpy' , return_dict=a_ )[0]
UpperCamelCase_: str = image[0, -3:, -3:, -1]
UpperCamelCase_: List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
UpperCamelCase_: List[Any] = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
def _a ( self ):
UpperCamelCase_: List[Any] = 'google/ddpm-cifar10-32'
UpperCamelCase_: List[str] = UNetaDModel.from_pretrained(a_ )
UpperCamelCase_: Tuple = PNDMScheduler()
UpperCamelCase_: List[str] = PNDMPipeline(unet=a_ , scheduler=a_ )
pndm.to(a_ )
pndm.set_progress_bar_config(disable=a_ )
UpperCamelCase_: Union[str, Any] = torch.manual_seed(0 )
UpperCamelCase_: Any = pndm(generator=a_ , output_type='numpy' ).images
UpperCamelCase_: Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
UpperCamelCase_: int = np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 359
|
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
while b:
UpperCamelCase_ ,UpperCamelCase_: int = b, a % b
return a
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase__ , a % b )
def snake_case () -> int:
print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}''' )
print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}''' )
print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}''' )
print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}''' )
print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}''' )
print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}''' )
print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}''' )
print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}''' )
print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}''' )
print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}''' )
if __name__ == "__main__":
main()
| 292
| 0
|
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
__A : int = '''src/transformers'''
# Matches is_xxx_available()
__A : Optional[int] = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
__A : Union[str, Any] = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__A : int = re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
__A : Optional[int] = re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
__A : List[Any] = re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__A : Optional[int] = re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
__A : Optional[int] = re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
__A : Union[str, Any] = re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
__A : Union[str, Any] = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
__A : Union[str, Any] = re.compile(R'''^\s*try:''')
# Catches a line with else:
__A : str = re.compile(R'''^\s*else:''')
def lowercase ( __snake_case : int ):
if _re_test_backend.search(__snake_case ) is None:
return None
lowercase_ : Dict = [b[0] for b in _re_backend.findall(__snake_case )]
backends.sort()
return "_and_".join(__snake_case )
def lowercase ( __snake_case : List[Any] ):
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase_ : str = f.readlines()
lowercase_ : str = 0
while line_index < len(__snake_case ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__snake_case ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase_ : Union[str, Any] = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
lowercase_ : List[Any] = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__snake_case ):
lowercase_ : Union[str, Any] = _re_one_line_import_struct.search(__snake_case ).groups()[0]
lowercase_ : int = re.findall('''\[([^\]]+)\]''' , __snake_case )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
lowercase_ : Union[str, Any] = _re_import_struct_key_value.search(__snake_case )
if single_line_import_search is not None:
lowercase_ : Dict = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(__snake_case ) > 0]
objects.extend(__snake_case )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
lowercase_ : Optional[int] = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase_ : Any = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase_ : Any = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase_ : str = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
lowercase_ : Optional[int] = lines[line_index]
if _re_import_struct_add_one.search(__snake_case ) is not None:
objects.append(_re_import_struct_add_one.search(__snake_case ).groups()[0] )
elif _re_import_struct_add_many.search(__snake_case ) is not None:
lowercase_ : int = _re_import_struct_add_many.search(__snake_case ).groups()[0].split(''', ''' )
lowercase_ : Optional[int] = [obj[1:-1] for obj in imports if len(__snake_case ) > 0]
objects.extend(__snake_case )
elif _re_between_brackets.search(__snake_case ) is not None:
lowercase_ : List[Any] = _re_between_brackets.search(__snake_case ).groups()[0].split(''', ''' )
lowercase_ : List[Any] = [obj[1:-1] for obj in imports if len(__snake_case ) > 0]
objects.extend(__snake_case )
elif _re_quote_object.search(__snake_case ) is not None:
objects.append(_re_quote_object.search(__snake_case ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 1_2 + '''"''' ):
objects.append(line[1_3:-3] )
line_index += 1
lowercase_ : str = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase_ : Union[str, Any] = []
while (
line_index < len(__snake_case )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
lowercase_ : str = lines[line_index]
lowercase_ : int = _re_import.search(__snake_case )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase_ : List[Any] = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(__snake_case ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase_ : List[str] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase_ : List[str] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase_ : Any = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
lowercase_ : Union[str, Any] = lines[line_index]
lowercase_ : List[Any] = _re_import.search(__snake_case )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 1_2 ):
objects.append(line[1_2:-2] )
line_index += 1
lowercase_ : Optional[int] = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def lowercase ( __snake_case : Any , __snake_case : List[str] ):
def find_duplicates(__snake_case : Tuple ):
return [k for k, v in collections.Counter(__snake_case ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase_ : List[str] = []
for key in import_dict_objects.keys():
lowercase_ : str = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
lowercase_ : Optional[int] = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase_ : str = '''base imports''' if key == '''none''' else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def lowercase ( ):
lowercase_ : Optional[int] = []
for root, _, files in os.walk(__snake_case ):
if "__init__.py" in files:
lowercase_ : List[str] = os.path.join(__snake_case , '''__init__.py''' )
lowercase_ : Tuple = parse_init(__snake_case )
if objects is not None:
lowercase_ : Optional[int] = analyze_results(*__snake_case )
if len(__snake_case ) > 0:
lowercase_ : Tuple = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('''\n'''.join(__snake_case ) )
if len(__snake_case ) > 0:
raise ValueError('''\n\n'''.join(__snake_case ) )
def lowercase ( ):
lowercase_ : List[Any] = []
for path, directories, files in os.walk(__snake_case ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(__snake_case )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__snake_case ) / folder).glob('''*.py''' ) ) ) == 0:
continue
lowercase_ : List[str] = str((Path(__snake_case ) / folder).relative_to(__snake_case ) )
lowercase_ : int = short_path.replace(os.path.sep , '''.''' )
submodules.append(__snake_case )
for fname in files:
if fname == "__init__.py":
continue
lowercase_ : Optional[Any] = str((Path(__snake_case ) / fname).relative_to(__snake_case ) )
lowercase_ : Optional[int] = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(__snake_case )
return submodules
__A : List[Any] = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
]
def lowercase ( ):
# This is to make sure the transformers module imported is the one in the repo.
lowercase_ : Optional[int] = importlib.util.spec_from_file_location(
'''transformers''' , os.path.join(__snake_case , '''__init__.py''' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
lowercase_ : Union[str, Any] = spec.loader.load_module()
lowercase_ : Optional[int] = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(__snake_case ) > 0:
lowercase_ : str = '''\n'''.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registered in the main init of Transformers:\n'''
F'''{list_of_modules}\n'''
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 33
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, 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_mobilenet_va import MobileNetVaConfig
__A : Union[str, Any] = logging.get_logger(__name__)
# General docstring
__A : Tuple = '''MobileNetV1Config'''
# Base docstring
__A : Union[str, Any] = '''google/mobilenet_v1_1.0_224'''
__A : Union[str, Any] = [1, 1_024, 7, 7]
# Image classification docstring
__A : Optional[Any] = '''google/mobilenet_v1_1.0_224'''
__A : List[Any] = '''tabby, tabby cat'''
__A : Union[str, Any] = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowercase ( __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Dict=None ):
lowercase_ : str = {}
if isinstance(__snake_case , __snake_case ):
lowercase_ : Union[str, Any] = model.mobilenet_va
else:
lowercase_ : Optional[Any] = model
lowercase_ : Union[str, Any] = '''MobilenetV1/Conv2d_0/'''
lowercase_ : Union[str, Any] = backbone.conv_stem.convolution.weight
lowercase_ : Optional[Any] = backbone.conv_stem.normalization.bias
lowercase_ : Union[str, Any] = backbone.conv_stem.normalization.weight
lowercase_ : Any = backbone.conv_stem.normalization.running_mean
lowercase_ : int = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
lowercase_ : Optional[int] = i + 1
lowercase_ : Union[str, Any] = i * 2
lowercase_ : Optional[Any] = backbone.layer[pt_index]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
lowercase_ : str = pointer.convolution.weight
lowercase_ : int = pointer.normalization.bias
lowercase_ : Any = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Union[str, Any] = pointer.normalization.running_var
lowercase_ : Any = backbone.layer[pt_index + 1]
lowercase_ : Union[str, Any] = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
lowercase_ : int = pointer.convolution.weight
lowercase_ : str = pointer.normalization.bias
lowercase_ : Tuple = pointer.normalization.weight
lowercase_ : Dict = pointer.normalization.running_mean
lowercase_ : Any = pointer.normalization.running_var
if isinstance(__snake_case , __snake_case ):
lowercase_ : Optional[Any] = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
lowercase_ : Any = model.classifier.weight
lowercase_ : Optional[int] = model.classifier.bias
return tf_to_pt_map
def lowercase ( __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
lowercase_ : Tuple = tf.train.list_variables(__snake_case )
lowercase_ : int = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
lowercase_ : Optional[Any] = tf.train.load_variable(__snake_case , __snake_case )
lowercase_ : Optional[int] = array
# Build TF to PyTorch weights loading map
lowercase_ : Any = _build_tf_to_pytorch_map(__snake_case , __snake_case , __snake_case )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
lowercase_ : Union[str, Any] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
lowercase_ : Any = np.transpose(__snake_case , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
lowercase_ : Optional[int] = array.squeeze().transpose()
else:
lowercase_ : Optional[int] = np.transpose(__snake_case , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
lowercase_ : str = torch.from_numpy(__snake_case )
tf_weights.pop(__snake_case , __snake_case )
tf_weights.pop(name + '''/RMSProp''' , __snake_case )
tf_weights.pop(name + '''/RMSProp_1''' , __snake_case )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , __snake_case )
logger.info(F'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
return model
def lowercase ( __snake_case : torch.Tensor , __snake_case : nn.Convad ):
lowercase_ , lowercase_ : Optional[int] = features.shape[-2:]
lowercase_ , lowercase_ : str = conv_layer.stride
lowercase_ , lowercase_ : Tuple = conv_layer.kernel_size
if in_height % stride_height == 0:
lowercase_ : Dict = max(kernel_height - stride_height , 0 )
else:
lowercase_ : List[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowercase_ : str = max(kernel_width - stride_width , 0 )
else:
lowercase_ : int = max(kernel_width - (in_width % stride_width) , 0 )
lowercase_ : int = pad_along_width // 2
lowercase_ : Union[str, Any] = pad_along_width - pad_left
lowercase_ : Tuple = pad_along_height // 2
lowercase_ : List[str] = pad_along_height - pad_top
lowercase_ : str = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__snake_case , __snake_case , '''constant''' , 0.0 )
class _UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any] , A : MobileNetVaConfig , A : int , A : int , A : int , A : Optional[int] = 1 , A : Optional[int] = 1 , A : bool = False , A : Optional[bool] = True , A : Optional[bool or str] = True , ) -> None:
super().__init__()
lowercase_ : int = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
lowercase_ : Tuple = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
lowercase_ : int = nn.Convad(
in_channels=A , out_channels=A , kernel_size=A , stride=A , padding=A , groups=A , bias=A , padding_mode='''zeros''' , )
if use_normalization:
lowercase_ : Optional[Any] = nn.BatchNormad(
num_features=A , eps=config.layer_norm_eps , momentum=0.9997 , affine=A , track_running_stats=A , )
else:
lowercase_ : Union[str, Any] = None
if use_activation:
if isinstance(A , A ):
lowercase_ : str = ACTaFN[use_activation]
elif isinstance(config.hidden_act , A ):
lowercase_ : Any = ACTaFN[config.hidden_act]
else:
lowercase_ : Tuple = config.hidden_act
else:
lowercase_ : Tuple = None
def A ( self : str , A : torch.Tensor ) -> torch.Tensor:
if self.config.tf_padding:
lowercase_ : List[Any] = apply_tf_padding(A , self.convolution )
lowercase_ : Optional[int] = self.convolution(A )
if self.normalization is not None:
lowercase_ : Union[str, Any] = self.normalization(A )
if self.activation is not None:
lowercase_ : Optional[int] = self.activation(A )
return features
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : Optional[int] = MobileNetVaConfig
SCREAMING_SNAKE_CASE_ : int = load_tf_weights_in_mobilenet_va
SCREAMING_SNAKE_CASE_ : Optional[Any] = "mobilenet_v1"
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "pixel_values"
SCREAMING_SNAKE_CASE_ : List[str] = False
def A ( self : Any , A : Union[nn.Linear, nn.Convad] ) -> None:
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.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__A : Union[str, Any] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): 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
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : str , A : MobileNetVaConfig , A : bool = True ) -> int:
super().__init__(A )
lowercase_ : Union[str, Any] = config
lowercase_ : List[str] = 32
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
lowercase_ : Union[str, Any] = MobileNetVaConvLayer(
A , in_channels=config.num_channels , out_channels=A , kernel_size=3 , stride=2 , )
lowercase_ : Optional[Any] = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowercase_ : List[Any] = nn.ModuleList()
for i in range(13 ):
lowercase_ : Dict = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowercase_ : str = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=3 , stride=strides[i] , groups=A , ) )
self.layer.append(
MobileNetVaConvLayer(
A , in_channels=A , out_channels=A , kernel_size=1 , ) )
lowercase_ : int = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def A ( self : Any , A : Optional[Any] ) -> Optional[int]:
raise NotImplementedError
@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 A ( self : List[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[bool] = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
lowercase_ : str = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase_ : Union[str, Any] = 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''' )
lowercase_ : List[str] = self.conv_stem(A )
lowercase_ : Dict = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
lowercase_ : Optional[int] = layer_module(A )
if output_hidden_states:
lowercase_ : str = all_hidden_states + (hidden_states,)
lowercase_ : Tuple = hidden_states
if self.pooler is not None:
lowercase_ : Dict = torch.flatten(self.pooler(A ) , start_dim=1 )
else:
lowercase_ : Optional[Any] = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A , pooler_output=A , hidden_states=A , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _A , )
class _UpperCAmelCase ( _A ):
def __init__( self : List[str] , A : MobileNetVaConfig ) -> None:
super().__init__(A )
lowercase_ : int = config.num_labels
lowercase_ : List[str] = MobileNetVaModel(A )
lowercase_ : Union[str, Any] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowercase_ : Tuple = nn.Dropout(config.classifier_dropout_prob , inplace=A )
lowercase_ : int = nn.Linear(A , 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 A ( self : Optional[Any] , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , A : Optional[torch.Tensor] = None , A : Optional[bool] = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
lowercase_ : Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict
lowercase_ : List[Any] = self.mobilenet_va(A , output_hidden_states=A , return_dict=A )
lowercase_ : Union[str, Any] = outputs.pooler_output if return_dict else outputs[1]
lowercase_ : Dict = self.classifier(self.dropout(A ) )
lowercase_ : int = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase_ : List[str] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase_ : Optional[Any] = '''single_label_classification'''
else:
lowercase_ : Tuple = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase_ : str = MSELoss()
if self.num_labels == 1:
lowercase_ : List[str] = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase_ : List[str] = loss_fct(A , A )
elif self.config.problem_type == "single_label_classification":
lowercase_ : List[Any] = CrossEntropyLoss()
lowercase_ : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase_ : str = BCEWithLogitsLoss()
lowercase_ : List[Any] = loss_fct(A , A )
if not return_dict:
lowercase_ : Tuple = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=A , logits=A , hidden_states=outputs.hidden_states , )
| 33
| 1
|
'''simple docstring'''
import string
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
A_ : Optional[int] = """"""
for symbol in message:
if symbol in string.ascii_uppercase:
A_ : List[str] = string.ascii_uppercase.find(a_ )
A_ : List[Any] = num - key
if num < 0:
A_ : List[str] = num + len(string.ascii_uppercase )
A_ : Tuple = translated + string.ascii_uppercase[num]
else:
A_ : List[str] = translated + symbol
print(F"Decryption using Key #{key}: {translated}" )
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
A_ : List[str] = input("""Encrypted message: """ )
A_ : Any = message.upper()
decrypt(a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 352
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : int = {'vocab_file': 'spm_char.model'}
UpperCamelCase__ : Optional[Any] = {
'vocab_file': {
'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
}
}
UpperCamelCase__ : Union[str, Any] = {
'microsoft/speecht5_asr': 1_024,
'microsoft/speecht5_tts': 1_024,
'microsoft/speecht5_vc': 1_024,
}
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = VOCAB_FILES_NAMES
lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCamelCase , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase = None , **_lowerCamelCase , ) -> None:
A_ : str = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
A_ : List[Any] = vocab_file
A_ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@property
def UpperCAmelCase_ ( self ) -> Any:
return self.sp_model.get_piece_size()
def UpperCAmelCase_ ( self ) -> int:
A_ : Dict = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> str:
A_ : Optional[int] = self.__dict__.copy()
A_ : str = None
return state
def __setstate__( self , _lowerCamelCase ) -> List[str]:
A_ : int = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
A_ : Union[str, Any] = {}
A_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> List[str]:
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> List[str]:
return self.sp_model.piece_to_id(_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> List[str]:
A_ : Dict = self.sp_model.IdToPiece(_lowerCamelCase )
return token
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Union[str, Any]:
A_ : Tuple = []
A_ : Union[str, Any] = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCamelCase ) + token
A_ : Optional[int] = []
else:
current_sub_tokens.append(_lowerCamelCase )
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
A_ : Union[str, Any] = [1]
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + suffix_ones
return ([0] * len(_lowerCamelCase )) + ([0] * len(_lowerCamelCase )) + suffix_ones
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase = None ) -> Tuple[str]:
if not os.path.isdir(_lowerCamelCase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
A_ : Optional[int] = os.path.join(
_lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , """wb""" ) as fi:
A_ : List[str] = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 164
| 0
|
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , _A , _A=7 , _A=3 , _A=30 , _A=400 , _A=True , _A=None , _A=True , _A=[0.5, 0.5, 0.5] , _A=[0.5, 0.5, 0.5] , _A=True , _A=1 / 255 , _A=True , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = min_resolution
__SCREAMING_SNAKE_CASE = max_resolution
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = size
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean
__SCREAMING_SNAKE_CASE = image_std
__SCREAMING_SNAKE_CASE = do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor
__SCREAMING_SNAKE_CASE = do_pad
def _A ( self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _A ( self , _A , _A=False ):
'''simple docstring'''
if not batched:
__SCREAMING_SNAKE_CASE = image_inputs[0]
if isinstance(_A , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE = int(self.size['shortest_edge'] * h / w )
__SCREAMING_SNAKE_CASE = self.size['shortest_edge']
elif w > h:
__SCREAMING_SNAKE_CASE = self.size['shortest_edge']
__SCREAMING_SNAKE_CASE = int(self.size['shortest_edge'] * w / h )
else:
__SCREAMING_SNAKE_CASE = self.size['shortest_edge']
__SCREAMING_SNAKE_CASE = self.size['shortest_edge']
else:
__SCREAMING_SNAKE_CASE = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE = max(_A , key=lambda _A : item[0] )[0]
__SCREAMING_SNAKE_CASE = max(_A , key=lambda _A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCAmelCase_ ( UpperCamelCase_ , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = ConditionalDetrImageProcessor if is_vision_available() else None
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = ConditionalDetrImageProcessingTester(self )
@property
def _A ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , 'image_mean' ) )
self.assertTrue(hasattr(_A , 'image_std' ) )
self.assertTrue(hasattr(_A , 'do_normalize' ) )
self.assertTrue(hasattr(_A , 'do_resize' ) )
self.assertTrue(hasattr(_A , 'size' ) )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1_333} )
self.assertEqual(image_processor.do_pad , _A )
__SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_A )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , _A )
def _A ( self ):
'''simple docstring'''
pass
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(_A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(_A , batched=_A )
__SCREAMING_SNAKE_CASE = 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,
expected_height,
expected_width,
) , )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , numpify=_A )
for image in image_inputs:
self.assertIsInstance(_A , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(_A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(_A , return_tensors='pt' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(_A , batched=_A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(_A )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(_A , return_tensors='pt' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(_A , batched=_A )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
__SCREAMING_SNAKE_CASE = json.loads(f.read() )
__SCREAMING_SNAKE_CASE = {'image_id': 39_769, 'annotations': target}
# encode them
__SCREAMING_SNAKE_CASE = ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50' )
__SCREAMING_SNAKE_CASE = image_processing(images=_A , annotations=_A , return_tensors='pt' )
# verify pixel values
__SCREAMING_SNAKE_CASE = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['pixel_values'].shape , _A )
__SCREAMING_SNAKE_CASE = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _A , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _A ) )
# verify boxes
__SCREAMING_SNAKE_CASE = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _A )
__SCREAMING_SNAKE_CASE = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _A , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _A ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _A ) )
# verify class_labels
__SCREAMING_SNAKE_CASE = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _A ) )
# verify orig_size
__SCREAMING_SNAKE_CASE = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _A ) )
# verify size
__SCREAMING_SNAKE_CASE = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _A ) )
@slow
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
__SCREAMING_SNAKE_CASE = json.loads(f.read() )
__SCREAMING_SNAKE_CASE = {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}
__SCREAMING_SNAKE_CASE = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
__SCREAMING_SNAKE_CASE = ConditionalDetrImageProcessor(format='coco_panoptic' )
__SCREAMING_SNAKE_CASE = image_processing(images=_A , annotations=_A , masks_path=_A , return_tensors='pt' )
# verify pixel values
__SCREAMING_SNAKE_CASE = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['pixel_values'].shape , _A )
__SCREAMING_SNAKE_CASE = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _A , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _A ) )
# verify boxes
__SCREAMING_SNAKE_CASE = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _A )
__SCREAMING_SNAKE_CASE = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _A , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _A ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _A ) )
# verify class_labels
__SCREAMING_SNAKE_CASE = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _A ) )
# verify masks
__SCREAMING_SNAKE_CASE = 822_873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , _A )
# verify orig_size
__SCREAMING_SNAKE_CASE = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _A ) )
# verify size
__SCREAMING_SNAKE_CASE = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _A ) )
| 257
|
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
lowerCAmelCase__ : Optional[int] =getLogger(__name__)
lowerCAmelCase__ : List[str] ='''cuda''' if torch.cuda.is_available() else '''cpu'''
def __lowercase ( a__ , a__ , a__ , a__ = 8 , a__ = DEFAULT_DEVICE , a__=False , a__="summarization" , a__=None , **a__ , ) -> Dict:
__SCREAMING_SNAKE_CASE = Path(a__ ).open('w' , encoding='utf-8' )
__SCREAMING_SNAKE_CASE = str(a__ )
__SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(a__ ).to(a__ )
if fpaa:
__SCREAMING_SNAKE_CASE = model.half()
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(a__ )
logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
__SCREAMING_SNAKE_CASE = time.time()
# update config with task specific params
use_task_specific_params(a__ , a__ )
if prefix is None:
__SCREAMING_SNAKE_CASE = prefix or getattr(model.config , 'prefix' , '' ) or ''
for examples_chunk in tqdm(list(chunks(a__ , a__ ) ) ):
__SCREAMING_SNAKE_CASE = [prefix + text for text in examples_chunk]
__SCREAMING_SNAKE_CASE = tokenizer(a__ , return_tensors='pt' , truncation=a__ , padding='longest' ).to(a__ )
__SCREAMING_SNAKE_CASE = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **a__ , )
__SCREAMING_SNAKE_CASE = tokenizer.batch_decode(a__ , skip_special_tokens=a__ , clean_up_tokenization_spaces=a__ )
for hypothesis in dec:
fout.write(hypothesis + '\n' )
fout.flush()
fout.close()
__SCREAMING_SNAKE_CASE = int(time.time() - start_time ) # seconds
__SCREAMING_SNAKE_CASE = len(a__ )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def __lowercase ( ) -> Any:
return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )
def __lowercase ( a__=True ) -> int:
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('model_name' , type=a__ , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('input_path' , type=a__ , help='like cnn_dm/test.source' )
parser.add_argument('save_path' , type=a__ , help='where to save summaries' )
parser.add_argument('--reference_path' , type=a__ , required=a__ , help='like cnn_dm/test.target' )
parser.add_argument('--score_path' , type=a__ , required=a__ , default='metrics.json' , help='where to save metrics' )
parser.add_argument('--device' , type=a__ , required=a__ , default=a__ , help='cuda, cuda:1, cpu etc.' )
parser.add_argument(
'--prefix' , type=a__ , required=a__ , default=a__ , help='will be added to the begininng of src examples' )
parser.add_argument('--task' , type=a__ , default='summarization' , help='used for task_specific_params + metrics' )
parser.add_argument('--bs' , type=a__ , default=8 , required=a__ , help='batch size' )
parser.add_argument(
'--n_obs' , type=a__ , default=-1 , required=a__ , help='How many observations. Defaults to all.' )
parser.add_argument('--fp16' , action='store_true' )
parser.add_argument('--dump-args' , action='store_true' , help='print the custom hparams with the results' )
parser.add_argument(
'--info' , nargs='?' , type=a__ , const=datetime_now() , help=(
'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
' lang=en-ru. If no value is passed, the current datetime string will be used.'
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = parser.parse_known_args()
__SCREAMING_SNAKE_CASE = parse_numeric_n_bool_cl_kwargs(a__ )
if parsed_args and verbose:
print(f"""parsed the following generate kwargs: {parsed_args}""" )
__SCREAMING_SNAKE_CASE = [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
__SCREAMING_SNAKE_CASE = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=a__ )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('Can\'t mix --fp16 and --device cpu' )
__SCREAMING_SNAKE_CASE = generate_summaries_or_translations(
a__ , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **a__ , )
if args.reference_path is None:
return {}
# Compute scores
__SCREAMING_SNAKE_CASE = calculate_bleu if 'translation' in args.task else calculate_rouge
__SCREAMING_SNAKE_CASE = [x.rstrip() for x in open(args.save_path ).readlines()]
__SCREAMING_SNAKE_CASE = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(a__ )]
__SCREAMING_SNAKE_CASE = score_fn(a__ , a__ )
scores.update(a__ )
if args.dump_args:
scores.update(a__ )
if args.info:
__SCREAMING_SNAKE_CASE = args.info
if verbose:
print(a__ )
if args.score_path is not None:
json.dump(a__ , open(args.score_path , 'w' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 257
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|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : str , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any]):
'''simple docstring'''
warnings.warn(
'''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use OwlViTImageProcessor instead.''' , lowercase_ , )
super().__init__(*lowercase_ , **lowercase_)
| 318
|
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
UpperCAmelCase_ : List[Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""
UpperCAmelCase_ : Optional[int] = """
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results['pearsonr'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
['p-value', 'pearsonr']
>>> print(round(results['pearsonr'], 2))
-0.74
>>> print(round(results['p-value'], 2))
0.15
"""
UpperCAmelCase_ : Tuple = """
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float'''),
'''references''': datasets.Value('''float'''),
}) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'''] , )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Union[str, Any]=False):
'''simple docstring'''
if return_pvalue:
SCREAMING_SNAKE_CASE_ : int = pearsonr(lowercase_ , lowercase_)
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(lowercase_ , lowercase_)[0])}
| 318
| 1
|
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class A_ :
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be trained.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''./''' , metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot-clean-train''' , metadata={'''help''': '''Name or path of training dataset.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot-clean-valid''' , metadata={'''help''': '''Name or path of validation dataset.'''} )
_UpperCAmelCase : Optional[int] = field(default=2 , metadata={'''help''': '''Batch size for training.'''} )
_UpperCAmelCase : Optional[int] = field(default=2 , metadata={'''help''': '''Batch size for evaluation.'''} )
_UpperCAmelCase : Optional[float] = field(default=0.1 , metadata={'''help''': '''Value of weight decay.'''} )
_UpperCAmelCase : Optional[int] = field(
default=10_000 , metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''} )
_UpperCAmelCase : Optional[float] = field(default=2E-4 , metadata={'''help''': '''Learning rate fo training.'''} )
_UpperCAmelCase : Optional[str] = field(default='''cosine''' , metadata={'''help''': '''Learning rate.'''} )
_UpperCAmelCase : Optional[int] = field(
default=750 , metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''} )
_UpperCAmelCase : Optional[int] = field(
default=16 , metadata={'''help''': '''Number of gradient accumulation steps.'''} )
_UpperCAmelCase : Optional[bool] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''} )
_UpperCAmelCase : Optional[int] = field(default=50_000 , metadata={'''help''': '''Maximum number of training steps.'''} )
_UpperCAmelCase : Optional[int] = field(
default=-1 , metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''} )
_UpperCAmelCase : Optional[int] = field(default=1_024 , metadata={'''help''': '''Sequence lengths used for training.'''} )
_UpperCAmelCase : Optional[int] = field(default=1 , metadata={'''help''': '''Training seed.'''} )
_UpperCAmelCase : Optional[int] = field(
default=1_024 , metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''} , )
_UpperCAmelCase : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''} )
_UpperCAmelCase : Optional[bool] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''If True the data is pretokenized.'''} )
@dataclass
class A_ :
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be evaluated.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot-clean-valid''' , metadata={'''help''': '''Name or path of validation dataset.'''} )
_UpperCAmelCase : Optional[int] = field(default=2 , metadata={'''help''': '''Batch size used for evaluation.'''} )
_UpperCAmelCase : Optional[int] = field(
default=-1 , metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''} )
_UpperCAmelCase : Optional[int] = field(default=1_024 , metadata={'''help''': '''Length of sequences to be evaluated.'''} )
_UpperCAmelCase : Optional[int] = field(default=1 , metadata={'''help''': '''Random seed used for evaluation.'''} )
@dataclass
class A_ :
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be evaluated.'''} )
_UpperCAmelCase : Optional[int] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Number of workers used for code evaluation.'''} )
_UpperCAmelCase : Optional[int] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''} , )
_UpperCAmelCase : Optional[bool] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Sample from the language model\'s output distribution.'''} )
_UpperCAmelCase : Optional[float] = field(default=0.2 , metadata={'''help''': '''Sampling temperature used for generation.'''} )
_UpperCAmelCase : Optional[int] = field(default=256 , metadata={'''help''': '''Maximum number of newly generated tokens.'''} )
_UpperCAmelCase : Optional[int] = field(default=0 , metadata={'''help''': '''Top-k parameter used for generation.'''} )
_UpperCAmelCase : Optional[float] = field(default=0.95 , metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''} )
_UpperCAmelCase : Optional[int] = field(default=10 , metadata={'''help''': '''Number of generations to run in parallel.'''} )
_UpperCAmelCase : Optional[int] = field(
default=200 , metadata={'''help''': '''Number of completions to generate for each sample.'''} )
_UpperCAmelCase : Optional[int] = field(default=1 , metadata={'''help''': '''Random seed used for evaluation.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''eval_results.json''' , metadata={'''help''': '''Random seed used for evaluation.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''0''' , metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''} )
_UpperCAmelCase : Optional[int] = field(
default=-1 , metadata={
'''help''': (
'''Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'''
''' number corresponds to which GPU device id to run on.'''
)
} , )
@dataclass
class A_ :
_UpperCAmelCase : Optional[int] = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
} , )
_UpperCAmelCase : Optional[str] = field(
default='''transformersbook/codeparrot''' , metadata={'''help''': '''Folder or name of dataset to process.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot-clean''' , metadata={'''help''': '''Folder to save processed processed dataset.'''} )
_UpperCAmelCase : Optional[int] = field(
default=100_000 , metadata={'''help''': '''Number of files to save per JSON output file.'''} )
_UpperCAmelCase : Optional[str] = field(default='''content''' , metadata={'''help''': '''Column containing text data to process.'''} )
_UpperCAmelCase : Optional[float] = field(
default=1_000 , metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''} )
_UpperCAmelCase : Optional[float] = field(
default=100 , metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''} )
_UpperCAmelCase : Optional[float] = field(
default=0.25 , metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''} )
_UpperCAmelCase : Optional[float] = field(
default=1.5 , metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''} )
_UpperCAmelCase : Optional[float] = field(
default=0.7 , metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Name or path to the tokenizer.'''} , )
_UpperCAmelCase : Optional[bool] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''If True, near-duplicate samples are removed.'''} )
_UpperCAmelCase : Optional[float] = field(
default=0.85 , metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''} )
@dataclass
class A_ :
_UpperCAmelCase : Optional[str] = field(
default='''gpt2''' , metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''transformersbook/codeparrot-train''' , metadata={'''help''': '''Dataset to train tokenizer on.'''} )
_UpperCAmelCase : Optional[str] = field(default='''content''' , metadata={'''help''': '''Column containing text data to process.'''} )
_UpperCAmelCase : Optional[int] = field(default=200_000 , metadata={'''help''': '''Number of examples to train tokenizer on.'''} )
_UpperCAmelCase : Optional[int] = field(
default=32_768 , metadata={'''help''': '''Number of examples to train the tokenizer on.'''} )
_UpperCAmelCase : Optional[str] = field(default='''codeparrot''' , metadata={'''help''': '''Name of new tokenizer.'''} )
_UpperCAmelCase : Optional[bool] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Push saved tokenizer to the hub.'''} )
@dataclass
class A_ :
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Name or path to the tokenizer.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot-clean-train''' , metadata={'''help''': '''Name or path to the dataset to pretokenize.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''tokenized-codeparrot-train''' , metadata={'''help''': '''Repo name of the pretokenized data.'''} )
_UpperCAmelCase : Optional[int] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Number of workers used for code evaluation.'''} )
@dataclass
class A_ :
_UpperCAmelCase : Optional[str] = field(
default='''gpt2-large''' , metadata={'''help''': '''Configuration to use for model initialization.'''} )
_UpperCAmelCase : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Tokenizer attached to model.'''} )
_UpperCAmelCase : Optional[str] = field(default='''codeparrot''' , metadata={'''help''': '''Name of the created model.'''} )
_UpperCAmelCase : Optional[bool] = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Push saved tokenizer to the hub.'''} )
| 73
|
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowerCamelCase : Any = re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase : str = re.compile(R'([a-z\d])([A-Z])')
lowerCamelCase : Optional[int] = re.compile(R'(?<!_)_(?!_)')
lowerCamelCase : List[Any] = re.compile(R'(_{2,})')
lowerCamelCase : str = R'^\w+(\.\w+)*$'
lowerCamelCase : Dict = R'<>:/\|?*'
def _SCREAMING_SNAKE_CASE (A ) -> Any:
"""simple docstring"""
lowercase__ = _uppercase_uppercase_re.sub(R'''\1_\2''' , A )
lowercase__ = _lowercase_uppercase_re.sub(R'''\1_\2''' , A )
return name.lower()
def _SCREAMING_SNAKE_CASE (A ) -> Tuple:
"""simple docstring"""
lowercase__ = _single_underscore_re.split(A )
lowercase__ = [_multiple_underscores_re.split(A ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(A ) if n != '''''' )
def _SCREAMING_SNAKE_CASE (A ) -> Tuple:
"""simple docstring"""
if os.path.basename(A ) != name:
raise ValueError(f"Should be a dataset name, not a path: {name}" )
return camelcase_to_snakecase(A )
def _SCREAMING_SNAKE_CASE (A , A ) -> Optional[Any]:
"""simple docstring"""
if os.path.basename(A ) != name:
raise ValueError(f"Should be a dataset name, not a path: {name}" )
if not re.match(_split_re , A ):
raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'." )
return f"{filename_prefix_for_name(A )}-{split}"
def _SCREAMING_SNAKE_CASE (A , A , A , A=None ) -> List[str]:
"""simple docstring"""
lowercase__ = filename_prefix_for_split(A , A )
if filetype_suffix:
prefix += f".{filetype_suffix}"
lowercase__ = os.path.join(A , A )
return f"{filepath}*"
def _SCREAMING_SNAKE_CASE (A , A , A , A=None , A=None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = filename_prefix_for_split(A , A )
lowercase__ = os.path.join(A , A )
if shard_lengths:
lowercase__ = len(A )
lowercase__ = [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(A )]
if filetype_suffix:
lowercase__ = [filename + f".{filetype_suffix}" for filename in filenames]
return filenames
else:
lowercase__ = prefix
if filetype_suffix:
filename += f".{filetype_suffix}"
return [filename]
| 2
| 0
|
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-canny' , from_pt=__lowercase , dtype=jnp.bfloataa)
SCREAMING_SNAKE_CASE = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=__lowercase , from_pt=__lowercase , dtype=jnp.bfloataa)
SCREAMING_SNAKE_CASE = controlnet_params
SCREAMING_SNAKE_CASE = '''bird'''
SCREAMING_SNAKE_CASE = jax.device_count()
SCREAMING_SNAKE_CASE = pipe.prepare_text_inputs([prompts] * num_samples)
SCREAMING_SNAKE_CASE = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png')
SCREAMING_SNAKE_CASE = pipe.prepare_image_inputs([canny_image] * num_samples)
SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0)
SCREAMING_SNAKE_CASE = jax.random.split(__lowercase , jax.device_count())
SCREAMING_SNAKE_CASE = replicate(__lowercase)
SCREAMING_SNAKE_CASE = shard(__lowercase)
SCREAMING_SNAKE_CASE = shard(__lowercase)
SCREAMING_SNAKE_CASE = pipe(
prompt_ids=__lowercase , image=__lowercase , params=__lowercase , prng_seed=__lowercase , num_inference_steps=50 , jit=__lowercase , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
SCREAMING_SNAKE_CASE = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
SCREAMING_SNAKE_CASE = images[0, 253:256, 253:256, -1]
SCREAMING_SNAKE_CASE = jnp.asarray(jax.device_get(image_slice.flatten()))
SCREAMING_SNAKE_CASE = jnp.array(
[0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78])
print(f'''output_slice: {output_slice}''')
assert jnp.abs(output_slice - expected_slice).max() < 1E-2
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
SCREAMING_SNAKE_CASE = FlaxControlNetModel.from_pretrained(
'lllyasviel/sd-controlnet-openpose' , from_pt=__lowercase , dtype=jnp.bfloataa)
SCREAMING_SNAKE_CASE = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , controlnet=__lowercase , from_pt=__lowercase , dtype=jnp.bfloataa)
SCREAMING_SNAKE_CASE = controlnet_params
SCREAMING_SNAKE_CASE = '''Chef in the kitchen'''
SCREAMING_SNAKE_CASE = jax.device_count()
SCREAMING_SNAKE_CASE = pipe.prepare_text_inputs([prompts] * num_samples)
SCREAMING_SNAKE_CASE = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png')
SCREAMING_SNAKE_CASE = pipe.prepare_image_inputs([pose_image] * num_samples)
SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0)
SCREAMING_SNAKE_CASE = jax.random.split(__lowercase , jax.device_count())
SCREAMING_SNAKE_CASE = replicate(__lowercase)
SCREAMING_SNAKE_CASE = shard(__lowercase)
SCREAMING_SNAKE_CASE = shard(__lowercase)
SCREAMING_SNAKE_CASE = pipe(
prompt_ids=__lowercase , image=__lowercase , params=__lowercase , prng_seed=__lowercase , num_inference_steps=50 , jit=__lowercase , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
SCREAMING_SNAKE_CASE = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
SCREAMING_SNAKE_CASE = images[0, 253:256, 253:256, -1]
SCREAMING_SNAKE_CASE = jnp.asarray(jax.device_get(image_slice.flatten()))
SCREAMING_SNAKE_CASE = jnp.array(
[[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]])
print(f'''output_slice: {output_slice}''')
assert jnp.abs(output_slice - expected_slice).max() < 1E-2
| 367
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_efficientnet': [
'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientNetConfig',
'EfficientNetOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[str] = ['EfficientNetImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Union[str, Any] = [
'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientNetForImageClassification',
'EfficientNetModel',
'EfficientNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
a_ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 327
| 0
|
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
a : Tuple = '''sshleifer/mar_enro_6_3_student'''
class __UpperCamelCase ( a__ ):
def __a ( self ) -> Optional[Any]:
super().setUp()
a : Any = cached_path(
"https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz" , extract_compressed_file=lowerCAmelCase__ , )
a : List[Any] = f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""
@slow
@require_torch_gpu
def __a ( self ) -> List[Any]:
MarianMTModel.from_pretrained(lowerCAmelCase__ )
@slow
@require_torch_gpu
def __a ( self ) -> List[str]:
a : Optional[int] = {
"$MAX_LEN": 64,
"$BS": 64,
"$GAS": 1,
"$ENRO_DIR": self.data_dir,
"facebook/mbart-large-cc25": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"--learning_rate=3e-5": "--learning_rate 3e-4",
"--num_train_epochs 6": "--num_train_epochs 1",
}
# Clean up bash script
a : str = (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py" )[1].strip()
a : int = bash_script.replace("\\\n" , "" ).strip().replace("\"$@\"" , "" )
for k, v in env_vars_to_replace.items():
a : int = bash_script.replace(lowerCAmelCase__ , str(lowerCAmelCase__ ) )
a : Any = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
a : List[str] = f"""
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
""".split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
a : Dict = ["finetune.py"] + bash_script.split() + args
with patch.object(lowerCAmelCase__ , "argv" , lowerCAmelCase__ ):
a : List[Any] = argparse.ArgumentParser()
a : Dict = pl.Trainer.add_argparse_args(lowerCAmelCase__ )
a : List[str] = SummarizationModule.add_model_specific_args(lowerCAmelCase__ , os.getcwd() )
a : str = parser.parse_args()
a : Union[str, Any] = main(lowerCAmelCase__ )
# Check metrics
a : List[str] = load_json(model.metrics_save_path )
a : Optional[int] = metrics["val"][0]
a : Dict = metrics["val"][-1]
self.assertEqual(len(metrics["val"] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""] , lowerCAmelCase__ )
self.assertGreater(last_step_stats["val_avg_gen_time"] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["val_avg_gen_time"] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["val_avg_bleu"] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
a : int = os.listdir(lowerCAmelCase__ )
a : Tuple = [x for x in contents if x.endswith(".ckpt" )][0]
a : Optional[Any] = os.path.join(args.output_dir , lowerCAmelCase__ )
a : Any = torch.load(lowerCAmelCase__ , map_location="cpu" )
a : Dict = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
a : Dict = {os.path.basename(lowerCAmelCase__ ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["test"] ) == 1
class __UpperCamelCase ( a__ ):
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def __a ( self ) -> Union[str, Any]:
a : int = f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
a : Optional[Any] = {
"--fp16_opt_level=O1": "",
"$MAX_LEN": 128,
"$BS": 16,
"$GAS": 1,
"$ENRO_DIR": data_dir,
"$m": "sshleifer/student_marian_en_ro_6_1",
"val_check_interval=0.25": "val_check_interval=1.0",
}
# Clean up bash script
a : Any = (
(self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py" )[1].strip()
)
a : Union[str, Any] = bash_script.replace("\\\n" , "" ).strip().replace("\"$@\"" , "" )
a : Any = bash_script.replace("--fp16 " , " " )
for k, v in env_vars_to_replace.items():
a : Dict = bash_script.replace(lowerCAmelCase__ , str(lowerCAmelCase__ ) )
a : int = self.get_auto_remove_tmp_dir()
a : Union[str, Any] = bash_script.replace("--fp16" , "" )
a : Optional[int] = 6
a : str = (
["distillation.py"]
+ bash_script.split()
+ [
f"""--output_dir={output_dir}""",
"--gpus=1",
"--learning_rate=1e-3",
f"""--num_train_epochs={epochs}""",
"--warmup_steps=10",
"--val_check_interval=1.0",
"--do_predict",
]
)
with patch.object(lowerCAmelCase__ , "argv" , lowerCAmelCase__ ):
a : int = argparse.ArgumentParser()
a : Optional[int] = pl.Trainer.add_argparse_args(lowerCAmelCase__ )
a : Tuple = SummarizationDistiller.add_model_specific_args(lowerCAmelCase__ , os.getcwd() )
a : List[str] = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
a : Optional[int] = distill_main(lowerCAmelCase__ )
# Check metrics
a : Tuple = load_json(model.metrics_save_path )
a : Union[str, Any] = metrics["val"][0]
a : List[Any] = metrics["val"][-1]
assert len(metrics["val"] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""] , lowerCAmelCase__ )
# check lightning ckpt can be loaded and has a reasonable statedict
a : List[str] = os.listdir(lowerCAmelCase__ )
a : Optional[Any] = [x for x in contents if x.endswith(".ckpt" )][0]
a : Optional[int] = os.path.join(args.output_dir , lowerCAmelCase__ )
a : Optional[Any] = torch.load(lowerCAmelCase__ , map_location="cpu" )
a : int = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
a : Optional[int] = {os.path.basename(lowerCAmelCase__ ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["test"] ) == 1
| 105
|
from datetime import datetime as dt
import os
from github import Github
__A : Dict = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def SCREAMING_SNAKE_CASE__ ( ) -> int:
'''simple docstring'''
lowerCAmelCase : Optional[Any] = Github(os.environ['GITHUB_TOKEN'] )
lowerCAmelCase : List[str] = g.get_repo('huggingface/transformers' )
lowerCAmelCase : Tuple = repo.get_issues(state='open' )
for issue in open_issues:
lowerCAmelCase : Dict = sorted([comment for comment in issue.get_comments()], key=lambda _UpperCAmelCase : i.created_at, reverse=_UpperCAmelCase )
lowerCAmelCase : Optional[Any] = comments[0] if len(_UpperCAmelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='closed' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 138
| 0
|
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class UpperCAmelCase__ ( UpperCAmelCase_ , unittest.TestCase):
__SCREAMING_SNAKE_CASE = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def __lowerCamelCase ( self , lowercase=0 ) -> Tuple:
__UpperCamelCase = floats_tensor((1, 3, 1_2_8, 1_2_8) , rng=random.Random(lowercase ) )
__UpperCamelCase = np.random.RandomState(lowercase )
__UpperCamelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""strength""": 0.75,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __lowerCamelCase ( self ) -> Optional[Any]:
__UpperCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = self.get_dummy_inputs()
__UpperCamelCase = pipe(**lowercase ).images
__UpperCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__UpperCamelCase = np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def __lowerCamelCase ( self ) -> Optional[Any]:
__UpperCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__UpperCamelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowercase )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = self.get_dummy_inputs()
__UpperCamelCase = pipe(**lowercase ).images
__UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__UpperCamelCase = np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCamelCase ( self ) -> Tuple:
__UpperCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__UpperCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowercase )
# warmup pass to apply optimizations
__UpperCamelCase = pipe(**self.get_dummy_inputs() )
__UpperCamelCase = self.get_dummy_inputs()
__UpperCamelCase = pipe(**lowercase ).images
__UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__UpperCamelCase = np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__UpperCamelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = self.get_dummy_inputs()
__UpperCamelCase = pipe(**lowercase ).images
__UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__UpperCamelCase = np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__UpperCamelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = self.get_dummy_inputs()
__UpperCamelCase = pipe(**lowercase ).images
__UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__UpperCamelCase = np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCamelCase ( self ) -> int:
__UpperCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__UpperCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = self.get_dummy_inputs()
__UpperCamelCase = pipe(**lowercase ).images
__UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
__UpperCamelCase = np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase):
@property
def __lowerCamelCase ( self ) -> int:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __lowerCamelCase ( self ) -> List[str]:
__UpperCamelCase = ort.SessionOptions()
__UpperCamelCase = False
return options
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
__UpperCamelCase = init_image.resize((7_6_8, 5_1_2) )
# using the PNDM scheduler by default
__UpperCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=lowercase , feature_extractor=lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = """A fantasy landscape, trending on artstation"""
__UpperCamelCase = np.random.RandomState(0 )
__UpperCamelCase = pipe(
prompt=lowercase , image=lowercase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=1_0 , generator=lowercase , output_type="""np""" , )
__UpperCamelCase = output.images
__UpperCamelCase = images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert images.shape == (1, 5_1_2, 7_6_8, 3)
__UpperCamelCase = np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def __lowerCamelCase ( self ) -> Optional[Any]:
__UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
__UpperCamelCase = init_image.resize((7_6_8, 5_1_2) )
__UpperCamelCase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__UpperCamelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=lowercase , safety_checker=lowercase , feature_extractor=lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase )
__UpperCamelCase = """A fantasy landscape, trending on artstation"""
__UpperCamelCase = np.random.RandomState(0 )
__UpperCamelCase = pipe(
prompt=lowercase , image=lowercase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=2_0 , generator=lowercase , output_type="""np""" , )
__UpperCamelCase = output.images
__UpperCamelCase = images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert images.shape == (1, 5_1_2, 7_6_8, 3)
__UpperCamelCase = np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 366
|
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
a__ : Dict = ''
a__ : List[str] = ''
a__ : Optional[Any] = ''
a__ : Any = ''
def _lowercase ( __A ):
'''simple docstring'''
__UpperCamelCase = tweepy.OAuthHandler(__A ,__A )
auth.set_access_token(__A ,__A )
__UpperCamelCase = tweepy.API(__A )
# initialize a list to hold all the tweepy Tweets
__UpperCamelCase = []
# make initial request for most recent tweets (200 is the maximum allowed count)
__UpperCamelCase = api.user_timeline(screen_name=__A ,count=200 )
# save most recent tweets
alltweets.extend(__A )
# save the id of the oldest tweet less one
__UpperCamelCase = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__A ) > 0:
print(f"getting tweets before {oldest}" )
# all subsequent requests use the max_id param to prevent duplicates
__UpperCamelCase = api.user_timeline(
screen_name=__A ,count=200 ,max_id=__A )
# save most recent tweets
alltweets.extend(__A )
# update the id of the oldest tweet less one
__UpperCamelCase = alltweets[-1].id - 1
print(f"...{len(__A )} tweets downloaded so far" )
# transform the tweepy tweets into a 2D array that will populate the csv
__UpperCamelCase = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(f"new_{screen_name}_tweets.csv" ,"""w""" ) as f:
__UpperCamelCase = csv.writer(__A )
writer.writerow(["""id""", """created_at""", """text"""] )
writer.writerows(__A )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('FirePing32')
| 243
| 0
|
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def UpperCamelCase_ ( *snake_case_ : Dict ) -> Any:
'''simple docstring'''
if not isinstance(snake_case_ , snake_case_ ):
__lowerCAmelCase = list(snake_case_ )
for i in range(len(snake_case_ ) ):
__lowerCAmelCase = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def UpperCamelCase_ ( snake_case_ : Exception ) -> bool:
'''simple docstring'''
__lowerCAmelCase = [
"""CUDA out of memory.""", # CUDA OOM
"""cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""", # CUDNN SNAFU
"""DefaultCPUAllocator: can't allocate memory""", # CPU OOM
]
if isinstance(snake_case_ , snake_case_ ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def UpperCamelCase_ ( snake_case_ : callable = None , snake_case_ : int = 1_28 ) -> List[Any]:
'''simple docstring'''
if function is None:
return functools.partial(snake_case_ , starting_batch_size=snake_case_ )
__lowerCAmelCase = starting_batch_size
def decorator(*snake_case_ : List[str] , **snake_case_ : Union[str, Any] ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
__lowerCAmelCase = list(inspect.signature(snake_case_ ).parameters.keys() )
# Guard against user error
if len(snake_case_ ) < (len(snake_case_ ) + 1):
__lowerCAmelCase = """, """.join([f"""{arg}={value}""" for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
f"""Batch size was passed into `{function.__name__}` as the first argument when called."""
f"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`""" )
while True:
if batch_size == 0:
raise RuntimeError("""No executable batch size found, reached zero.""" )
try:
return function(snake_case_ , *snake_case_ , **snake_case_ )
except Exception as e:
if should_reduce_batch_size(snake_case_ ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 229
|
'''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__lowerCAmelCase = str(bin(snake_case_ ) )[2:] # remove the leading "0b"
__lowerCAmelCase = str(bin(snake_case_ ) )[2:] # remove the leading "0b"
__lowerCAmelCase = max(len(snake_case_ ) , len(snake_case_ ) )
return "0b" + "".join(
str(int(char_a == """1""" and char_b == """1""" ) )
for char_a, char_b in zip(a_binary.zfill(snake_case_ ) , b_binary.zfill(snake_case_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 229
| 1
|
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = 0
@slow
def lowercase_ ( self : Dict ):
'''simple docstring'''
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
self.assertGreater(len(_A ) , 0 )
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , (GPTaTokenizer, GPTaTokenizerFast) )
self.assertGreater(len(_A ) , 0 )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , (RobertaTokenizer, RobertaTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 20 )
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = AutoConfig.from_pretrained(_A )
self.assertIsInstance(_A , _A )
# Check that tokenizer_type ≠ model_type
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained(_A , config=_A )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def lowercase_ ( self : str ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.txt''' , os.path.join(_A , '''vocab.txt''' ) )
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained(_A , tokenizer_type='''bert''' , use_fast=_A )
self.assertIsInstance(_A , _A )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.json''' , os.path.join(_A , '''vocab.json''' ) )
shutil.copy('''./tests/fixtures/merges.txt''' , os.path.join(_A , '''merges.txt''' ) )
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(_A , tokenizer_type='''gpt2''' , use_fast=_A )
self.assertIsInstance(_A , _A )
@require_tokenizers
def lowercase_ ( self : str ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.txt''' , os.path.join(_A , '''vocab.txt''' ) )
UpperCAmelCase__ : str = AutoTokenizer.from_pretrained(_A , tokenizer_type='''bert''' )
self.assertIsInstance(_A , _A )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.json''' , os.path.join(_A , '''vocab.json''' ) )
shutil.copy('''./tests/fixtures/merges.txt''' , os.path.join(_A , '''merges.txt''' ) )
UpperCAmelCase__ : Any = AutoTokenizer.from_pretrained(_A , tokenizer_type='''gpt2''' )
self.assertIsInstance(_A , _A )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
with pytest.raises(_A ):
AutoTokenizer.from_pretrained('''./''' , tokenizer_type='''xxx''' )
@require_tokenizers
def lowercase_ ( self : int ):
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
UpperCAmelCase__ : Optional[int] = tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased''' )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
if isinstance(_A , _A ):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , _A )
else:
self.assertEqual(tokenizer.do_lower_case , _A )
self.assertEqual(tokenizer.model_max_length , 512 )
@require_tokenizers
def lowercase_ ( self : List[str] ):
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
_A , '''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier''' , ):
UpperCAmelCase__ : Dict = tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists''' )
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = TOKENIZER_MAPPING.values()
UpperCAmelCase__ : Any = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__ )
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__ )
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(_A )
@require_tokenizers
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased''' , use_fast=_A ) , _A )
self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased''' ) , _A )
@require_tokenizers
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained('''distilbert-base-uncased''' , do_lower_case=_A )
UpperCAmelCase__ : Any = '''Hello, world. How are you?'''
UpperCAmelCase__ : Dict = tokenizer.tokenize(_A )
self.assertEqual('''[UNK]''' , tokens[0] )
UpperCAmelCase__ : Union[str, Any] = AutoTokenizer.from_pretrained('''microsoft/mpnet-base''' , do_lower_case=_A )
UpperCAmelCase__ : Union[str, Any] = tokenizer.tokenize(_A )
self.assertEqual('''[UNK]''' , tokens[0] )
@require_tokenizers
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config''' )
self.assertEqual(type(_A ) , _A )
self.assertEqual(tokenizer.model_max_length , 512 )
self.assertEqual(tokenizer.vocab_size , 30_000 )
self.assertEqual(tokenizer.unk_token , '''[UNK]''' )
self.assertEqual(tokenizer.padding_side , '''right''' )
self.assertEqual(tokenizer.truncation_side , '''right''' )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , tokenizer.__class__ )
self.assertEqual(tokenizera.vocab_size , 12 )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained('''ctrl''' )
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(_A , _A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = get_tokenizer_config('''bert-base-cased''' )
UpperCAmelCase__ : Optional[int] = config.pop('''_commit_hash''' , _A )
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(_A , {'''do_lower_case''': False} )
# This model does not have a tokenizer_config so we get back an empty dict.
UpperCAmelCase__ : Tuple = get_tokenizer_config(_A )
self.assertDictEqual(_A , {} )
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(_A )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : List[Any] = get_tokenizer_config(_A )
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config['''tokenizer_class'''] , '''BertTokenizer''' )
def lowercase_ ( self : Dict ):
'''simple docstring'''
try:
AutoConfig.register('''custom''' , _A )
AutoTokenizer.register(_A , slow_tokenizer_class=_A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A ):
AutoTokenizer.register(_A , slow_tokenizer_class=_A )
UpperCAmelCase__ : Optional[int] = CustomTokenizer.from_pretrained(_A )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , _A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def lowercase_ ( self : Any ):
'''simple docstring'''
try:
AutoConfig.register('''custom''' , _A )
# Can register in two steps
AutoTokenizer.register(_A , slow_tokenizer_class=_A )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None) )
AutoTokenizer.register(_A , fast_tokenizer_class=_A )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
_A , slow_tokenizer_class=_A , fast_tokenizer_class=_A )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A ):
AutoTokenizer.register(_A , fast_tokenizer_class=_A )
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase__ : Any = BertTokenizerFast.from_pretrained(_A )
bert_tokenizer.save_pretrained(_A )
UpperCAmelCase__ : Optional[int] = CustomTokenizerFast.from_pretrained(_A )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , _A )
UpperCAmelCase__ : Union[str, Any] = AutoTokenizer.from_pretrained(_A , use_fast=_A )
self.assertIsInstance(_A , _A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
with self.assertRaises(_A ):
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_A ):
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A )
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A )
self.assertTrue(tokenizer.special_attribute_present )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(_A , trust_remote_code=_A )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
# Test we can also load the slow version
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A , use_fast=_A )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : Any = AutoTokenizer.from_pretrained(_A , trust_remote_code=_A , use_fast=_A )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
else:
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizer''' )
@require_tokenizers
def lowercase_ ( self : int ):
'''simple docstring'''
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = False
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = NewTokenizer
lowerCAmelCase__ = False
try:
AutoConfig.register('''custom''' , _A )
AutoTokenizer.register(_A , slow_tokenizer_class=_A )
AutoTokenizer.register(_A , fast_tokenizer_class=_A )
# If remote code is not set, the default is to use local
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertFalse(tokenizer.special_attribute_present )
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' , use_fast=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertFalse(tokenizer.special_attribute_present )
# If remote code is disabled, we load the local one.
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertFalse(tokenizer.special_attribute_present )
UpperCAmelCase__ : str = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A , use_fast=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertFalse(tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertTrue(tokenizer.special_attribute_present )
UpperCAmelCase__ : Any = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A , use_fast=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertTrue(tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer_legacy''' , trust_remote_code=_A )
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
# Test we can also load the slow version
UpperCAmelCase__ : Optional[Any] = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer_legacy''' , trust_remote_code=_A , use_fast=_A )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
else:
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
with self.assertRaisesRegex(
_A , '''bert-base is not a local folder and is not a valid model identifier''' ):
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained('''bert-base''' )
def lowercase_ ( self : Dict ):
'''simple docstring'''
with self.assertRaisesRegex(
_A , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(_A , revision='''aaaaaa''' )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
with RequestCounter() as counter:
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 299
|
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a__ ( lowerCAmelCase__ ) -> List[Any]:
return 1 / (1 + np.exp(-z ))
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
return (-y * np.log(lowerCAmelCase__ ) - (1 - y) * np.log(1 - h )).mean()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
UpperCAmelCase__ : str = np.dot(lowerCAmelCase__ , lowerCAmelCase__ )
return np.sum(y * scores - np.log(1 + np.exp(lowerCAmelCase__ ) ) )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=7_00_00 ) -> List[Any]:
UpperCAmelCase__ : Tuple = np.zeros(x.shape[1] )
for iterations in range(lowerCAmelCase__ ):
UpperCAmelCase__ : List[Any] = np.dot(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ : List[str] = sigmoid_function(lowerCAmelCase__ )
UpperCAmelCase__ : int = np.dot(x.T , h - y ) / y.size
UpperCAmelCase__ : Optional[int] = theta - alpha * gradient # updating the weights
UpperCAmelCase__ : Dict = np.dot(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ : int = sigmoid_function(lowerCAmelCase__ )
UpperCAmelCase__ : Tuple = cost_function(lowerCAmelCase__ , lowerCAmelCase__ )
if iterations % 1_00 == 0:
print(F"""loss: {j} \t""" ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
UpperCamelCase__ = datasets.load_iris()
UpperCamelCase__ = iris.data[:, :2]
UpperCamelCase__ = (iris.target != 0) * 1
UpperCamelCase__ = 0.1
UpperCamelCase__ = logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
print('''theta: ''', theta) # printing the theta i.e our weights vector
def a__ ( lowerCAmelCase__ ) -> Dict:
return sigmoid_function(
np.dot(lowerCAmelCase__ , lowerCAmelCase__ ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(1_0, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
((UpperCamelCase__) , (UpperCamelCase__)) = (x[:, 0].min(), x[:, 0].max())
((UpperCamelCase__) , (UpperCamelCase__)) = (x[:, 1].min(), x[:, 1].max())
((UpperCamelCase__) , (UpperCamelCase__)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
UpperCamelCase__ = np.c_[xxa.ravel(), xxa.ravel()]
UpperCamelCase__ = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')
plt.legend()
plt.show()
| 299
| 1
|
'''simple docstring'''
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__lowercase = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))
print('''Googling.....''')
__lowercase = f'https://www.google.com/search?q={query}&num=100'
__lowercase = requests.get(
url,
headers={'''User-Agent''': str(UserAgent().random)},
)
try:
__lowercase = (
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''yuRUbf'''})
.find('''a''')
.get('''href''')
)
except AttributeError:
__lowercase = parse_qs(
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''kCrYT'''})
.find('''a''')
.get('''href''')
)['''url'''][0]
webbrowser.open(link)
| 272
|
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase: Optional[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase: List[str] = 0
while number > 0:
__lowerCAmelCase: Any = number % 1_0
sum_of_digits += last_digit
__lowerCAmelCase: List[Any] = number // 1_0 # Removing the last_digit from the given number
return sum_of_digits
def a__ ( __SCREAMING_SNAKE_CASE = 1_0_0 ) -> int:
__lowerCAmelCase: Tuple = factorial(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = split_and_add(__SCREAMING_SNAKE_CASE )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 217
| 0
|
"""simple docstring"""
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
__snake_case = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : int, _lowerCAmelCase : Any ):
"""simple docstring"""
return [
int(10_00 * (box[0] / width) ),
int(10_00 * (box[1] / height) ),
int(10_00 * (box[2] / width) ),
int(10_00 * (box[3] / height) ),
]
def A_ ( _lowerCAmelCase : np.ndarray, _lowerCAmelCase : Optional[str], _lowerCAmelCase : Optional[str] ):
"""simple docstring"""
_a = to_pil_image(_lowerCAmelCase )
_a , _a = pil_image.size
_a = pytesseract.image_to_data(_lowerCAmelCase, lang=_lowerCAmelCase, output_type='''dict''', config=_lowerCAmelCase )
_a , _a , _a , _a , _a = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
_a = [idx for idx, word in enumerate(_lowerCAmelCase ) if not word.strip()]
_a = [word for idx, word in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices]
_a = [coord for idx, coord in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices]
_a = [coord for idx, coord in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices]
_a = [coord for idx, coord in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices]
_a = [coord for idx, coord in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
_a = []
for x, y, w, h in zip(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase ):
_a = [x, y, x + w, y + h]
actual_boxes.append(_lowerCAmelCase )
# finally, normalize the bounding boxes
_a = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase ) )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : str = ['pixel_values']
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BILINEAR , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = "" , **__UpperCAmelCase , ) -> None:
super().__init__(**__UpperCAmelCase )
_a = size if size is not None else {'''height''': 224, '''width''': 224}
_a = get_size_dict(__UpperCAmelCase )
_a = do_resize
_a = size
_a = resample
_a = do_rescale
_a = rescale_value
_a = do_normalize
_a = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_a = image_std if image_std is not None else IMAGENET_STANDARD_STD
_a = apply_ocr
_a = ocr_lang
_a = tesseract_config
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BILINEAR , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
_a = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
_a = (size['''height'''], size['''width'''])
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase=None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ) -> PIL.Image.Image:
_a = do_resize if do_resize is not None else self.do_resize
_a = size if size is not None else self.size
_a = get_size_dict(__UpperCAmelCase )
_a = resample if resample is not None else self.resample
_a = do_rescale if do_rescale is not None else self.do_rescale
_a = rescale_factor if rescale_factor is not None else self.rescale_factor
_a = do_normalize if do_normalize is not None else self.do_normalize
_a = image_mean if image_mean is not None else self.image_mean
_a = image_std if image_std is not None else self.image_std
_a = apply_ocr if apply_ocr is not None else self.apply_ocr
_a = ocr_lang if ocr_lang is not None else self.ocr_lang
_a = tesseract_config if tesseract_config is not None else self.tesseract_config
_a = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.''' )
# All transformations expect numpy arrays.
_a = [to_numpy_array(__UpperCAmelCase ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , '''pytesseract''' )
_a = []
_a = []
for image in images:
_a , _a = apply_tesseract(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
words_batch.append(__UpperCAmelCase )
boxes_batch.append(__UpperCAmelCase )
if do_resize:
_a = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_rescale:
_a = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
_a = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
_a = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
_a = BatchFeature(data={'''pixel_values''': images} , tensor_type=__UpperCAmelCase )
if apply_ocr:
_a = words_batch
_a = boxes_batch
return data
| 153
|
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ) -> str:
_a = '''ylacombe/bark-small'''
_a = tempfile.mkdtemp()
_a = '''en_speaker_1'''
_a = '''This is a test string'''
_a = '''speaker_embeddings_path.json'''
_a = '''speaker_embeddings'''
def _UpperCAmelCase ( self , **__UpperCAmelCase ) -> Tuple:
return AutoTokenizer.from_pretrained(self.checkpoint , **__UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Tuple:
shutil.rmtree(self.tmpdirname )
def _UpperCAmelCase ( self ) -> str:
_a = self.get_tokenizer()
_a = BarkProcessor(tokenizer=__UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
_a = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def _UpperCAmelCase ( self ) -> Optional[Any]:
_a = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
_a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_a = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='''(BOS)''' , eos_token='''(EOS)''' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def _UpperCAmelCase ( self ) -> str:
_a = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
_a = 35
_a = 2
_a = 8
_a = {
'''semantic_prompt''': np.ones(__UpperCAmelCase ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
_a = processor(text=self.input_string , voice_preset=__UpperCAmelCase )
_a = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__UpperCAmelCase , np.array([] ) ).tolist() )
# test loading voice preset from npz file
_a = os.path.join(self.tmpdirname , '''file.npz''' )
np.savez(__UpperCAmelCase , **__UpperCAmelCase )
_a = processor(text=self.input_string , voice_preset=__UpperCAmelCase )
_a = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__UpperCAmelCase , np.array([] ) ).tolist() )
# test loading voice preset from the hub
_a = processor(text=self.input_string , voice_preset=self.voice_preset )
def _UpperCAmelCase ( self ) -> Tuple:
_a = self.get_tokenizer()
_a = BarkProcessor(tokenizer=__UpperCAmelCase )
_a = processor(text=self.input_string )
_a = tokenizer(
self.input_string , padding='''max_length''' , max_length=256 , add_special_tokens=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 153
| 1
|
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__A = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__A = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'''{len(upper_files)} files contain uppercase characters:''')
print("""\n""".join(upper_files) + """\n""")
__A = [file for file in filepaths if """ """ in file]
if space_files:
print(F'''{len(space_files)} files contain space characters:''')
print("""\n""".join(space_files) + """\n""")
__A = [file for file in filepaths if """-""" in file]
if hyphen_files:
print(F'''{len(hyphen_files)} files contain hyphen characters:''')
print("""\n""".join(hyphen_files) + """\n""")
__A = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'''{len(nodir_files)} files are not in a directory:''')
print("""\n""".join(nodir_files) + """\n""")
__A = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 293
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__A = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__A = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__A = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 293
| 1
|
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def __lowerCamelCase ( __lowerCAmelCase : int ) -> typing.Counter[int]:
snake_case = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(__lowerCAmelCase , max_perimeter + 1 ):
snake_case = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(__lowerCAmelCase ):
snake_case = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def __lowerCamelCase ( __lowerCAmelCase : int = 10_00 ) -> int:
snake_case = pythagorean_triple(__lowerCAmelCase )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(F"""Perimeter {solution()} has maximum solutions""")
| 3
|
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __lowerCamelCase ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus" ) -> dict:
snake_case = BeautifulSoup(requests.get(__lowerCAmelCase ).text , """html.parser""" )
snake_case = soup.findAll("""h1""" )
snake_case = soup.findAll("""div""" , {"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" , {"""class""": """panel-title"""} )
values += soup.findAll("""div""" , {"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(__lowerCAmelCase , __lowerCAmelCase )}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covidaa_stats().items():
print(F"""{key}\n{value}\n""")
| 3
| 1
|
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : str , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str=sys.maxsize ) -> List[Any]:
SCREAMING_SNAKE_CASE__ = '''bilinear'''
SCREAMING_SNAKE_CASE__ = max_size
SCREAMING_SNAKE_CASE__ = short_edge_length
def __call__( self : int , __lowerCamelCase : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ = []
for img in imgs:
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = img.shape[:2]
# later: provide list and randomly choose index for resize
SCREAMING_SNAKE_CASE__ = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
SCREAMING_SNAKE_CASE__ = size * 1.0 / min(__lowerCamelCase , __lowerCamelCase )
if h < w:
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = size, scale * w
else:
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = scale * h, size
if max(__lowerCamelCase , __lowerCamelCase ) > self.max_size:
SCREAMING_SNAKE_CASE__ = self.max_size * 1.0 / max(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE__ = newh * scale
SCREAMING_SNAKE_CASE__ = neww * scale
SCREAMING_SNAKE_CASE__ = int(neww + 0.5 )
SCREAMING_SNAKE_CASE__ = int(newh + 0.5 )
if img.dtype == np.uinta:
SCREAMING_SNAKE_CASE__ = Image.fromarray(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
SCREAMING_SNAKE_CASE__ = np.asarray(__lowerCamelCase )
else:
SCREAMING_SNAKE_CASE__ = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
SCREAMING_SNAKE_CASE__ = nn.functional.interpolate(
__lowerCamelCase , (newh, neww) , mode=self.interp_method , align_corners=__lowerCamelCase ).squeeze(0 )
img_augs.append(__lowerCamelCase )
return img_augs
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
SCREAMING_SNAKE_CASE__ = cfg.INPUT.FORMAT
SCREAMING_SNAKE_CASE__ = cfg.SIZE_DIVISIBILITY
SCREAMING_SNAKE_CASE__ = cfg.PAD_VALUE
SCREAMING_SNAKE_CASE__ = cfg.INPUT.MAX_SIZE_TEST
SCREAMING_SNAKE_CASE__ = cfg.MODEL.DEVICE
SCREAMING_SNAKE_CASE__ = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
SCREAMING_SNAKE_CASE__ = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
SCREAMING_SNAKE_CASE__ = lambda __lowerCamelCase : (x - self.pixel_mean) / self.pixel_std
def lowercase_ ( self : Union[str, Any] , __lowerCamelCase : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ = tuple(max(__lowerCamelCase ) for s in zip(*[img.shape for img in images] ) )
SCREAMING_SNAKE_CASE__ = [im.shape[-2:] for im in images]
SCREAMING_SNAKE_CASE__ = [
nn.functional.pad(
__lowerCamelCase , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(__lowerCamelCase , __lowerCamelCase )
]
return torch.stack(__lowerCamelCase ), torch.tensor(__lowerCamelCase )
def __call__( self : Any , __lowerCamelCase : str , __lowerCamelCase : Tuple=False ) -> List[str]:
with torch.no_grad():
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
SCREAMING_SNAKE_CASE__ = [images]
if single_image:
assert len(__lowerCamelCase ) == 1
for i in range(len(__lowerCamelCase ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(__lowerCamelCase , images.pop(__lowerCamelCase ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
__lowerCamelCase , torch.as_tensor(img_tensorize(images.pop(__lowerCamelCase ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
SCREAMING_SNAKE_CASE__ = torch.tensor([im.shape[:2] for im in images] )
SCREAMING_SNAKE_CASE__ = self.aug(__lowerCamelCase )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
SCREAMING_SNAKE_CASE__ = [self.normalizer(__lowerCamelCase ) for x in images]
# now pad them to do the following operations
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = self.pad(__lowerCamelCase )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
SCREAMING_SNAKE_CASE__ = torch.true_divide(__lowerCamelCase , __lowerCamelCase )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
assert torch.isfinite(_A ).all(), "Box tensor contains infinite or NaN!"
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = box_size
tensor[:, 0].clamp_(min=0 , max=_A )
tensor[:, 1].clamp_(min=0 , max=_A )
tensor[:, 2].clamp_(min=0 , max=_A )
tensor[:, 3].clamp_(min=0 , max=_A )
| 314
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE__ = tempfile.mkdtemp()
# fmt: off
SCREAMING_SNAKE_CASE__ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ = {
'''do_resize''': True,
'''size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , __lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__lowerCamelCase , __lowerCamelCase )
def lowercase_ ( self : Dict , **__lowerCamelCase : Dict ) -> Union[str, Any]:
return BertTokenizer.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def lowercase_ ( self : Optional[Any] , **__lowerCamelCase : Dict ) -> int:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def lowercase_ ( self : str ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def lowercase_ ( self : List[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ = [Image.fromarray(np.moveaxis(__lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase_ ( self : Optional[int] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCamelCase )
def lowercase_ ( self : Any ) -> int:
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
SCREAMING_SNAKE_CASE__ = self.get_image_processor(do_normalize=__lowerCamelCase , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCamelCase )
def lowercase_ ( self : List[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ = image_processor(__lowerCamelCase , return_tensors='''np''' )
SCREAMING_SNAKE_CASE__ = processor(images=__lowerCamelCase , 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 lowercase_ ( self : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = '''lower newer'''
SCREAMING_SNAKE_CASE__ = processor(text=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = tokenizer(__lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase_ ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = '''lower newer'''
SCREAMING_SNAKE_CASE__ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ = processor(text=__lowerCamelCase , images=__lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(__lowerCamelCase ):
processor()
def lowercase_ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ = processor.batch_decode(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = tokenizer.batch_decode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
def lowercase_ ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase , image_processor=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = '''lower newer'''
SCREAMING_SNAKE_CASE__ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ = processor(text=__lowerCamelCase , images=__lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 314
| 1
|
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _a ( _lowerCAmelCase ):
UpperCamelCase = 42
UpperCamelCase = 42
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 357
|
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
UpperCamelCase_ ="""bart"""
UpperCamelCase_ =True
@st.cache(allow_output_mutation=_lowercase )
def a_ ( ):
if LOAD_DENSE_INDEX:
_UpperCamelCase : Dict = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
_UpperCamelCase : Optional[Any] = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
_UpperCamelCase : Union[str, Any] = qar_model.eval()
else:
_UpperCamelCase , _UpperCamelCase : str = (None, None)
if MODEL_TYPE == "bart":
_UpperCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
_UpperCamelCase : List[str] = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
_UpperCamelCase : List[Any] = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
_UpperCamelCase : Dict = sas_model.eval()
else:
_UpperCamelCase , _UpperCamelCase : List[Any] = make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=_lowercase )
def a_ ( ):
if LOAD_DENSE_INDEX:
_UpperCamelCase : List[Any] = faiss.StandardGpuResources()
_UpperCamelCase : List[str] = datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
_UpperCamelCase : Tuple = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 128) , )
_UpperCamelCase : Optional[int] = faiss.IndexFlatIP(128 )
_UpperCamelCase : Tuple = faiss.index_cpu_to_gpu(_lowercase , 1 , _lowercase )
wikiaab_gpu_index_flat.add(_lowercase ) # TODO fix for larger GPU
else:
_UpperCamelCase , _UpperCamelCase : Tuple = (None, None)
_UpperCamelCase : List[Any] = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=_lowercase )
def a_ ( ):
_UpperCamelCase : Optional[Any] = datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
_UpperCamelCase : Any = elia['''train_eli5''']
_UpperCamelCase : Union[str, Any] = np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 128) )
_UpperCamelCase : str = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(_lowercase )
return (elia_train, eli5_train_q_index)
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ =load_indexes()
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ =load_models()
UpperCamelCase_ , UpperCamelCase_ =load_train_data()
def a_ ( _lowercase , _lowercase=10 ):
_UpperCamelCase : Any = embed_questions_for_retrieval([question] , _lowercase , _lowercase )
_UpperCamelCase , _UpperCamelCase : List[Any] = eli5_train_q_index.search(_lowercase , _lowercase )
_UpperCamelCase : Tuple = [elia_train[int(_lowercase )] for i in I[0]]
return nn_examples
def a_ ( _lowercase , _lowercase="wiki40b" , _lowercase="dense" , _lowercase=10 ):
if source == "none":
_UpperCamelCase , _UpperCamelCase : List[str] = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
_UpperCamelCase , _UpperCamelCase : Dict = query_qa_dense_index(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
else:
_UpperCamelCase , _UpperCamelCase : List[str] = query_es_index(
_lowercase , _lowercase , index_name='''english_wiki40b_snippets_100w''' , n_results=_lowercase , )
_UpperCamelCase : Any = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
_UpperCamelCase : List[Any] = '''question: {} context: {}'''.format(_lowercase , _lowercase )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda _lowercase : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowercase : None),
} )
def a_ ( _lowercase , _lowercase , _lowercase , _lowercase=64 , _lowercase=256 , _lowercase=False , _lowercase=2 , _lowercase=0.95 , _lowercase=0.8 ):
with torch.no_grad():
_UpperCamelCase : List[Any] = qa_sas_generate(
_lowercase , _lowercase , _lowercase , num_answers=1 , num_beams=_lowercase , min_len=_lowercase , max_len=_lowercase , do_sample=_lowercase , temp=_lowercase , top_p=_lowercase , top_k=_lowercase , max_input_length=1024 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
UpperCamelCase_ ="""<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
UpperCamelCase_ ="""
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class=\"img-container\"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
UpperCamelCase_ ="""
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)
UpperCamelCase_ =[
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
UpperCamelCase_ =st.sidebar.checkbox("""Demo options""")
if demo_options:
UpperCamelCase_ =st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
UpperCamelCase_ =action_list.index(action_st)
UpperCamelCase_ =st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
UpperCamelCase_ =show_type == """Show full text of passages"""
else:
UpperCamelCase_ =3
UpperCamelCase_ =True
UpperCamelCase_ =st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
UpperCamelCase_ ="""
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
"""
st.sidebar.markdown(retriever_info)
UpperCamelCase_ =st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
UpperCamelCase_ =st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
UpperCamelCase_ ="""wiki40b"""
UpperCamelCase_ ="""dense"""
UpperCamelCase_ ="""beam"""
UpperCamelCase_ =2
UpperCamelCase_ =64
UpperCamelCase_ =256
UpperCamelCase_ =None
UpperCamelCase_ =None
UpperCamelCase_ =st.sidebar.checkbox("""Generation options""")
if generate_options:
UpperCamelCase_ ="""
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder's output probabilities.
"""
st.sidebar.markdown(generate_info)
UpperCamelCase_ =st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
UpperCamelCase_ =st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
UpperCamelCase_ =st.sidebar.slider(
"""Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
UpperCamelCase_ =st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
UpperCamelCase_ =st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
UpperCamelCase_ =st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
UpperCamelCase_ =None
# start main text
UpperCamelCase_ =[
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What's the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What's the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
UpperCamelCase_ =st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
UpperCamelCase_ =st.text_input("""Enter your question here:""", """""")
else:
UpperCamelCase_ =question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
UpperCamelCase_ , UpperCamelCase_ =make_support(question, source=wiki_source, method="""dense""", n_results=10)
UpperCamelCase_ , UpperCamelCase_ =make_support(question, source=wiki_source, method="""sparse""", n_results=10)
UpperCamelCase_ =[]
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
UpperCamelCase_ =support_list[:10]
UpperCamelCase_ ="""<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
UpperCamelCase_ , UpperCamelCase_ =make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
UpperCamelCase_ , UpperCamelCase_ =answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
UpperCamelCase_ ="""https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
UpperCamelCase_ =res[1].strip()
if sec_titles == "":
UpperCamelCase_ ="""[{}]({})""".format(res[0], wiki_url)
else:
UpperCamelCase_ =sec_titles.split(""" & """)
UpperCamelCase_ =""" & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
UpperCamelCase_ =find_nearest_training(question)
UpperCamelCase_ =nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
UpperCamelCase_ =[
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
UpperCamelCase_ ="""
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 128
| 0
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowerCamelCase__ ( lowerCamelCase_ , unittest.TestCase ):
a__ : Union[str, Any] = BarthezTokenizer
a__ : int = BarthezTokenizerFast
a__ : List[Any] = True
a__ : str = True
def lowerCamelCase_ ( self ):
"""simple docstring"""
super().setUp()
snake_case : List[str] = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=SCREAMING_SNAKE_CASE )
snake_case : Dict = tokenizer
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Any = "<pad>"
snake_case : Optional[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 101_122 )
def lowerCamelCase_ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 101_122 )
@require_torch
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : str = ["A long paragraph for summarization.", "Another paragraph for summarization."]
snake_case : Optional[int] = [0, 57, 3_018, 70_307, 91, 2]
snake_case : Union[str, Any] = self.tokenizer(
SCREAMING_SNAKE_CASE , max_length=len(SCREAMING_SNAKE_CASE ) , padding=SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , return_tensors="pt" )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
snake_case : Tuple = batch.input_ids.tolist()[0]
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowerCamelCase_ ( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
snake_case : Union[str, Any] = self.get_tokenizer()
snake_case : List[str] = self.get_rust_tokenizer()
snake_case : Optional[int] = "I was born in 92000, and this is falsé."
snake_case : str = tokenizer.tokenize(SCREAMING_SNAKE_CASE )
snake_case : Optional[Any] = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
snake_case : int = tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE )
snake_case : int = rust_tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
snake_case : Any = self.get_rust_tokenizer()
snake_case : Any = tokenizer.encode(SCREAMING_SNAKE_CASE )
snake_case : Union[str, Any] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Optional[Any] = {"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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
# moussaKam/mbarthez is a french model. So we also use french texts.
snake_case : int = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE , model_name="moussaKam/mbarthez" , revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" , sequences=SCREAMING_SNAKE_CASE , )
| 148
|
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def UpperCamelCase__ ( lowercase__ : List[str] ):
snake_case : Tuple = min(lowercase__ ) # min() finds the minimum value
snake_case : int = max(lowercase__ ) # max() finds the maximum value
snake_case : List[Any] = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
snake_case : List[Any] = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(lowercase__ , lowercase__ ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
snake_case : Optional[Any] = 0
for count in range(lowercase__ ):
while holes[count] > 0:
holes[count] -= 1
snake_case : List[str] = count + min_val
i += 1
def UpperCamelCase__ ( ):
snake_case : Dict = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(lowercase__ )
print("Sorted order is:" , " ".join(lowercase__ ) )
if __name__ == "__main__":
main()
| 148
| 1
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_A = logging.get_logger(__name__)
_A = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
_A = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
_A = {
"""google/realm-cc-news-pretrained-embedder""": 5_12,
"""google/realm-cc-news-pretrained-encoder""": 5_12,
"""google/realm-cc-news-pretrained-scorer""": 5_12,
"""google/realm-cc-news-pretrained-openqa""": 5_12,
"""google/realm-orqa-nq-openqa""": 5_12,
"""google/realm-orqa-nq-reader""": 5_12,
"""google/realm-orqa-wq-openqa""": 5_12,
"""google/realm-orqa-wq-reader""": 5_12,
}
_A = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class lowerCamelCase ( a_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = RealmTokenizer
def __init__(self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=True , _lowerCamelCase="[UNK]" , _lowerCamelCase="[SEP]" , _lowerCamelCase="[PAD]" , _lowerCamelCase="[CLS]" , _lowerCamelCase="[MASK]" , _lowerCamelCase=True , _lowerCamelCase=None , **_lowerCamelCase , ):
"""simple docstring"""
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , do_lower_case=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , tokenize_chinese_chars=lowercase_ , strip_accents=lowercase_ , **lowercase_ , )
UpperCAmelCase__ : Optional[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowercase_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowercase_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowercase_ ) != tokenize_chinese_chars
):
UpperCAmelCase__ : List[Any] = getattr(lowercase_ , normalizer_state.pop("""type""" ) )
UpperCAmelCase__ : Any = do_lower_case
UpperCAmelCase__ : str = strip_accents
UpperCAmelCase__ : Dict = tokenize_chinese_chars
UpperCAmelCase__ : Dict = normalizer_class(**lowercase_ )
UpperCAmelCase__ : int = do_lower_case
def _a (self , _lowerCamelCase , **_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = PaddingStrategy.MAX_LENGTH
UpperCAmelCase__ : Any = text
UpperCAmelCase__ : int = kwargs.pop("""text_pair""" , lowercase_ )
UpperCAmelCase__ : Dict = kwargs.pop("""return_tensors""" , lowercase_ )
UpperCAmelCase__ : Union[str, Any] = {
"""input_ids""": [],
"""attention_mask""": [],
"""token_type_ids""": [],
}
for idx, candidate_text in enumerate(lowercase_ ):
if batch_text_pair is not None:
UpperCAmelCase__ : List[Any] = batch_text_pair[idx]
else:
UpperCAmelCase__ : List[str] = None
UpperCAmelCase__ : Optional[int] = super().__call__(lowercase_ , lowercase_ , return_tensors=lowercase_ , **lowercase_ )
UpperCAmelCase__ : str = encoded_candidates.get("""input_ids""" )
UpperCAmelCase__ : Any = encoded_candidates.get("""attention_mask""" )
UpperCAmelCase__ : Union[str, Any] = encoded_candidates.get("""token_type_ids""" )
if encoded_input_ids is not None:
output_data["input_ids"].append(lowercase_ )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(lowercase_ )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(lowercase_ )
UpperCAmelCase__ : Union[str, Any] = {key: item for key, item in output_data.items() if len(lowercase_ ) != 0}
return BatchEncoding(lowercase_ , tensor_type=lowercase_ )
def _a (self , _lowerCamelCase , _lowerCamelCase=None ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _a (self , _lowerCamelCase , _lowerCamelCase = None ):
"""simple docstring"""
UpperCAmelCase__ : Any = [self.sep_token_id]
UpperCAmelCase__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _a (self , _lowerCamelCase , _lowerCamelCase = None ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
| 364
|
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__(self , *_lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=None , **_lowerCamelCase ):
"""simple docstring"""
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
UpperCAmelCase__ : List[str] = eval_examples
UpperCAmelCase__ : List[Any] = post_process_function
def _a (self , _lowerCamelCase = None , _lowerCamelCase=None , _lowerCamelCase = None , _lowerCamelCase = "eval" , **_lowerCamelCase , ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = gen_kwargs.copy()
UpperCAmelCase__ : Optional[Any] = (
gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length""" ) is not None else self.args.generation_max_length
)
UpperCAmelCase__ : int = (
gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams""" ) is not None else self.args.generation_num_beams
)
UpperCAmelCase__ : int = gen_kwargs
UpperCAmelCase__ : Optional[int] = self.eval_dataset if eval_dataset is None else eval_dataset
UpperCAmelCase__ : Tuple = self.get_eval_dataloader(_lowerCamelCase )
UpperCAmelCase__ : Optional[Any] = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
UpperCAmelCase__ : List[str] = self.compute_metrics
UpperCAmelCase__ : List[Any] = None
UpperCAmelCase__ : Optional[int] = time.time()
UpperCAmelCase__ : str = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
UpperCAmelCase__ : int = eval_loop(
_lowerCamelCase , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_lowerCamelCase , metric_key_prefix=_lowerCamelCase , )
finally:
UpperCAmelCase__ : Any = compute_metrics
UpperCAmelCase__ : Union[str, Any] = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_lowerCamelCase , _lowerCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
UpperCAmelCase__ : List[str] = self.post_process_function(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
UpperCAmelCase__ : Optional[Any] = self.compute_metrics(_lowerCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
UpperCAmelCase__ : Tuple = metrics.pop(_lowerCamelCase )
metrics.update(output.metrics )
else:
UpperCAmelCase__ : List[str] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_lowerCamelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
UpperCAmelCase__ : List[str] = self.callback_handler.on_evaluate(self.args , self.state , self.control , _lowerCamelCase )
return metrics
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase = "test" , **_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = gen_kwargs.copy()
UpperCAmelCase__ : List[Any] = self.get_test_dataloader(_lowerCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
UpperCAmelCase__ : Any = self.compute_metrics
UpperCAmelCase__ : List[Any] = None
UpperCAmelCase__ : str = time.time()
UpperCAmelCase__ : Any = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
UpperCAmelCase__ : List[str] = eval_loop(
_lowerCamelCase , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_lowerCamelCase , metric_key_prefix=_lowerCamelCase , )
finally:
UpperCAmelCase__ : int = compute_metrics
UpperCAmelCase__ : Any = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_lowerCamelCase , _lowerCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
UpperCAmelCase__ : Optional[Any] = self.post_process_function(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , """predict""" )
UpperCAmelCase__ : List[str] = self.compute_metrics(_lowerCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
UpperCAmelCase__ : str = metrics.pop(_lowerCamelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_lowerCamelCase )
| 166
| 0
|
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class lowercase :
def __init__( self ,A__ ,A__=1_3 ,A__=1_0 ,A__=3 ,A__=2 ,A__=2 ,A__=2 ,A__=True ,A__=True ,A__=3_2 ,A__=5 ,A__=4 ,A__=3_7 ,A__="gelu" ,A__=0.1 ,A__=0.1 ,A__=1_0 ,A__=0.02 ,A__=0.9 ,A__=None ,):
lowercase = parent
lowercase = batch_size
lowercase = image_size
lowercase = num_channels
lowercase = patch_size
lowercase = tubelet_size
lowercase = num_frames
lowercase = is_training
lowercase = use_labels
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = mask_ratio
lowercase = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
lowercase = (image_size // patch_size) ** 2
lowercase = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
lowercase = int(mask_ratio * self.seq_length)
def A__ ( self):
lowercase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] ,self.type_sequence_label_size)
lowercase = self.get_config()
return config, pixel_values, labels
def A__ ( self):
return VideoMAEConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,num_frames=self.num_frames ,tubelet_size=self.tubelet_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 ,is_decoder=A__ ,initializer_range=self.initializer_range ,)
def A__ ( self ,A__ ,A__ ,A__):
lowercase = VideoMAEModel(config=A__)
model.to(A__)
model.eval()
lowercase = model(A__)
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size))
def A__ ( self ,A__ ,A__ ,A__):
lowercase = VideoMAEForPreTraining(A__)
model.to(A__)
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
lowercase = torch.ones((self.num_masks,))
lowercase = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
lowercase = mask.expand(self.batch_size ,-1).bool()
lowercase = model(A__ ,A__)
# model only returns predictions for masked patches
lowercase = mask.sum().item()
lowercase = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_masked_patches, decoder_num_labels))
def A__ ( self):
lowercase = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase = config_and_inputs
lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
lowercase_ : str =(
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowercase_ : Tuple =(
{'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowercase_ : List[str] =False
lowercase_ : str =False
lowercase_ : int =False
lowercase_ : Dict =False
def A__ ( self):
lowercase = VideoMAEModelTester(self)
lowercase = ConfigTester(self ,config_class=A__ ,has_text_modality=A__ ,hidden_size=3_7)
def A__ ( self ,A__ ,A__ ,A__=False):
lowercase = copy.deepcopy(A__)
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
lowercase = torch.ones((self.model_tester.num_masks,))
lowercase = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))])
lowercase = mask.expand(self.model_tester.batch_size ,-1).bool()
lowercase = bool_masked_pos.to(A__)
if return_labels:
if model_class in [
*get_values(A__),
]:
lowercase = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=A__)
return inputs_dict
def A__ ( self):
self.config_tester.run_common_tests()
@unittest.skip(reason='''VideoMAE does not use inputs_embeds''')
def A__ ( self):
pass
def A__ ( self):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(A__)
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module))
lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ ,nn.Linear))
def A__ ( self):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(A__)
lowercase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,A__)
def A__ ( self):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__)
def A__ ( self):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*A__)
@slow
def A__ ( self):
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = VideoMAEModel.from_pretrained(A__)
self.assertIsNotNone(A__)
def A__ ( self):
if not self.has_attentions:
pass
else:
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = True
for model_class in self.all_model_classes:
lowercase = self.model_tester.seq_length - self.model_tester.num_masks
lowercase = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
lowercase = True
lowercase = False
lowercase = True
lowercase = model_class(A__)
model.to(A__)
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(A__ ,A__))
lowercase = outputs.attentions
self.assertEqual(len(A__) ,self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase = True
lowercase = model_class(A__)
model.to(A__)
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(A__ ,A__))
lowercase = outputs.attentions
self.assertEqual(len(A__) ,self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]) ,[self.model_tester.num_attention_heads, seq_len, seq_len] ,)
lowercase = len(A__)
# Check attention is always last and order is fine
lowercase = True
lowercase = True
lowercase = model_class(A__)
model.to(A__)
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(A__ ,A__))
self.assertEqual(out_len + 1 ,len(A__))
lowercase = outputs.attentions
self.assertEqual(len(A__) ,self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]) ,[self.model_tester.num_attention_heads, seq_len, seq_len] ,)
def A__ ( self):
def check_hidden_states_output(A__ ,A__ ,A__):
lowercase = model_class(A__)
model.to(A__)
model.eval()
with torch.no_grad():
lowercase = model(**self._prepare_for_class(A__ ,A__))
lowercase = outputs.hidden_states
lowercase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(A__) ,A__)
lowercase = self.model_tester.seq_length - self.model_tester.num_masks
lowercase = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:]) ,[seq_length, self.model_tester.hidden_size] ,)
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = True
check_hidden_states_output(A__ ,A__ ,A__)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase = True
check_hidden_states_output(A__ ,A__ ,A__)
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def A__ ( self):
pass
def UpperCamelCase ( ):
'''simple docstring'''
lowercase = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
lowercase = np.load(lowerCAmelCase__ )
return list(lowerCAmelCase__ )
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
@cached_property
def A__ ( self):
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,image_std=[0.5, 0.5, 0.5])
if is_vision_available()
else None
)
@slow
def A__ ( self):
lowercase = VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics''').to(
A__)
lowercase = self.default_image_processor
lowercase = prepare_video()
lowercase = image_processor(A__ ,return_tensors='''pt''').to(A__)
# forward pass
with torch.no_grad():
lowercase = model(**A__)
# verify the logits
lowercase = torch.Size((1, 4_0_0))
self.assertEqual(outputs.logits.shape ,A__)
lowercase = torch.tensor([0.3669, -0.0688, -0.2421]).to(A__)
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,A__ ,atol=1E-4))
@slow
def A__ ( self):
lowercase = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''').to(A__)
lowercase = self.default_image_processor
lowercase = prepare_video()
lowercase = image_processor(A__ ,return_tensors='''pt''').to(A__)
# add boolean mask, indicating which patches to mask
lowercase = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' ,filename='''bool_masked_pos.pt''')
lowercase = torch.load(A__)
# forward pass
with torch.no_grad():
lowercase = model(**A__)
# verify the logits
lowercase = torch.Size([1, 1_4_0_8, 1_5_3_6])
lowercase = torch.tensor(
[[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] ,device=A__)
self.assertEqual(outputs.logits.shape ,A__)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,A__ ,atol=1E-4))
# verify the loss (`config.norm_pix_loss` = `True`)
lowercase = torch.tensor([0.5142] ,device=A__)
self.assertTrue(torch.allclose(outputs.loss ,A__ ,atol=1E-4))
# verify the loss (`config.norm_pix_loss` = `False`)
lowercase = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' ,norm_pix_loss=A__).to(
A__)
with torch.no_grad():
lowercase = model(**A__)
lowercase = torch.tensor(torch.tensor([0.6469]) ,device=A__)
self.assertTrue(torch.allclose(outputs.loss ,A__ ,atol=1E-4))
| 101
|
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class A__ ( lowerCAmelCase__ ):
lowerCAmelCase__ : Union[str, Any] = ["vqvae"]
def __init__( self : int , _UpperCAmelCase : AutoencoderKL , _UpperCAmelCase : UNetaDConditionModel , _UpperCAmelCase : Mel , _UpperCAmelCase : Union[DDIMScheduler, DDPMScheduler] , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=_UpperCAmelCase , scheduler=_UpperCAmelCase , mel=_UpperCAmelCase , vqvae=_UpperCAmelCase )
def a__ ( self : Tuple ) -> int:
"""simple docstring"""
return 50 if isinstance(self.scheduler , _UpperCAmelCase ) else 10_00
@torch.no_grad()
def __call__( self : str , _UpperCAmelCase : int = 1 , _UpperCAmelCase : str = None , _UpperCAmelCase : np.ndarray = None , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = None , _UpperCAmelCase : torch.Generator = None , _UpperCAmelCase : float = 0 , _UpperCAmelCase : float = 0 , _UpperCAmelCase : torch.Generator = None , _UpperCAmelCase : float = 0 , _UpperCAmelCase : torch.Tensor = None , _UpperCAmelCase : torch.Tensor = None , _UpperCAmelCase : str=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
"""simple docstring"""
__lowercase = steps or self.get_default_steps()
self.scheduler.set_timesteps(_UpperCAmelCase )
__lowercase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
__lowercase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
__lowercase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=_UpperCAmelCase , device=self.device , )
__lowercase = noise
__lowercase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(_UpperCAmelCase , _UpperCAmelCase )
__lowercase = self.mel.audio_slice_to_image(_UpperCAmelCase )
__lowercase = np.frombuffer(input_image.tobytes() , dtype='uint8' ).reshape(
(input_image.height, input_image.width) )
__lowercase = (input_image / 2_55) * 2 - 1
__lowercase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
__lowercase = self.vqvae.encode(torch.unsqueeze(_UpperCAmelCase , 0 ) ).latent_dist.sample(
generator=_UpperCAmelCase )[0]
__lowercase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
__lowercase = self.scheduler.add_noise(_UpperCAmelCase , _UpperCAmelCase , self.scheduler.timesteps[start_step - 1] )
__lowercase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
__lowercase = int(mask_start_secs * pixels_per_second )
__lowercase = int(mask_end_secs * pixels_per_second )
__lowercase = self.scheduler.add_noise(_UpperCAmelCase , _UpperCAmelCase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , _UpperCAmelCase ):
__lowercase = self.unet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )['sample']
else:
__lowercase = self.unet(_UpperCAmelCase , _UpperCAmelCase )['sample']
if isinstance(self.scheduler , _UpperCAmelCase ):
__lowercase = self.scheduler.step(
model_output=_UpperCAmelCase , timestep=_UpperCAmelCase , sample=_UpperCAmelCase , eta=_UpperCAmelCase , generator=_UpperCAmelCase , )['prev_sample']
else:
__lowercase = self.scheduler.step(
model_output=_UpperCAmelCase , timestep=_UpperCAmelCase , sample=_UpperCAmelCase , generator=_UpperCAmelCase , )['prev_sample']
if mask is not None:
if mask_start > 0:
__lowercase = mask[:, step, :, :mask_start]
if mask_end > 0:
__lowercase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
__lowercase = 1 / self.vqvae.config.scaling_factor * images
__lowercase = self.vqvae.decode(_UpperCAmelCase )['sample']
__lowercase = (images / 2 + 0.5).clamp(0 , 1 )
__lowercase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
__lowercase = (images * 2_55).round().astype('uint8' )
__lowercase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(_UpperCAmelCase , mode='RGB' ).convert('L' ) for _ in images) )
__lowercase = [self.mel.image_to_audio(_UpperCAmelCase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(_UpperCAmelCase )[:, np.newaxis, :] ) , **ImagePipelineOutput(_UpperCAmelCase ) )
@torch.no_grad()
def a__ ( self : Any , _UpperCAmelCase : List[Image.Image] , _UpperCAmelCase : int = 50 ) -> np.ndarray:
"""simple docstring"""
assert isinstance(self.scheduler , _UpperCAmelCase )
self.scheduler.set_timesteps(_UpperCAmelCase )
__lowercase = np.array(
[np.frombuffer(image.tobytes() , dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
__lowercase = (sample / 2_55) * 2 - 1
__lowercase = torch.Tensor(_UpperCAmelCase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
__lowercase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
__lowercase = self.scheduler.alphas_cumprod[t]
__lowercase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
__lowercase = 1 - alpha_prod_t
__lowercase = self.unet(_UpperCAmelCase , _UpperCAmelCase )['sample']
__lowercase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
__lowercase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
__lowercase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def a__ ( _UpperCAmelCase : torch.Tensor , _UpperCAmelCase : torch.Tensor , _UpperCAmelCase : float ) -> torch.Tensor:
"""simple docstring"""
__lowercase = acos(torch.dot(torch.flatten(_UpperCAmelCase ) , torch.flatten(_UpperCAmelCase ) ) / torch.norm(_UpperCAmelCase ) / torch.norm(_UpperCAmelCase ) )
return sin((1 - alpha) * theta ) * xa / sin(_UpperCAmelCase ) + sin(alpha * theta ) * xa / sin(_UpperCAmelCase )
| 325
| 0
|
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase = " " ) -> list:
__lowerCAmelCase = []
__lowerCAmelCase = 0
for index, char in enumerate(lowercase ):
if char == separator:
split_words.append(string[last_index:index] )
__lowerCAmelCase = index + 1
elif index + 1 == len(lowercase ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 46
|
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> List[str]:
# Initialise PyTorch model
__lowerCAmelCase = BertConfig.from_json_file(lowercase )
print(f'Building PyTorch model from configuration: {config}' )
__lowerCAmelCase = BertForPreTraining(lowercase )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase , lowercase , lowercase )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , lowercase )
if __name__ == "__main__":
_a : Dict = 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(
"""--bert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
_a : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 46
| 1
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = None
class snake_case ( lowercase ):
"""simple docstring"""
def __init__( self , UpperCamelCase=1 , UpperCamelCase=0 , UpperCamelCase=2 , UpperCamelCase=512 , UpperCamelCase="cls" , UpperCamelCase=False , UpperCamelCase=True , **UpperCamelCase , ):
"""simple docstring"""
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCamelCase_ = project_dim
lowerCamelCase_ = pooler_fn
lowerCamelCase_ = learn_encoder
lowerCamelCase_ = use_attention_mask
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = [R"pooler", R"logit_scale"]
_lowerCamelCase = [R"position_ids", R"predictions.decoder.bias"]
_lowerCamelCase = "roberta"
_lowerCamelCase = RobertaSeriesConfig
def __init__( self , UpperCamelCase ):
"""simple docstring"""
super().__init__(__A )
lowerCamelCase_ = XLMRobertaModel(__A )
lowerCamelCase_ = nn.Linear(config.hidden_size , config.project_dim )
lowerCamelCase_ = getattr(__A , "has_pre_transformation" , __A )
if self.has_pre_transformation:
lowerCamelCase_ = nn.Linear(config.hidden_size , config.project_dim )
lowerCamelCase_ = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def snake_case ( self , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , ):
"""simple docstring"""
lowerCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ = self.base_model(
input_ids=__A , attention_mask=__A , token_type_ids=__A , position_ids=__A , head_mask=__A , inputs_embeds=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , output_attentions=__A , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=__A , )
if self.has_pre_transformation:
lowerCamelCase_ = outputs["hidden_states"][-2]
lowerCamelCase_ = self.pre_LN(__A )
lowerCamelCase_ = self.transformation_pre(__A )
return TransformationModelOutput(
projection_state=__A , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
lowerCamelCase_ = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__A , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 55
|
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if "model" in orig_key:
lowerCamelCase : Dict = orig_key.replace("model." , "" )
if "norm1" in orig_key:
lowerCamelCase : Union[str, Any] = orig_key.replace("norm1" , "attention.output.LayerNorm" )
if "norm2" in orig_key:
lowerCamelCase : Union[str, Any] = orig_key.replace("norm2" , "output.LayerNorm" )
if "norm" in orig_key:
lowerCamelCase : Optional[Any] = orig_key.replace("norm" , "LayerNorm" )
if "transformer" in orig_key:
lowerCamelCase : int = orig_key.split("." )[0].split("_" )[-1]
lowerCamelCase : Dict = orig_key.replace(f"""transformer_{layer_num}""" , f"""encoder.layer.{layer_num}""" )
if "mha.attn" in orig_key:
lowerCamelCase : List[str] = orig_key.replace("mha.attn" , "attention.self" )
if "mha" in orig_key:
lowerCamelCase : List[Any] = orig_key.replace("mha" , "attention" )
if "W_q" in orig_key:
lowerCamelCase : Optional[int] = orig_key.replace("W_q" , "self.query" )
if "W_k" in orig_key:
lowerCamelCase : List[Any] = orig_key.replace("W_k" , "self.key" )
if "W_v" in orig_key:
lowerCamelCase : Union[str, Any] = orig_key.replace("W_v" , "self.value" )
if "ff1" in orig_key:
lowerCamelCase : Union[str, Any] = orig_key.replace("ff1" , "intermediate.dense" )
if "ff2" in orig_key:
lowerCamelCase : Optional[int] = orig_key.replace("ff2" , "output.dense" )
if "ff" in orig_key:
lowerCamelCase : Optional[int] = orig_key.replace("ff" , "output.dense" )
if "mlm_class" in orig_key:
lowerCamelCase : Dict = orig_key.replace("mlm.mlm_class" , "cls.predictions.decoder" )
if "mlm" in orig_key:
lowerCamelCase : List[Any] = orig_key.replace("mlm" , "cls.predictions.transform" )
if "cls" not in orig_key:
lowerCamelCase : int = "yoso." + orig_key
return orig_key
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
lowerCamelCase : List[str] = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
lowerCamelCase : Dict = val
lowerCamelCase : Dict = orig_state_dict["cls.predictions.decoder.bias"]
lowerCamelCase : Dict = torch.arange(SCREAMING_SNAKE_CASE_ ).expand((1, -1) ) + 2
return orig_state_dict
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : List[Any] = torch.load(SCREAMING_SNAKE_CASE_ , map_location="cpu" )["model_state_dict"]
lowerCamelCase : List[str] = YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Any = YosoForMaskedLM(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[Any] = convert_checkpoint_helper(config.max_position_embeddings , SCREAMING_SNAKE_CASE_ )
print(model.load_state_dict(SCREAMING_SNAKE_CASE_ ) )
model.eval()
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The json file for YOSO model config.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_snake_case = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 283
| 0
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = [
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
__UpperCamelCase = [
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def _a ( _lowerCamelCase ) -> Optional[Any]:
"""simple docstring"""
__snake_case : Optional[int] = torch.load(_lowerCamelCase , map_location="""cpu""" )
return sd
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=rename_keys_prefix ) -> str:
"""simple docstring"""
__snake_case : Optional[Any] = OrderedDict()
__snake_case : Optional[Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
__snake_case : Optional[int] = key
for name_pair in rename_keys_prefix:
__snake_case : str = new_key.replace(name_pair[0] , name_pair[1] )
__snake_case : List[str] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
__snake_case : List[Any] = new_d["""cls.predictions.bias"""]
return new_d
@torch.no_grad()
def _a ( _lowerCamelCase , _lowerCamelCase ) -> str:
"""simple docstring"""
assert (
checkpoint_path.split("""/""" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''
# Get Config
if "pre" in checkpoint_path:
__snake_case : Tuple = """pretraining"""
if "vcr" in checkpoint_path:
__snake_case : Tuple = {"""visual_embedding_dim""": 512}
elif "vqa_advanced" in checkpoint_path:
__snake_case : int = {"""visual_embedding_dim""": 2048}
elif "vqa" in checkpoint_path:
__snake_case : Optional[int] = {"""visual_embedding_dim""": 2048}
elif "nlvr" in checkpoint_path:
__snake_case : str = {"""visual_embedding_dim""": 1024}
else:
raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.''' )
else:
if "vcr" in checkpoint_path:
__snake_case : str = {"""visual_embedding_dim""": 512}
__snake_case : Dict = """multichoice"""
elif "vqa_advanced" in checkpoint_path:
__snake_case : int = {"""visual_embedding_dim""": 2048}
__snake_case : Dict = """vqa_advanced"""
elif "vqa" in checkpoint_path:
__snake_case : Dict = {"""visual_embedding_dim""": 2048, """num_labels""": 3129}
__snake_case : List[Any] = """vqa"""
elif "nlvr" in checkpoint_path:
__snake_case : List[Any] = {
"""visual_embedding_dim""": 1024,
"""num_labels""": 2,
}
__snake_case : int = """nlvr"""
__snake_case : Optional[Any] = VisualBertConfig(**_lowerCamelCase )
# Load State Dict
__snake_case : List[Any] = load_state_dict(_lowerCamelCase )
__snake_case : Tuple = get_new_dict(_lowerCamelCase , _lowerCamelCase )
if model_type == "pretraining":
__snake_case : Tuple = VisualBertForPreTraining(_lowerCamelCase )
elif model_type == "vqa":
__snake_case : List[str] = VisualBertForQuestionAnswering(_lowerCamelCase )
elif model_type == "nlvr":
__snake_case : Tuple = VisualBertForVisualReasoning(_lowerCamelCase )
elif model_type == "multichoice":
__snake_case : Optional[Any] = VisualBertForMultipleChoice(_lowerCamelCase )
model.load_state_dict(_lowerCamelCase )
# Save Checkpoints
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
model.save_pretrained(_lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
__UpperCamelCase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 13
|
'''simple docstring'''
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ) -> None:
"""simple docstring"""
__snake_case : int = len(_lowerCamelCase )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(_lowerCamelCase ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , _lowerCamelCase , _lowerCamelCase , )
def _a ( _lowerCamelCase ) -> None:
"""simple docstring"""
__snake_case : list[list[str]] = []
depth_first_search([] , [] , [] , _lowerCamelCase , _lowerCamelCase )
# Print all the boards
for board in boards:
for column in board:
print(_lowerCamelCase )
print("""""" )
print(len(_lowerCamelCase ) , """solutions were found.""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 13
| 1
|
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
__UpperCamelCase : Optional[int] = '''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
__UpperCamelCase : Tuple = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
__UpperCamelCase : Dict = dict(zip(vocab, range(len(vocab))))
__UpperCamelCase : int = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
with tempfile.TemporaryDirectory() as tmpdirname:
__UpperCamelCase : Optional[Any] = Path(tmpdirname)
__UpperCamelCase : Optional[int] = build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
__UpperCamelCase : List[Any] = build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
__UpperCamelCase : Any = build_dir / VOCAB_FILES_NAMES['''merges_file''']
with open(src_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, '''w''') as fp:
fp.write('''\n'''.join(merges))
__UpperCamelCase : List[Any] = FSMTTokenizer(
langs=['''en''', '''ru'''],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
__UpperCamelCase : Union[str, Any] = FSMTConfig(
langs=['''ru''', '''en'''],
src_vocab_size=1_0_0_0,
tgt_vocab_size=1_0_0_0,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
__UpperCamelCase : Optional[int] = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
__UpperCamelCase : int = tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__UpperCamelCase : str = tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 106
|
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
snake_case__ : Dict = logging.getLogger(__name__)
def _snake_case ( _snake_case : Any , _snake_case : Any ):
return (preds == labels).mean()
@dataclass
class snake_case_:
__UpperCamelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__UpperCamelCase = field(
default=a__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__UpperCamelCase = field(
default=a__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__UpperCamelCase = field(
default=a__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class snake_case_:
__UpperCamelCase = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys() )} )
__UpperCamelCase = field(metadata={'''help''': '''Should contain the data files for the task.'''} )
__UpperCamelCase = field(
default=128 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__UpperCamelCase = field(
default=a__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def _snake_case ( ):
# 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.
lowerCAmelCase : str = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase : Optional[int] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , _snake_case )
# Set seed
set_seed(training_args.seed )
try:
lowerCAmelCase : Tuple = processors[data_args.task_name]()
lowerCAmelCase : Any = processor.get_labels()
lowerCAmelCase : Union[str, Any] = len(_snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
lowerCAmelCase : List[str] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
lowerCAmelCase : Dict = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowerCAmelCase : Any = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_snake_case : EvalPrediction ) -> Dict:
lowerCAmelCase : int = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(_snake_case , p.label_ids )}
# Data collator
lowerCAmelCase : List[Any] = DataCollatorWithPadding(_snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowerCAmelCase : Union[str, Any] = Trainer(
model=_snake_case , args=_snake_case , train_dataset=_snake_case , eval_dataset=_snake_case , compute_metrics=_snake_case , data_collator=_snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowerCAmelCase : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowerCAmelCase : Any = trainer.evaluate()
lowerCAmelCase : int = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(_snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , _snake_case , _snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(_snake_case )
return results
def _snake_case ( _snake_case : List[str] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 60
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : str ={
'''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _lowercase ( _lowercase ):
a = """levit"""
def __init__( self: str , UpperCamelCase__: str=224 , UpperCamelCase__: str=3 , UpperCamelCase__: Union[str, Any]=3 , UpperCamelCase__: Tuple=2 , UpperCamelCase__: str=1 , UpperCamelCase__: List[Any]=16 , UpperCamelCase__: Any=[128, 256, 384] , UpperCamelCase__: Any=[4, 8, 12] , UpperCamelCase__: List[Any]=[4, 4, 4] , UpperCamelCase__: List[str]=[16, 16, 16] , UpperCamelCase__: Dict=0 , UpperCamelCase__: Dict=[2, 2, 2] , UpperCamelCase__: Tuple=[2, 2, 2] , UpperCamelCase__: int=0.02 , **UpperCamelCase__: Tuple , ):
super().__init__(**UpperCamelCase__ )
lowerCamelCase__ : Optional[int] = image_size
lowerCamelCase__ : Tuple = num_channels
lowerCamelCase__ : Dict = kernel_size
lowerCamelCase__ : Union[str, Any] = stride
lowerCamelCase__ : int = padding
lowerCamelCase__ : Dict = hidden_sizes
lowerCamelCase__ : int = num_attention_heads
lowerCamelCase__ : Any = depths
lowerCamelCase__ : List[Any] = key_dim
lowerCamelCase__ : List[str] = drop_path_rate
lowerCamelCase__ : Optional[Any] = patch_size
lowerCamelCase__ : Tuple = attention_ratio
lowerCamelCase__ : Dict = mlp_ratio
lowerCamelCase__ : Tuple = initializer_range
lowerCamelCase__ : Union[str, Any] = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _lowercase ( _lowercase ):
a = version.parse("""1.11""" )
@property
def lowerCamelCase_ ( self: Any ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCamelCase_ ( self: Tuple ):
return 1e-4
| 361
|
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_A : str ={
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 1_000,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
_A : Union[str, Any] ={
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 1_000,
'''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''',
}
_A : Dict ={
'''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''',
}
_A : Dict ={
'''num_train_timesteps''': 40,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
_A : str ={
'''num_train_timesteps''': 201,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
_A : int ={
'''num_train_timesteps''': 151,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Dict:
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 SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=False ) -> Any:
lowerCamelCase__ : Any = checkpoint[f'''{old_prefix}.in_layers.0.weight''']
lowerCamelCase__ : int = checkpoint[f'''{old_prefix}.in_layers.0.bias''']
lowerCamelCase__ : Any = checkpoint[f'''{old_prefix}.in_layers.2.weight''']
lowerCamelCase__ : Any = checkpoint[f'''{old_prefix}.in_layers.2.bias''']
lowerCamelCase__ : Optional[Any] = checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
lowerCamelCase__ : Optional[int] = checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
lowerCamelCase__ : Dict = checkpoint[f'''{old_prefix}.out_layers.0.weight''']
lowerCamelCase__ : Tuple = checkpoint[f'''{old_prefix}.out_layers.0.bias''']
lowerCamelCase__ : str = checkpoint[f'''{old_prefix}.out_layers.3.weight''']
lowerCamelCase__ : int = checkpoint[f'''{old_prefix}.out_layers.3.bias''']
if has_skip:
lowerCamelCase__ : Tuple = checkpoint[f'''{old_prefix}.skip_connection.weight''']
lowerCamelCase__ : List[Any] = checkpoint[f'''{old_prefix}.skip_connection.bias''']
return new_checkpoint
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=None ) -> str:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3 , dim=0 )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3 , dim=0 )
lowerCamelCase__ : Any = checkpoint[f'''{old_prefix}.norm.weight''']
lowerCamelCase__ : Optional[int] = checkpoint[f'''{old_prefix}.norm.bias''']
lowerCamelCase__ : List[Any] = weight_q.squeeze(-1 ).squeeze(-1 )
lowerCamelCase__ : List[Any] = bias_q.squeeze(-1 ).squeeze(-1 )
lowerCamelCase__ : Any = weight_k.squeeze(-1 ).squeeze(-1 )
lowerCamelCase__ : Optional[Any] = bias_k.squeeze(-1 ).squeeze(-1 )
lowerCamelCase__ : Dict = weight_v.squeeze(-1 ).squeeze(-1 )
lowerCamelCase__ : Union[str, Any] = bias_v.squeeze(-1 ).squeeze(-1 )
lowerCamelCase__ : Optional[Any] = (
checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
)
lowerCamelCase__ : Dict = checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> List[Any]:
lowerCamelCase__ : str = torch.load(UpperCamelCase , map_location="""cpu""" )
lowerCamelCase__ : Optional[int] = {}
lowerCamelCase__ : Optional[int] = checkpoint["""time_embed.0.weight"""]
lowerCamelCase__ : List[Any] = checkpoint["""time_embed.0.bias"""]
lowerCamelCase__ : int = checkpoint["""time_embed.2.weight"""]
lowerCamelCase__ : Optional[Any] = checkpoint["""time_embed.2.bias"""]
if unet_config["num_class_embeds"] is not None:
lowerCamelCase__ : Optional[Any] = checkpoint["""label_emb.weight"""]
lowerCamelCase__ : Tuple = checkpoint["""input_blocks.0.0.weight"""]
lowerCamelCase__ : List[str] = checkpoint["""input_blocks.0.0.bias"""]
lowerCamelCase__ : Optional[Any] = unet_config["""down_block_types"""]
lowerCamelCase__ : Any = unet_config["""layers_per_block"""]
lowerCamelCase__ : Any = unet_config["""attention_head_dim"""]
lowerCamelCase__ : List[Any] = unet_config["""block_out_channels"""]
lowerCamelCase__ : str = 1
lowerCamelCase__ : str = channels_list[0]
for i, layer_type in enumerate(UpperCamelCase ):
lowerCamelCase__ : List[Any] = channels_list[i]
lowerCamelCase__ : List[Any] = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(UpperCamelCase ):
lowerCamelCase__ : int = f'''down_blocks.{i}.resnets.{j}'''
lowerCamelCase__ : Dict = f'''input_blocks.{current_layer}.0'''
lowerCamelCase__ : Tuple = True if j == 0 and downsample_block_has_skip else False
lowerCamelCase__ : List[Any] = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , has_skip=UpperCamelCase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(UpperCamelCase ):
lowerCamelCase__ : Tuple = f'''down_blocks.{i}.resnets.{j}'''
lowerCamelCase__ : Optional[Any] = f'''input_blocks.{current_layer}.0'''
lowerCamelCase__ : str = True if j == 0 and downsample_block_has_skip else False
lowerCamelCase__ : Union[str, Any] = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , has_skip=UpperCamelCase )
lowerCamelCase__ : Any = f'''down_blocks.{i}.attentions.{j}'''
lowerCamelCase__ : Dict = f'''input_blocks.{current_layer}.1'''
lowerCamelCase__ : Tuple = convert_attention(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
current_layer += 1
if i != len(UpperCamelCase ) - 1:
lowerCamelCase__ : Tuple = f'''down_blocks.{i}.downsamplers.0'''
lowerCamelCase__ : str = f'''input_blocks.{current_layer}.0'''
lowerCamelCase__ : Union[str, Any] = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
current_layer += 1
lowerCamelCase__ : Union[str, Any] = current_channels
# hardcoded the mid-block for now
lowerCamelCase__ : Any = """mid_block.resnets.0"""
lowerCamelCase__ : Optional[Any] = """middle_block.0"""
lowerCamelCase__ : int = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : List[Any] = """mid_block.attentions.0"""
lowerCamelCase__ : Dict = """middle_block.1"""
lowerCamelCase__ : int = convert_attention(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : Any = """mid_block.resnets.1"""
lowerCamelCase__ : Tuple = """middle_block.2"""
lowerCamelCase__ : int = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : Union[str, Any] = 0
lowerCamelCase__ : Any = unet_config["""up_block_types"""]
for i, layer_type in enumerate(UpperCamelCase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
lowerCamelCase__ : int = f'''up_blocks.{i}.resnets.{j}'''
lowerCamelCase__ : Optional[Any] = f'''output_blocks.{current_layer}.0'''
lowerCamelCase__ : Any = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , has_skip=UpperCamelCase )
current_layer += 1
if i != len(UpperCamelCase ) - 1:
lowerCamelCase__ : Dict = f'''up_blocks.{i}.upsamplers.0'''
lowerCamelCase__ : List[str] = f'''output_blocks.{current_layer-1}.1'''
lowerCamelCase__ : Optional[Any] = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
lowerCamelCase__ : str = f'''up_blocks.{i}.resnets.{j}'''
lowerCamelCase__ : List[Any] = f'''output_blocks.{current_layer}.0'''
lowerCamelCase__ : Optional[Any] = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , has_skip=UpperCamelCase )
lowerCamelCase__ : Optional[Any] = f'''up_blocks.{i}.attentions.{j}'''
lowerCamelCase__ : Any = f'''output_blocks.{current_layer}.1'''
lowerCamelCase__ : Optional[int] = convert_attention(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
current_layer += 1
if i != len(UpperCamelCase ) - 1:
lowerCamelCase__ : Tuple = f'''up_blocks.{i}.upsamplers.0'''
lowerCamelCase__ : Tuple = f'''output_blocks.{current_layer-1}.2'''
lowerCamelCase__ : List[str] = convert_resnet(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
lowerCamelCase__ : Dict = checkpoint["""out.0.weight"""]
lowerCamelCase__ : Dict = checkpoint["""out.0.bias"""]
lowerCamelCase__ : Dict = checkpoint["""out.2.weight"""]
lowerCamelCase__ : Tuple = checkpoint["""out.2.bias"""]
return new_checkpoint
if __name__ == "__main__":
_A : Tuple =argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
_A : Tuple =parser.parse_args()
_A : Optional[int] =strabool(args.class_cond)
_A : List[str] =os.path.basename(args.unet_path)
print(F'Checkpoint: {ckpt_name}')
# Get U-Net config
if "imagenet64" in ckpt_name:
_A : int =IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
_A : Tuple =LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
_A : Any =TEST_UNET_CONFIG
else:
raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')
if not args.class_cond:
_A : str =None
_A : Optional[int] =con_pt_to_diffuser(args.unet_path, unet_config)
_A : Optional[int] =UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
_A : Tuple =CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
_A : 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)):
_A : Union[str, Any] =CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')
_A : str =CMStochasticIterativeScheduler(**scheduler_config)
_A : Optional[Any] =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 129
| 0
|
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : List[str] ) -> Optional[int]:
lowerCamelCase_ = len(_lowerCamelCase )
for i in range(length - 1 ):
lowerCamelCase_ = i
for k in range(i + 1 , _lowerCamelCase ):
if collection[k] < collection[least]:
lowerCamelCase_ = k
if least != i:
lowerCamelCase_ , lowerCamelCase_ = (collection[i], collection[least])
return collection
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : List[Any] = input('''Enter numbers separated by a comma:\n''').strip()
_SCREAMING_SNAKE_CASE : Optional[Any] = [int(item) for item in user_input.split(''',''')]
print(selection_sort(unsorted))
| 183
|
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCamelCase__ ( _lowerCamelCase : Tuple ) -> Dict:
# getting number of pixels in the image
lowerCamelCase_ , lowerCamelCase_ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
lowerCamelCase_ = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
_SCREAMING_SNAKE_CASE : List[Any] = imread('''image_data/lena.jpg''', 1)
# convert to its negative
_SCREAMING_SNAKE_CASE : List[Any] = convert_to_negative(img)
# show result image
imshow('''negative of original image''', img)
waitKey(0)
destroyAllWindows()
| 183
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
'''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
'''kssteven/ibert-roberta-large-mnli''': (
'''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
),
}
class a ( __lowerCamelCase ):
__lowerCAmelCase : Optional[Any] = """ibert"""
def __init__( self :Tuple ,__lowercase :Dict=3_0_5_2_2 ,__lowercase :Dict=7_6_8 ,__lowercase :Any=1_2 ,__lowercase :Dict=1_2 ,__lowercase :Dict=3_0_7_2 ,__lowercase :Dict="gelu" ,__lowercase :Optional[Any]=0.1 ,__lowercase :Tuple=0.1 ,__lowercase :Union[str, Any]=5_1_2 ,__lowercase :Optional[int]=2 ,__lowercase :Dict=0.02 ,__lowercase :Tuple=1e-1_2 ,__lowercase :str=1 ,__lowercase :int=0 ,__lowercase :Optional[int]=2 ,__lowercase :Union[str, Any]="absolute" ,__lowercase :Tuple=False ,__lowercase :int="none" ,**__lowercase :List[Any] ,):
super().__init__(pad_token_id=__lowercase ,bos_token_id=__lowercase ,eos_token_id=__lowercase ,**__lowercase )
snake_case__ : str = vocab_size
snake_case__ : int = hidden_size
snake_case__ : Any = num_hidden_layers
snake_case__ : Tuple = num_attention_heads
snake_case__ : Any = hidden_act
snake_case__ : Dict = intermediate_size
snake_case__ : str = hidden_dropout_prob
snake_case__ : Tuple = attention_probs_dropout_prob
snake_case__ : Optional[Any] = max_position_embeddings
snake_case__ : Tuple = type_vocab_size
snake_case__ : Dict = initializer_range
snake_case__ : List[Any] = layer_norm_eps
snake_case__ : int = position_embedding_type
snake_case__ : List[Any] = quant_mode
snake_case__ : Union[str, Any] = force_dequant
class a ( __lowerCamelCase ):
@property
def __lowerCamelCase ( self :Dict ):
if self.task == "multiple-choice":
snake_case__ : Dict = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case__ : Optional[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 361
|
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
snake_case__ , snake_case__ : Optional[int] = unsorted[j - 1], unsorted[j]
snake_case__ : Any = True
for j in range(__lowerCAmelCase ):
if unsorted[j] > unsorted[j + 1]:
snake_case__ , snake_case__ : Tuple = unsorted[j + 1], unsorted[j]
snake_case__ : int = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
A__ = input('''Enter numbers separated by a comma:\n''').strip()
A__ = [int(item) for item in user_input.split(''',''')]
print(f"""{cocktail_shaker_sort(unsorted) = }""")
| 44
| 0
|
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